#how to use prompt engineering to improve AI results

[bhawaybhalla]

Prompt Engineering: The #1 Skill That Will Unlock Your AI Potential in 2024

Imagine having a superpower that allows you to converse with the most advanced AI systems on the planet, shaping their output to perfectly suit your needs. Welcome to the dynamic world of prompt engineering – the art and science of crafting the instructions that guide AI models to generate amazing text, translate languages, write different kinds of creative compositions, and much more. In this…

[canmom]

using LLMs to control a game character's dialogue seems an obvious use for the technology. and indeed people have tried, for example nVidia made a demo where the player interacts with AI-voiced NPCs:

this looks bad, right? like idk about you but I am not raring to play a game with LLM bots instead of human-scripted characters. they don't seem to have anything interesting to say that a normal NPC wouldn't, and the acting is super wooden.

so, the attempts to do this so far that I've seen have some pretty obvious faults:

relying on external API calls to process the data (expensive!)

presumably relying on generic 'you are xyz' prompt engineering to try to get a model to respond 'in character', resulting in bland, flavourless output

limited connection between game state and model state (you would need to translate the relevant game state into a text prompt)

responding to freeform input, models may not be very good at staying 'in character', with the default 'chatbot' persona emerging unexpectedly. or they might just make uncreative choices in general.

AI voice generation, while it's moved very fast in the last couple years, is still very poor at 'acting', producing very flat, emotionless performances, or uncanny mismatches of tone, inflection, etc.

although the model may generate contextually appropriate dialogue, it is difficult to link that back to the behaviour of characters in game

so how could we do better?

the first one could be solved by running LLMs locally on the user's hardware. that has some obvious drawbacks: running on the user's GPU means the LLM is competing with the game's graphics, meaning both must be more limited. ideally you would spread the LLM processing over multiple frames, but you still are limited by available VRAM, which is contested by the game's texture data and so on, and LLMs are very thirsty for VRAM. still, imo this is way more promising than having to talk to the internet and pay for compute time to get your NPC's dialogue lmao

second one might be improved by using a tool like control vectors to more granularly and consistently shape the tone of the output. I heard about this technique today (thanks @cherrvak)

third one is an interesting challenge - but perhaps a control-vector approach could also be relevant here? if you could figure out how a description of some relevant piece of game state affects the processing of the model, you could then apply that as a control vector when generating output. so the bridge between the game state and the LLM would be a set of weights for control vectors that are applied during generation.

this one is probably something where finetuning the model, and using control vectors to maintain a consistent 'pressure' to act a certain way even as the context window gets longer, could help a lot.

probably the vocal performance problem will improve in the next generation of voice generators, I'm certainly not solving it. a purely text-based game would avoid the problem entirely of course.

this one is tricky. perhaps the model could be taught to generate a description of a plan or intention, but linking that back to commands to perform by traditional agentic game 'AI' is not trivial. ideally, if there are various high-level commands that a game character might want to perform (like 'navigate to a specific location' or 'target an enemy') that are usually selected using some other kind of algorithm like weighted utilities, you could train the model to generate tokens that correspond to those actions and then feed them back in to the 'bot' side? I'm sure people have tried this kind of thing in robotics. you could just have the LLM stuff go 'one way', and rely on traditional game AI for everything besides dialogue, but it would be interesting to complete that feedback loop.

I doubt I'll be using this anytime soon (models are just too demanding to run on anything but a high-end PC, which is too niche, and I'll need to spend time playing with these models to determine if these ideas are even feasible), but maybe something to come back to in the future. first step is to figure out how to drive the control-vector thing locally.

[mariacallous]

On a 5K screen in Kirkland, Washington, four terminals blur with activity as artificial intelligence generates thousands of lines of code. Steve Yegge, a veteran software engineer who previously worked at Google and AWS, sits back to watch.

“This one is running some tests, that one is coming up with a plan. I am now coding on four different projects at once, although really I’m just burning tokens,” Yegge says, referring to the cost of generating chunks of text with a large language model (LLM).

Learning to code has long been seen as the ticket to a lucrative, secure career in tech. Now, the release of advanced coding models from firms like OpenAI, Anthropic, and Google threatens to upend that notion entirely. X and Bluesky are brimming with talk of companies downsizing their developer teams—or even eliminating them altogether.

When ChatGPT debuted in late 2022, AI models were capable of autocompleting small portions of code—a helpful, if modest step forward that served to speed up software development. As models advanced and gained “agentic” skills that allow them to use software programs, manipulate files, and access online services, engineers and non-engineers alike started using the tools to build entire apps and websites. Andrej Karpathy, a prominent AI researcher, coined the term “vibe coding” in February, to describe the process of developing software by prompting an AI model with text.

The rapid progress has led to speculation—and even panic—among developers, who fear that most development work could soon be automated away, in what would amount to a job apocalypse for engineers.

“We are not far from a world—I think we’ll be there in three to six months—where AI is writing 90 percent of the code,” Dario Amodei, CEO of Anthropic, said at a Council on Foreign Relations event in March. “And then in 12 months, we may be in a world where AI is writing essentially all of the code,” he added.

But many experts warn that even the best models have a way to go before they can reliably automate a lot of coding work. While future advancements might unleash AI that can code just as well as a human, until then relying too much on AI could result in a glut of buggy and hackable code, as well as a shortage of developers with the knowledge and skills needed to write good software.

David Autor, an economist at MIT who studies how AI affects employment, says it’s possible that software development work will be automated—similar to how transcription and translation jobs are quickly being replaced by AI. He notes, however, that advanced software engineering is much more complex and will be harder to automate than routine coding.

Autor adds that the picture may be complicated by the “elasticity” of demand for software engineering—the extent to which the market might accommodate additional engineering jobs.

“If demand for software were like demand for colonoscopies, no improvement in speed or reduction in costs would create a mad rush for the proctologist's office,” Autor says. “But if demand for software is like demand for taxi services, then we may see an Uber effect on coding: more people writing more code at lower prices, and lower wages.”

Yegge’s experience shows that perspectives are evolving. A prolific blogger as well as coder, Yegge was previously doubtful that AI would help produce much code. Today, he has been vibe-pilled, writing a book called Vibe Coding with another experienced developer, Gene Kim, that lays out the potential and the pitfalls of the approach. Yegge became convinced that AI would revolutionize software development last December, and he has led a push to develop AI coding tools at his company, Sourcegraph.

“This is how all programming will be conducted by the end of this year,” Yegge predicts. “And if you're not doing it, you're just walking in a race.”

The Vibe-Coding Divide

Today, coding message boards are full of examples of mobile apps, commercial websites, and even multiplayer games all apparently vibe-coded into being. Experienced coders, like Yegge, can give AI tools instructions and then watch AI bring complex ideas to life.

Several AI-coding startups, including Cursor and Windsurf have ridden a wave of interest in the approach. (OpenAI is widely rumored to be in talks to acquire Windsurf).

At the same time, the obvious limitations of generative AI, including the way models confabulate and become confused, has led many seasoned programmers to see AI-assisted coding—and especially gung-ho, no-hands vibe coding—as a potentially dangerous new fad.

Martin Casado, a computer scientist and general partner at Andreessen Horowitz who sits on the board of Cursor, says the idea that AI will replace human coders is overstated. “AI is great at doing dazzling things, but not good at doing specific things,” he said.

Still, Casado has been stunned by the pace of recent progress. “I had no idea it would get this good this quick,” he says. “This is the most dramatic shift in the art of computer science since assembly was supplanted by higher-level languages.”

Ken Thompson, vice president of engineering at Anaconda, a company that provides open source code for software development, says AI adoption tends to follow a generational divide, with younger developers diving in and older ones showing more caution. For all the hype, he says many developers still do not trust AI tools because their output is unpredictable, and will vary from one day to the next, even when given the same prompt. “The nondeterministic nature of AI is too risky, too dangerous,” he explains.

Both Casado and Thompson see the vibe-coding shift as less about replacement than abstraction, mimicking the way that new languages like Python build on top of lower-level languages like C, making it easier and faster to write code. New languages have typically broadened the appeal of programming and increased the number of practitioners. AI could similarly increase the number of people capable of producing working code.

Bad Vibes

Paradoxically, the vibe-coding boom suggests that a solid grasp of coding remains as important as ever. Those dabbling in the field often report running into problems, including introducing unforeseen security issues, creating features that only simulate real functionality, accidentally running up high bills using AI tools, and ending up with broken code and no idea how to fix it.

“AI [tools] will do everything for you—including fuck up,” Yegge says. “You need to watch them carefully, like toddlers.”

The fact that AI can produce results that range from remarkably impressive to shockingly problematic may explain why developers seem so divided about the technology. WIRED surveyed programmers in March to ask how they felt about AI coding, and found that the proportion who were enthusiastic about AI tools (36 percent) was mirrored by the portion who felt skeptical (38 percent).

“Undoubtedly AI will change the way code is produced,” says Daniel Jackson, a computer scientist at MIT who is currently exploring how to integrate AI into large-scale software development. “But it wouldn't surprise me if we were in for disappointment—that the hype will pass.”

Jackson cautions that AI models are fundamentally different from the compilers that turn code written in a high-level language into a lower-level language that is more efficient for machines to use, because they don’t always follow instructions. Sometimes an AI model may take an instruction and execute better than the developer—other times it might do the task much worse.

Jackson adds that vibe coding falls down when anyone is building serious software. “There are almost no applications in which ‘mostly works’ is good enough,” he says. “As soon as you care about a piece of software, you care that it works right.”

Many software projects are complex, and changes to one section of code can cause problems elsewhere in the system. Experienced programmers are good at understanding the bigger picture, Jackson says, but “large language models can't reason their way around those kinds of dependencies.”

Jackson believes that software development might evolve with more modular codebases and fewer dependencies to accommodate AI blind spots. He expects that AI may replace some developers but will also force many more to rethink their approach and focus more on project design.

Too much reliance on AI may be “a bit of an impending disaster,” Jackson adds, because “not only will we have masses of broken code, full of security vulnerabilities, but we'll have a new generation of programmers incapable of dealing with those vulnerabilities.”

Learn to Code

Even firms that have already integrated coding tools into their software development process say the technology remains far too unreliable for wider use.

Christine Yen, CEO at Honeycomb, a company that provides technology for monitoring the performance of large software systems, says that projects that are simple or formulaic, like building component libraries, are more amenable to using AI. Even so, she says the developers at her company who use AI in their work have only increased their productivity by about 50 percent.

Yen adds that for anything requiring good judgement, where performance is important, or where the resulting code touches sensitive systems or data, “AI just frankly isn't good enough yet to be additive.”

“The hard part about building software systems isn't just writing a lot of code,” she says. “Engineers are still going to be necessary, at least today, for owning that curation, judgment, guidance and direction.”

Others suggest that a shift in the workforce is coming. “We are not seeing less demand for developers,” says Liad Elidan, CEO of Milestone, a company that helps firms measure the impact of generative AI projects. “We are seeing less demand for average or low-performing developers.”

“If I'm building a product, I could have needed 50 engineers and now maybe I only need 20 or 30,” says Naveen Rao, VP of AI at Databricks, a company that helps large businesses build their own AI systems. “That is absolutely real.”

Rao says, however, that learning to code should remain a valuable skill for some time. “It’s like saying ‘Don't teach your kid to learn math,’” he says. Understanding how to get the most out of computers is likely to remain extremely valuable, he adds.

Yegge and Kim, the veteran coders, believe that most developers can adapt to the coming wave. In their book on vibe coding, the pair recommend new strategies for software development including modular code bases, constant testing, and plenty of experimentation. Yegge says that using AI to write software is evolving into its own—slightly risky—art form. “It’s about how to do this without destroying your hard disk and draining your bank account,” he says.

[athomewithladylux]

How I use AI as an admin assistant to improve my job performance:

First of all, stop being scared of AI. It's like being scared of cars. They're here to stay, there are some dangers, but it's super useful so you should figure out how to make them work for you. Second, make sure you're not sharing personal or company secrets. AI is great but if you're not paying the providing company for the tool with cash then you are paying with your data. If you're not sure if the AI service your company uses is secure, ask IT. If your company isn't using AI ask them why, what the policy on AI use is, and stick to that policy.

Now, here's how I use AI to improve my work performance:

Make a Personal Assistant: I use enterprise ChatGPT's custom GPT feature to make all kinds of things. An email writing chat (where I can put in details and get it to write the email and match my tone and style), a reference library for a major project (so I always have the information and source at my fingertips in a meeting), one for the company's brand voice and style so anything I send to marketing is easy for them to work with, and gets picked up faster. I treat these GPTs like an intern who tries really hard but may not always get things right. I always review and get the GPT to site its sources so I can confirm things. It saves hours of repetitive work every week.

Analyze complex data: I deal with multiple multi-page documents and Word's "compare" feature is frankly terrible. I can drop two similar documents into my AI and get it to tell me what's different and where the differences are. Again, a huge timesaver.

Prepare for meetings and career progression support: Before any meeting I upload any materials from the organizers and anything relevant from my unit, and then get it to tell me, given the audience, what sort of questions might be asked in the meeting and what are the answers. I also ask it to align my questions and planned actions to the strategic plan. 

Plan my career development: I told my AI where I wanted to go in the next five years and got it to analyze my resume and current role. I asked it to show me where I needed skills, and provide examples of where I could get those skills. Then I asked it to cost out the classes and give me a timeline. Now I'm studying for a certificate I didn't know about before to get to an accreditation I really want.

How to do it all (prompt engineering):

Do the groundwork by giving your AI context, details, information, and very specific requests. I loaded a bunch of emails into my email-writing GPT and also told it my career ambitions. It's tweaked my tone just a little. I sound like me, but a bit more professional. Likewise if you're making a reference library. It can't tell you what it doesn't know, but it will try, so be sure to tell it not to infer based on data, but to tell you when it doesn't have information.

Security risks to consider:

Secure access: You absolutely must protect sensitive information and follow whatever AI policy is in place where you work. If there isn't one, spearhead the team working on it. It's a perfect leadership opportunity.

Data protection: Be very careful when sharing sensitive data with AI systems, and know your security. Also check your results! Again, think of AI as an eager but kind of hapless intern and double check their work.

Recognize AI threats: Stay aware of potential AI-driven cyberattacks, such as deepfake videos or social engineering attempts. There have been some huge ones lately!

By getting a handle on AI and being aware of the risks you can improve your work quality, offload the boring stuff, and advance your career. So get started. But be careful.

[onecooooooolcat]

putting aside the faults and flaws with the function atm "ideally" ai chatbots are supposed to mimic or regurgitate the results of search engines but in a like more customized manner like if you search your search engine of choice for smth like how to write an email rsvp-ing no you will find tons of websites with details of how to do it and templates you can just copy and paste and fill in the specifics. the ai chatbot is supposed to more of less the same thing? but possibly omit the need to write in the specifics yourself if you provide it with all that in the prompt and it follows the instructions. like in a professional setting there are lots of standard phrases you use in emails it's not necessarily smth where you're trying to invent the wheel 2.0 yet it is also smth some ppl know how to do and some ppl dont and there is a skill to it and you can learn and improve but also it's just an email you can find and copy many examples online you can even copy those you receive it's an electronic mail

[mollytranscribes]

[Profile picture transcription: An eye shape with a rainbow flag covering the whites. The iris in the middle is red, with a white d20 for a pupil. End transcription.]

Hello! This is a blog specifically dedicated to image transcriptions. My main blog is @mollymaclachlan.

For those who don't know, I used to be part of r/TranscribersOfReddit, a Reddit community dedicated to transcribing posts to improve accessibility. That project sadly had to shut down, partially as a result of the whole fiasco with Reddit's API changes. But I miss transcribing and I often see posts on Tumblr with no alt text and no transcription.

So! Here I am, making a new blog. I'll be transcribing posts that need it when I see them and I have time; likely mainly ones I see on my dashboard. I also have asks open so anyone can request posts or images.

I have plenty of experience transcribing but that doesn't mean I'm perfect. We can always learn to be better and I'm not visually impaired myself, so if you have any feedback on how I can improve my transcriptions please don't hesitate to tell me. Just be friendly about it.

The rest of this post is an FAQ, adapted from one I posted on Reddit.

1. Why do you do transcriptions?

Transcriptions help improve the accessibility of posts. Tumblr has capabilities for adding alt-text to images, but not everyone uses it, and it has a character limit that can hamper descriptions for complex images. The following is a non-exhaustive list of the ways transcriptions improve accessibility:

They help visually-impaired people. Most visually-impaired people rely on screen readers, technology that reads out what's on the screen, but this technology can't read out images.

They help people who have trouble reading any small, blurry or oddly formatted text.

In some cases they're helpful for people with colour deficiencies, particularly if there is low contrast.

They help people with bad internet connections, who might as a result not be able to load images at high quality or at all.

They can provide context or note small details many people may otherwise miss when first viewing a post.

They are useful for search engine indexing and the preservation of images.

They can provide data for improving OCR (Optical Character Recognition) technology.

2. Why don't you just use OCR or AI?

OCR (Optical Character Recoginition) is technology that detects and transcribes text in an image. However, it is currently insufficient for accessibility purposes for three reasons:

It can and does get a lot wrong. It's most accurate on simple images of plain text (e.g. screenshots of social media posts) but even there produces errors from time to time. Accessibility services have to be as close to 100% accuracy as possible. OCR just isn't reliable enough for that.

Even were OCR able to 100%-accurately describe text, there are many portions of images that don't have text, or relevant context that should be placed in transcriptions to aid understanding. OCR can't do this.

"AI" in terms of what most people mean by it - generative AI - should never be used for anything where accuracy is a requirement. Generative AI doesn't answer questions, it doesn't describe images, and it doesn't read text. It takes a prompt and it generates a statistically-likely response. No matter how well-trained it is, there's always a chance that it makes up nonsense. That simply isn't acceptable for accessibility.

3. Why do you say "image transcription" and not "image ID"?

I'm from r/TranscribersOfReddit and we called them transcriptions there. It's ingrained in my mind.

For the same reason, I follow advice and standards from our old guidelines that might not exactly match how many Tumblr transcribers do things.

[oblivionwonderlust]

the irony of the whole AI discourse is that the people who go around saying “AI won’t put you out of a job, people who know how to use AI will” is that these people who “know how to use AI” will instantly lose their jobs if AI actually did what it says on the tin.

the whole point of gen AI is to take some kind of vague human input describing and output and produce that— the way a client gives a team of professionals a brief and expects a deliverable of a certain quality. Generally, there are a couple rounds of feedback and reviews before the deliverable is handed over. the issue with gen AI is that it requires the initial input to be very precise and feedback generally results in a process of recreation (that creates its own new issues) as opposed to feedback driven modification of the existing deliverable.

here’s where the “prompt engineers” come in. the entire point of this group of people is to take a vague request and then “translate” that to a precise form that the AI can “understand”. they are the people who “know how to use AI”. their allure is very obvious to someone that only thinks in numbers— instead of paying a team of professionals, you can just pay this one guy who says he is the AI whisperer. the issue with this, of course, is that the quality of the deliverable is not very good. no matter how precise the input is, the quality of the output is fundamentally limited by whatever model is powering it. there is also the fact that most people that call themselves “prompt engineers” tend not to have broader creative credentials and may not be able to identify what might constitute as high quality.

this emergent class of “prompt engineers” tends to be the proselytisers of the use of AI and are specifically the ones going around saying “we’re gonna replace all artists with AI”— and also tend to be the people giving feedback to the development team behind these models. there is one obvious problem with this: the “prompt engineers” need the AI to be very difficult to use if they want to keep their job.

if gen AI starts doing what it promises, turning vague inputs into some kind of finished deliverable, the same client will turn around and say “what do i need a prompt engineer for?” and the IKEA effect will make the client be satisfied with the output, even if the quality is garbage.

the “prompt engineer” grift (and it is a grift) is a very precarious one. on the one hand, you need AI models to improve because that’s the hard limit of the quality you can provide (which is the same as every single other “prompt engineer” can provide mind you). on the other hand, you need it to be absolutely garbage to use and remain as it, lest your clients replace you or your tricks stop working.

[schraubd]

What Quality of Language Will LLMs Converge On?

Like many professors, I've been looking uneasily at the development of Large Language Models (LLMs) and what they mean for the profession. A few weeks ago, I wrote about my concerns regarding how LLMs will affect training the next generation of writers, particularly in the inevitably-necessary stage where they're going to be kind of crummy writers.

Today I want to focus on a different question: what quality of writing are LLMs converging upon? It seems to me there are two possibilities:

As LLMs improve, they will continually become better and better writers, until eventually they surpass the abilities of all human writers.

As LLMs improve, they will more closely mimic the aggregation of all writers, and thus will not necessarily perform better than strong human writers.

If you take the Kevin Drum view that AI by definition will be able to do anything a human can do, but better, then you probably think the end game is door number one. Use chess engines as your template. As the engines improved, they got better and better at playing chess, until eventually they surpassed the capacities of even the best human players. The same thing will eventually happen with writing.

But there's another possibility. Unlike chess, writing does not have an objective end-goal to it that a machine can orient itself to. So LLMs, as I understand them, are (and I concede this is an oversimplification) souped-up text prediction programs. They take in a mountain of data in the form of pre-existing text and use it to answer the question "what is the most likely way that text would be generated in response to this prompt?"

"Most likely" is a different approach than "best". A chess engine that decided its moves based on what the aggregate community of chess players was most likely to play would be pretty good at chess -- considerably better than average, in fact, because of the wisdom of crowds. But it probably would not be better than the best chess players. (We actually got to see a version of this in the "Kasparov vs. the World" match, which was pretty cool especially given how it only could have happened in that narrow window when the internet was active but chess engines were still below human capacities. But even there -- where "the world" was actually a subset of highly engaged chess players and the inputs were guided by human experts -- Kasparov squeaked out a victory). 

I saw somewhere that LLMs are facing a crisis at the moment because the training data they're going to draw from increasingly will be ... LLM-generated content, creating not quite a death spiral but certainly the strong likelihood of stagnation. But even if the training data was all human-created, you're still getting a lot of bitter with the sweet, and the result is that the models should by design not surpass high-level human writers. When I've looked at ChatGPT 4 answers to various essay prompts, I've been increasingly impressed with them in the sense that they're topical, grammatically coherent, clearly written, and so on. But they never have flair or creativity -- they are invariably generic.

Now, this doesn't mean that LLMs won't be hugely disruptive. They will be. As I wrote before, the best analogy for LLMs may be to mass production -- it's not that they produce the highest-quality writing, it's that they dramatically lower the cost of adequate writing. The vast majority of writing does not need to be especially inspired or creative, and LLMs can do that work basically for free. But at least in their current paradigm, and assuming I understand LLMs correctly, in the immediate term they're not going to replace top-level creative writing, because even if they "improve" their improvement will only go in the direction of converging on the median.

via The Debate Link https://ift.tt/hwCIMir

[vinhjacker1]

How should I create a hit when I do e-commerce?

Creating a hit in e-commerce requires a combination of factors, including:

Unique product or service: A product or service that is unique and valuable to your target audience is essential for standing out from the competition.

High-quality product photos: Good product photos are essential for making a good impression on potential customers.

Effective product listings: Product listings should be informative and engaging, and highlight the key features and benefits of your product.

Strong branding: A strong brand identity will help you connect with your target audience and establish yourself as a trusted source.

Competitive pricing: Your prices should be competitive with other sellers, but you should also make sure to factor in the cost of goods, shipping, and other expenses.

Effective marketing: E-commerce requires a strong marketing strategy to reach your target audience and drive sales. This could include:

Search engine optimization (SEO): Optimizing your website and product listings to rank higher in search engine results pages (SERPs).

Social media marketing: Using social media to connect with your target audience and promote your products.

Pay-per-click (PPC) advertising: Running ads on search engines and social media platforms to reach a wider audience.

Email marketing: Sending email newsletters to your subscribers to keep them updated on new products, promotions, and other news.

Content marketing: Creating and sharing informative and engaging content to attract potential customers and establish your brand as a thought leader.

Excellent customer service: Providing excellent customer service is essential for building trust and loyalty with your customers. This includes prompt and helpful responses to inquiries, a willingness to go the extra mile to resolve issues, and a commitment to providing a positive customer experience.

Adaptability and innovation: The e-commerce landscape is constantly evolving, so it is important to be adaptable and innovative to stay ahead of the curve. This could include:

Keeping up with the latest trends in e-commerce: Following industry news and trends to identify opportunities to improve your business.

Experimenting with new marketing techniques: Trying new marketing strategies to see what works best for your business.

Embracing new technologies: Adopting new technologies that can improve your e-commerce operations, such as artificial intelligence (AI) and machine learning (ML).

By following these tips, you can increase your chances of creating a hit in e-commerce. However, it is important to remember that success in e-commerce is not guaranteed, and it takes hard work, dedication, and a willingness to learn and adapt.

Here are some additional tips for creating a hit in e-commerce:

Focus on your niche: Don't try to be everything to everyone. Focus on a specific niche market and become the go-to source for products or services in that area.

Build relationships with influencers: Partnering with influencers in your niche can help you reach a wider audience and promote your products or services.

Create a great shopping experience: Make sure your website is easy to navigate and that the checkout process is seamless.

Offer excellent customer service: Respond to customer inquiries promptly and helpfully.

Get involved in your community: Participate in online forums and groups related to your niche.

Be patient: It takes time to build a successful e-commerce business. Don't get discouraged if you don't see results overnight.

By following these tips and staying up-to-date on the latest trends in e-commerce, you can increase your chances of creating a hit.

I hope this helps!

[kanguin]

Yeah search engines have sucked shit for a while and are just getting bloated and worse. It used to be a simple program that read your search words, looked for modifiers (+, -, ""), did an efficient search of archived web pages, then returned results. This is why Google got popular, why it became so big. It did the job exactly how you asked it to and returned results ranked by how well it feels they match your search parameters, without unnecessary garbage or a long delay. Google's chaching of websites is actually why you used to be able to use Google to view offline copies of webpages in your search results; it was just part of how Google searches anyway, so might as well share that as a feature for people who want it, right?

But money talks and features disappeared, hidden "features" got integrated, and nowadays the only modifier (+,-,"",etc) that works properly anymore is the quotes. Because along the way, brands started paying off Google to secretly add their sponsored words to certain searches based on what you're looking for, and various parties started paying off Google more to remove and blacklist certain terms in part of the world to censor what people see without telling them.

There was a post on here going around a few years ago where an AI image generator was asked to draw Homer Simpson, but got racebent Homer instead. Everyone was scratching their heads at why this happened. It turned out the ai developers were told to make the results less whitewashed, but instead of retraining the dataset to punish bias toward white faces with no other descriptors, they simply took the lazy route and would inject words like "diverse" and "poc" and so on into the prompt without the user's knowledge, which is why the results were so bizarre, with named characters and people generated being put through a bigot's idea of a "woke" filter.

Google, and most other search engines today honestly, does the same thing, just with paid sponsors and censors instead of a half-assed attempt at seeming more diverse.

But in more recent years, as search engines have been guzzling the cocks of big businesses for years and bending to their every demand so long as it means more money, search engines have stopped being code driven and have been learning-algorithm based as well. Whether this improves anything for the developers I have no clue, but what this means is that instead of a simple machine reading inputs and giving simple outputs, so many hands are in the pot of search engine optimization that it's really just being read by a computer taught to comprehend text, modified with a bunch of parameters, passed through a neural network black box, then spat out the other side. This is why searching anything is so shitty now, and why we have so little control over the output of any search. Yes, even DuckDuckGo doesn't give you the control you had on Google 20 years ago.

I just. I wish a new search engine company would start up and do things the old way again. Back in 2010 you had so much control over your search results if you knew all the tricks. But realistically Google probably has a patent on their old way of doing things, so that seems dismal.

[itfreaks]

How AI is Shaping the Future of SEO: What Digital Marketers Must Know

In today's fast-paced digital world, How AI is Shaping the Future of SEO is more than just a buzzphrase—it’s a reality transforming how businesses attract, engage, and convert audiences. From content generation to search ranking dynamics, artificial intelligence (AI) is redefining every aspect of search engine optimization (SEO). In this comprehensive guide, we’ll break down How AI is Shaping the Future of SEO in clear, accessible language for digital marketers. We’ll explore practical insights, proven strategies, and expert advice to help you stay ahead of the curve.

1. What Exactly Is AI in SEO?

AI in SEO refers to the use of machine learning, natural language processing (NLP), and other intelligent algorithms to improve how websites rank and how content is created, analyzed, and optimized. Search engines like Google now rely heavily on AI to:

Understand user intent and context

Evaluate content quality with E-A-T (Expertise, Authoritativeness, Trustworthiness)

Optimize search result relevance

Knowing How AI is Shaping the Future of SEO helps you leverage these advancements effectively.

2. Predictive Analytics: Smart Strategy Before You Start

One of the biggest benefits of AI-driven SEO is predictive analytics. Tools powered by AI can now forecast:

Trending keywords

Seasonal or regional search behaviors

Unexpected spikes in search interest

By integrating predictive analytics, brands can proactively adjust strategies rather than react after their competitors. This proactive approach shows precisely How AI is Shaping the Future of SEO through data-driven foresight.

3. AI-Powered Keyword Research and Topic Ideation

Traditional keyword research is time-consuming. AI tools automate this by analyzing large volumes of keyword data, clustering related terms, and identifying semantic connections. This reveals long-tail keywords and content gaps that manual research might miss.

When you grasp How AI is Shaping the Future of SEO, you recognize that AI isn’t just boosting speed—it’s enhancing insight quality, allowing marketers to craft laser-focused content that resonates.

4. Automated Content Creation and Optimization

Generative AI tools, like GPT-based models, are now used to draft blog posts, product descriptions, meta tags, and more. Tips for using these tools effectively:

Use AI for first drafts: Save time but always refine with a human touch.

Follow SEO best practices: Incorporate your primary keyword (e.g., “How AI is Shaping the Future of SEO”) within the first paragraph, H1/headings, and naturally throughout (~2–4% density).

Quality check: Ensure tone, accuracy, and context align with your brand voice.

By leaning into How AI is Shaping the Future of SEO, marketers can produce bulk content faster without sacrificing quality or relevance.

5. Enhancing User Experience with AI

Search engines reward sites that deliver excellent user experience (UX). AI helps by:

Optimizing site structure: AI analyzes navigation patterns and suggests improvements.

Personalizing content: Tailored recommendations based on user behavior.

Predicting churn: Spotting users likely to exit early and prompting dynamic interventions.

These applications highlight How AI is Shaping the Future of SEO by intertwining UX and SEO for better engagement and lower bounce rates.

6. AI-Driven Technical SEO: Smarter, Faster, Better

On the technical side, AI excels in areas like:

Crawl budget optimization: Prioritizing important pages to reduce wasted resources.

AI-powered image & video tagging: Improving discoverability through smart alt text and captions.

Log file analysis: Detecting crawl errors and inefficiencies quickly.

All these technical gains reflect How AI is Shaping the Future of SEO behind the scenes.

7. Semantic Search: The Role of NLP

Semantic search uses NLP to interpret not just keywords, but the entire meaning behind queries. AI understands synonyms, intent, and context—making SEO more sophisticated.

To stay aligned with How AI is Shaping the Future of SEO, embrace:

Topic clusters: Structure content with pillar pages and subtopics.

Answer boxes & featured snippets: Write concise, question-based content.

Entity-based optimization: Include relevant entities associated with your topic.

This shifts SEO from “keyword stuffing” to genuine, meaningful content.

8. Smarter Link Building with AI

Traditional link-building can be tedious. AI revolutionizes it by:

Identifying outreach opportunities: Find relevant blogs, forums, and stakeholders.

Predicting link-worthy content: Spot topics that naturally attract backlinks.

Monitoring backlinks: Alerting you to toxic links or broken links that harm SEO.

Seeing How AI is Shaping the Future of SEO here shows it’s not just smarter work—it’s more efficient and effective link strategies.

9. Voice and Visual Search: AI in Emerging Interfaces

Voice assistants (Google Assistant, Siri, Alexa) and visual search (Google Lens) rely heavily on AI:

Voice search: Focus on natural phrasing and local intent.

Visual search: Optimize images with structured metadata and descriptive captions.

Understanding How AI is Shaping the Future of SEO means preparing for these next-gen search methods.

10. The Role of Tools

A variety of AI-powered SEO tools are transforming how marketers plan and execute strategies:

Surfer SEO: Helps structure content and improve on-page SEO elements.

MarketMuse: Assists in deep topic research and content scoring for better authority.

ChatGPT API: Useful for content generation, idea expansion, and query refinement.

For those looking to learn how to use AI in SEO, platforms like WsCube Tech offer reliable and structured training. Their practical, hands-on courses are designed for beginners and professionals who want to stay ahead in the SEO game.

Agencies and learners alike are now blending human creativity with AI efficiency, and WsCube Tech is playing a pivotal role in preparing the next generation of SEO experts. That’s a great example of How AI is Shaping the Future of SEO by combining education, expertise, and cutting-edge tools.

11. Ethics and the Human Touch

As AI takes a larger role, ethics and human judgment become critical:

Avoid AI-generated fluff: Always fact-check and edit.

Stay transparent: Let users know when content is AI-assisted.

Value human experience: Unique perspectives still drive engagement and trust.

This balance shows How AI is Shaping the Future of SEO responsibly and sustainably.

12. Measuring Impact: AI-Powered Analytics

AI enhances analytics platforms by:

Predicting performance: Identify top- and low-performing pages.

Attributing ROI: Assign conversions to content and channels smartly.

Automated reporting: Deliver real-time dashboards with insights.

These metrics help marketers understand How AI is Shaping the Future of SEO in measurable, actionable ways.

13. Practical Roadmap for Digital Marketers

Ready to act? Here’s a step-by-step plan embracing How AI is Shaping the Future of SEO:

Audit your current SEO: Include technical, content, and link profiles.

Select AI tools: Choose a mix that suits your budget and goals.

Create an AI-driven pilot: Focus on content, UX, or technical improvement.

Test and iterate: Use A/B testing and analytics to refine strategies.

Scale with oversight: Expand successful pilots while monitoring quality.

Stay updated: Keep learning about new AI features from search engines.

14. FAQs on AI in SEO

Q: Will AI replace SEO professionals? A: No—it enhances human work. Strategy, creativity, and judgment remain indispensable.

Q: How do I maintain SEO keyword density? A: Include your main keyword naturally in titles, headings, first paragraphs, and body—around 2–4% is safe.

Q: Can AI help with voice search? A: Yes—voice search optimization requires natural phrasing and conversational tone, which AI can help craft.

15. Final Thoughts

AI is not just a tool—it’s redefining How AI is Shaping the Future of SEO across every layer of the field. From smart content to predictive analytics, voice interfaces to technical automations, AI accelerates and refines how marketers optimize websites. The competitive edge belongs to those who leverage AI thoughtfully—complemented by human expertise and ethical standards.

As digital marketers, it’s time to embrace AI: test the right tools, stay ethical, focus on value, and keep people at the center. That’s the real future of SEO.

[aditi2987]

Why AI in HR Isn’t Just a Trend — It’s a Transformation

Artificial Intelligence (AI) is redefining how businesses operate, and nowhere is this transformation more evident than in Human Resources. Once considered a back-office function, HR is now at the forefront of digital evolution, and AI is the engine driving this shift. This isn’t about following a trend; it’s about fundamentally reshaping how people are hired, managed, and retained.

From predictive analytics to intelligent automation, platforms like uKnowva HRMS are integrating AI tools that help HR teams deliver faster, more accurate, and more personalized services across the employee lifecycle.

The Difference Between Trend and Transformation

A trend is temporary — a response to the moment. A transformation is structural, strategic, and irreversible. AI in HR is proving to be the latter because it:

Solves long-standing challenges like bias and inefficiency

Adapts to complex, real-time data environments

Scales consistently across geographies and business units

Organizations that embrace AI aren’t just modernizing — they’re future-proofing.

Key Areas Where AI Is Transforming HR

1. Talent Acquisition

AI helps HR teams:

Analyze resumes quickly and fairly

Rank candidates based on fit

Predict hiring success using historical data

uKnowva HRMS integrates AI-powered recruitment tools that reduce time-to-hire while improving quality-of-hire.

2. Employee Onboarding and Experience

AI chatbots assist new hires with FAQs, process guidance, and personalized onboarding plans, creating a smoother, touchless first week.

uKnowva HRMS enables AI-driven onboarding workflows that reduce HR workload while increasing employee satisfaction.

3. Performance Management and Feedback

With AI, organizations can:

Track performance trends

Recommend training paths

Automate goal tracking and review cycles

uKnowva HRMS’s intelligent performance tools suggest development plans and highlight at-risk employees, making HR more proactive.

4. Predictive Attrition and Engagement Insights

AI analyzes behavior, feedback, and system usage to:

Forecast attrition risks

Identify disengaged employees

Suggest timely interventions

Using uKnowva HRMS’s analytics dashboard, HR leaders gain real-time visibility into workforce sentiment and engagement patterns.

5. Learning and Development

AI customizes learning paths based on:

Role-specific needs

Skill gaps

Career goals

This ensures employees only get relevant content, boosting ROI on L&D initiatives. uKnowva HRMS links performance data to learning modules for precision development.

Why This Matters to the Business

AI doesn’t just make HR faster. It makes it:

Smarter decisions backed by real-time data

Fairer algorithms can reduce unconscious bias

Scalable — consistent experience regardless of location

Strategic — HR becomes a partner in long-term business goals

Real-World Example: Redefining Performance Culture

A mid-sized enterprise used uKnowva HRMS to introduce AI into their performance system. Managers received prompts to provide feedback. Employees saw automated goal suggestions. Engagement and review participation jumped by 40% in one quarter.

Tips for HR Teams Embracing AI

Start with high-impact areas like recruitment or performance reviews

Audit data quality to improve model accuracy

Ensure transparency and explainability of AI decisions

Combine AI insights with human judgment for the best results

Final Thoughts

AI in HR isn’t about replacing people — it’s about empowering them. It helps HR teams move from reactive administration to proactive strategy. And that’s not a trend. That’s a transformation.

With uKnowva HRMS, organizations can integrate AI across recruitment, onboarding, engagement, performance, and analytics — creating a digital-first, people-smart workplace. For businesses that want to stay competitive, embracing AI in HR isn’t just smart — it’s essential.

[himanshu123]

Finding the Right Prompt Engineer: Skills, Traits, and More 

As artificial intelligence (AI) continues to revolutionize industries, the demand for specialized roles like prompt engineers has skyrocketed. In particular, businesses working with AI-powered platforms, such as language models, need experts who can effectively design and optimize prompts to achieve accurate and relevant results. So, what should you look for when you hire prompt engineers? Understanding the essential skills, experience, and mindset required for this role is key to ensuring you find the right fit for your project. 

Prompt engineering is the art and science of crafting inputs to AI models that guide the model to generate desired outputs. It's a crucial skill when leveraging large language models like GPT-3, GPT-4, or other AI-driven tools for specific tasks, such as content generation, automation, or conversational interfaces. As AI technology becomes more sophisticated, so does the need for skilled professionals who can design these prompts in a way that enhances performance and accuracy. 

In this blog post, we will explore the key factors to consider when hiring a prompt engineer, including technical expertise, creativity, and practical experience. Understanding these factors can help you make an informed decision that will contribute to the success of your AI-driven initiatives. 

Key Skills and Qualifications for a Prompt Engineer 

Strong Understanding of AI and Machine Learning Models 

First and foremost, a prompt engineer must have a deep understanding of how AI models, particularly language models, work. They should be well-versed in the theory behind natural language processing (NLP) and how AI systems interpret and respond to different types of input. This knowledge is critical because the effectiveness of the prompts hinges on how well the engineer understands the AI's capabilities and limitations. 

A background in AI, machine learning, or a related field is often essential for prompt engineers. Ideally, the candidate should have experience working with models such as OpenAI's GPT, Google's BERT, or other transformer-based models. This understanding helps the engineer tailor prompts to achieve specific outcomes and improve the model's efficiency and relevance in various contexts. 

Experience with Data and Text Analysis 

Another vital skill is the ability to analyze and interpret large volumes of text data. A prompt engineer should be able to identify patterns, trends, and nuances in text to create prompts that will extract the right kind of response from an AI model. Whether the goal is to generate content, conduct sentiment analysis, or automate a process, understanding how to structure data for the model is key to delivering high-quality outputs. 

The ability to process and analyze textual data often goes hand-in-hand with a good command of programming languages such as Python, which is commonly used for text processing and working with machine learning libraries. Familiarity with libraries like TensorFlow or Hugging Face can be an added advantage. 

Creativity and Problem-Solving Abilities 

While prompt engineering may seem like a highly technical role, creativity plays a significant part. A good prompt engineer needs to think creatively about how to approach problems and design prompts that generate relevant, meaningful, and often innovative responses. This requires a balance of logic and imagination, as engineers must continually experiment with different prompt variations to find the most effective one. 

A strong problem-solving mindset is necessary to optimize prompt performance, especially when working with models that are not perfect and may produce unexpected results. The best prompt engineers know how to tweak inputs, fine-tune instructions, and adjust formatting to guide the AI model toward desired outcomes. 

Communication Skills and Collaboration 

Although prompt engineers often work with AI, they must also work closely with other team members, including developers, designers, and project managers. Effective communication is critical when collaborating on a project. A prompt engineer should be able to explain complex technical concepts in an easy-to-understand manner, ensuring that stakeholders are aligned and understand the capabilities and limitations of the AI system. 

Good collaboration skills also mean being able to work well within interdisciplinary teams. Since prompt engineering is part of a broader AI or product development process, a prompt engineer needs to understand the project’s objectives and collaborate with others to deliver solutions that fit seamlessly into the product. 

Technical Tools and Methodologies to Consider 

When hiring prompt engineers, it’s also important to consider the tools and methodologies they are familiar with. The ideal candidate should have experience with prompt generation tools, as well as the ability to evaluate the performance of different prompts. Familiarity with AI model training, testing, and debugging is highly valuable, as well as knowledge of evaluation metrics for AI outputs. 

Experience with platforms such as OpenAI, Google AI, and Microsoft’s Azure AI can be an advantage, especially if your project involves using specific tools provided by these companies. Additionally, being proficient in working with cloud platforms, APIs, and working knowledge of DevOps practices is beneficial for smoother integration with your overall system. 

How to Estimate Costs for AI-Based Projects 

When integrating AI models into your business, whether it's for customer service chatbots, content generation, or automating business processes, it’s essential to have a clear understanding of the costs involved. One useful tool for estimating these costs is a mobile app cost calculator, especially if you're incorporating AI into mobile app development. These calculators consider factors like the complexity of AI integration, the amount of data processed, and the need for scalability, providing a rough estimate of how much your project will cost to develop and deploy. 

If your AI implementation is aimed at improving a mobile or web application, understanding the full scope of the project, including the cost of hiring prompt engineers, AI model development, and integration, is crucial for proper budgeting. 

If you're interested in exploring the benefits of prompt engineering services services for your business, we encourage you to book an appointment with our team of experts. 

Book an Appointment 

Conclusion: Prompt Engineering Services and the Right Hire 

When you are ready to embark on an AI-driven project, hiring the right prompt engineer can make all the difference. The combination of technical expertise, creativity, and problem-solving skills is essential for crafting effective prompts that will optimize AI models and generate high-quality results. By focusing on these key qualities, you can hire prompt engineers who will contribute significantly to the success of your project. 

If you’re looking for expert prompt engineering services that will align with your business goals, ensure optimal AI performance, and drive results, don’t hesitate to reach out. By understanding the key factors to consider when hiring a prompt engineer, you’ll be well on your way to building powerful AI solutions tailored to your needs. 

[excelrthane1]

Hybrid AI Systems: Combining Symbolic and Statistical Approaches

Artificial Intelligence (AI) over the last few years has been driven primarily by two distinct methodologies: symbolic AI and statistical (or connectionist) AI. While both have achieved substantial results in isolation, the limitations of each approach have prompted researchers and organisations to explore hybrid AI systems—an integration of symbolic reasoning with statistical learning.   

This hybrid model is reshaping the AI landscape by combining the strengths of both paradigms, leading to more robust, interpretable, and adaptable systems. In this blog, we’ll dive into how hybrid AI systems work, why they matter, and where they are being applied.

Understanding the Two Pillars: Symbolic vs. Statistical AI

Symbolic AI, also known as good old-fashioned AI (GOFAI), relies on explicit rules and logic. It represents knowledge in a human-readable form, such as ontologies and decision trees, and applies inference engines to reason through problems.

Example: Expert systems like MYCIN (used in medical diagnosis) operate on a set of "if-then" rules curated by domain experts.

Statistical AI, on the other hand, involves learning from data—primarily through machine learning models, especially neural networks. These models can recognise complex patterns and make predictions, but often lack transparency and interpretability.

Example: Deep learning models used in image and speech recognition can process vast datasets to identify subtle correlations but can be seen as "black boxes" in terms of reasoning.

The Need for Hybrid AI Systems

Each approach has its own set of strengths and weaknesses. Symbolic AI is interpretable and excellent for incorporating domain knowledge, but it struggles with ambiguity and scalability. Statistical AI excels at learning from large volumes of data but falters when it comes to reasoning, abstraction, and generalisation from few examples.

Hybrid AI systems aim to combine the strengths of both:

Interpretability from symbolic reasoning

Adaptability and scalability from statistical models

This fusion allows AI to handle both the structure and nuance of real-world problems more effectively.

Key Components of Hybrid AI

Knowledge Graphs: These are structured symbolic representations of relationships between entities. They provide context and semantic understanding to machine learning models. Google’s search engine is a prime example, where a knowledge graph enhances search intent detection.

Neuro-symbolic Systems: These models integrate neural networks with logic-based reasoning. A notable initiative is IBM’s Project Neuro-Symbolic AI, which combines deep learning with logic programming to improve visual question answering tasks.

Explainability Modules: By merging symbolic explanations with statistical outcomes, hybrid AI can provide users with clearer justifications for its decisions—crucial in regulated industries like healthcare and finance.

Real-world Applications of Hybrid AI

Healthcare: Diagnosing diseases often requires pattern recognition (statistical AI) and domain knowledge (symbolic AI). Hybrid systems are being developed to integrate patient history, medical literature, and real-time data for better diagnostics and treatment recommendations.

Autonomous Systems: Self-driving cars need to learn from sensor data (statistical) while following traffic laws and ethical considerations (symbolic). Hybrid AI helps in balancing these needs effectively.

Legal Tech: Legal document analysis benefits from NLP-based models combined with rule-based systems that understand jurisdictional nuances and precedents.

The Role of Hybrid AI in Data Science Education

As hybrid AI gains traction, it’s becoming a core topic in advanced AI and data science training. Enrolling in a Data Science Course that includes modules on symbolic logic, machine learning, and hybrid models can provide you with a distinct edge in the job market.

Especially for learners based in India, a Data Science Course in Mumbai often offers a diverse curriculum that bridges foundational AI concepts with cutting-edge developments like hybrid systems. Mumbai, being a major tech and financial hub, provides access to industry collaborations, real-world projects, and expert faculty—making it an ideal location to grasp the practical applications of hybrid AI.

Challenges and Future Outlook

Despite its promise, hybrid AI faces several challenges:

Integration Complexity: Merging symbolic and statistical approaches requires deep expertise across different AI domains.

Data and Knowledge Curation: Building and maintaining symbolic knowledge bases (e.g., ontologies) is resource-intensive.

Scalability: Hybrid systems must be engineered to perform efficiently at scale, especially in dynamic environments.

However, ongoing research is rapidly addressing these concerns. For instance, tools like Logic Tensor Networks (LTNs) and Probabilistic Soft Logic (PSL) are providing frameworks to facilitate hybrid modelling. Major tech companies like IBM, Microsoft, and Google are heavily investing in this space, indicating that hybrid AI is more than just a passing trend—it’s the future of intelligent systems.

Conclusion

Hybrid AI systems represent a promising convergence of logic-based reasoning and data-driven learning. By combining the explainability of symbolic AI with the predictive power of statistical models, these systems offer a more complete and reliable approach to solving complex problems.

For aspiring professionals, mastering this integrated approach is key to staying ahead in the evolving AI ecosystem. Whether through a Data Science Course online or an in-person Data Science Course in Mumbai, building expertise in hybrid AI will open doors to advanced roles in AI development, research, and strategic decision-making.

Business name: ExcelR- Data Science, Data Analytics, Business Analytics Course Training Mumbai

Address: 304, 3rd Floor, Pratibha Building. Three Petrol pump, Lal Bahadur Shastri Rd, opposite Manas Tower, Pakhdi, Thane West, Thane, Maharashtra 400602

Phone: 09108238354 

Email: enquiry@excelr.com