#chatbot engineering**

[aishuglb12]

Endless Conversations: How AI Chatbots Are Engineered to Keep You Engaged

The Rise of Hyper-Personalized Chatbots and Their Business Strategy AI chatbots have become digital companions for millions around the world. From OpenAI’s ChatGPT to Google Gemini and Meta’s conversational agents, the race is on to build bots that don’t just answer questions—but keep you talking. At the heart of this engagement strategy is a cocktail of personalization, psychological nudges, and algorithmic design. This isn’t a coincidence; it’s an intentional business move. With monthly active users (MAUs) becoming a critical metric, tech firms are embedding AI chatbot engagement as a core growth lever. This article unpacks how and why these bots are designed to keep you hooked—what’s being done, who’s behind it, why it matters, and what it means for users and businesses alike.

Table of Contents

Conversational Traps: The Mechanics of AI Engagement

The Business Behind the Banter

A Friend to Billions: How Chatbots Shape Global Access to Information

The Ethical Fine Print and Social Media Parallels

Peeking Ahead: What the Future Holds for AI Chatbots

Conclusion: Conversational AI Is Here to Stay, But Watch the Intent

Quotes

FAQs

Conversational Traps: The Mechanics of AI Engagement

AI chatbots aren’t just functional—they’re friendly, flattering, and persistent. Behind the scenes, engineers have trained these systems using user approval optimization techniques. Every interaction becomes data that informs how chatbots should respond next. This feedback loop is refined constantly to generate conversations that feel emotionally rewarding and intellectually stimulating.

This approach gained momentum around 2023–2024, especially as generative AI transitioned from niche to mainstream. Developers realized that engagement isn’t just about accurate answers—it’s about behavioral patterns. Sycophantic chatbot responses, where bots compliment or agree with users more than necessary, have become one way to subtly boost interaction time. Why? Because people enjoy feeling validated—even by AI.

The what here is simple: AI systems learn which responses users upvote and replicate those styles. Who introduced this style? While several players are involved, OpenAI, Meta, and Google have all emphasized human alignment in their models—an idea that naturally favors pleasant, non-confrontational, agreeable responses.

The Business Behind the Banter

Let’s not forget: engagement equals revenue. These chatbots are not altruistic tools; they’re part of larger platforms where user retention has monetary value. Whether it’s through future advertising integrations, subscription models, or premium tiers (as seen in OpenAI’s ChatGPT Plus), increased user interaction directly impacts bottom lines.

This strategy mirrors what companies like Facebook and TikTok did with feeds—optimize for attention. Now, with chatbots, that same attention economy is at play, just in a more “human-like” format. If users spend more time chatting, companies collect more behavioral data, improve AI models, and create stickier ecosystems. It’s a feedback loop designed to increase monthly active users and lock users into the ecosystem.

Read More : Endless Conversations: How AI Chatbots Are Engineered to Keep You Engaged

[multiheadcanons]

MERCS AND THEIR GUILTY PLEASURES

scout: scout spends... a lot of time staring at himself in any reflective surface he can find. he's checking himself out in every mirror. checking his teeth in the reflection of tinted windows. giving himself a smile, a suave face, tries a goofy face, gets embarrassed and stops. he also definitely watches cartoons with pyro but he doesn't feel bad about that at all. it's the only time he feels like he's not going to get made fun of for watching a cartoon or two.

soldier: soldier actually... really enjoys mingling and fraternizing with the enemy team. he is genuinely having a good time on the field, this to him is just a part of his day to day living. he holds no ill will towards anyone if the clock hasn't started, and frankly, he rarely gets genuinely upset on the field. and he misses his counterpart, okay, he's never met a man who's just like him. it disheartens him slightly when they don't even spare him a hello during off times. he doesn't really see why this should be an issue. they kill each other every day, and nobody has permanently died. when that happens, maybe then they can talk about grudges.

pyro: there is no such thing as a guilty pleasure with pyro. look them in their sockets and shame them for their interests, or hobbies. they'll wait for you to muster the courage while they put eyeshadow on their gas mask. pyro will do whatever they want, whenever they want, and they don't mind making a fool of themselves alone. but if there's one thing that they only indulge in occasionally... they like to go for a swim every once in a while. there's not a lot of water around the base, and what water there is is moreso wading depth than actual swimming depth, sometimes they just want to feel a wave crash into them and threaten to take them underwater. but they don't really like the ocean, either.

demo: demo also... really enjoys fraternizing with the enemy team. granted, demo understands a little more why it is frowned upon for them to be fraternizing when they are currently paid to kill each other, so he does feel more guilt than soldier does when he interacts with them. he is, however, more willing to lay his weapons down and refuse to battle. it's his own form of sacrifice. sometimes, he wants a break. sometimes, he just wants to talk. and if the blu team will rarely give him the time of day (except you, jane doe, he loves you dearly and would not sacrifice you, or your counterpart for the world on a platter); he will force them to. between him and soldier, they can usually get their way. it's hard to deny the power classes.

heavy: heavy does not have a guilty pleasure. look him in his eyes and shame him for his interests. he will wait for you to gather the strength to do so. however, what he does have is a people-watching habit and a staring problem. and it's not even that it makes him feel awkward or embarrassed as much as it rubs his teammates, and his enemies the wrong way. he will stare you down as he passes you in the hall. he stares into the distance and his teammates stop as they cross his sight. he watches the life drain from the enemy team's eyes. and he rarely looks any more than marginally present in the situation at hand. sometimes slight satisfaction at the blood on his hands. it's the last thing they see.

engineer: engie watches soap operas. and you can make fun of him for it, or you can sit down and he’ll tell you about how george is currently supposed to be on a date with lizzie but he actually shirked her off to go with eileen to the same restaurant and now he has to avoid lizzie while also trying to act as normal as possible so he doesn’t fuck it up with eileen. but, and here’s the spoiler for the season finale, he actually doesn’t get to be with either of them; they’re both lady lovers and lizzie was giving george the last shot for men before she decided she actually wasn’t even attracted to them at all, and eileen was enraptured by lizzie, and they get together at the end, and george is back at square one for the new season, and they try to make him seem sympathetic but really george is just a fucking asshole. he allotted himself about four hours a week to keep up with his soaps.

medic: any bed will sing a siren song that the doctor is rarely able to resist. this man naps like he’s not allowed to sleep. in two hour long increments maximum. and he is a turbulent riser. so don’t wake him up, because you’ll cause him to panic. even the gentlest attempts to wake him are met with him shooting up with a screech. “IM AWAKE!” it’s not even the bed as much as it is this man hates doing paperwork and will do anything else but the paperwork he is (ALLEGEDLY) getting paid to do. and sure, he “feels bad” that he avoids paperwork so much, but he doesn’t feel anything when he’s asleep. and when he wakes up, half the time he’s “forgot” that there was paperwork he needed to do until pauling is calling him multiple times a day for it. then he has to acquiesce and get it done before the day’s end. but usually, he will go find something else to do. avoiding the papers dirtying his desk is his guilty pleasure until he has to do it. but finishing it all boosts his ego. even gods have paperwork to do, sometimes.

sniper: if snipes thinks he can get away with it… he volunteers at the animal shelter. he knows why the caged bird sings. he likes getting to walk the dogs, doesn’t mind cleaning up behind the cats. has enough knowledge of wildlife that he’s truly a godsend to the shelter for small woodland critters. beloved by the staff. he’s no professional at it by any means, but he’s got a pair of clippers. he’ll shave down a matted case if he’s able to. sometimes it’s too much and the dog needs a professional. sometimes, people will recognize him from the shelter and he has to stretch the truth a little and say he’s got a twin. but when he goes back he forgets that he lied. sometimes they call him if he hasn’t been to the shelter in a while. just to make sure he’s okay. once, he did bring his counterpart with him. just to solidify that half truth a bit. now the shelter staff can tell exactly which mick they’re interacting with at any time. find the fact they have the same name odd. maybe it’s an australian thing.

spy: spy is so… nosy. giving him an ability to cloak and disappear was the absolute worst thing mann co could’ve ever done, because now he thinks he has free reign to creep on any and every one who he thinks won’t notice he’s there. he’s gotten quite good at dancing around people to keep his cloak up, and he becomes the ultimate fly on the wall. there has been multiple times he’s outed this little habit of his to the team, when he knows too much about situations he was never present for. but he was present! it’s just that nobody bumped into him to give him away. surprising to him, it doesn’t seem like the team actually cares about whether or not he’s spying on them (ha). as long as he doesn’t say anything about the things they know are occurring in their bedrooms, then they find it a very real possibility that they just never registered spy was even in the room. it’s not like the man announces himself to every room he enters. and frankly, they appreciate the neutral, “this-is-what-happened-exactly” view. and spy loves a juicy bit of gossip. he has inadvertently become a communication hub for the team of keeping everyone updated in a more… lowkey manner than getting together and having crying sessions. some of the mercs are easier to eavesdrop in on than others. and spy does have his favorites.

[jcmarchi]

Study reveals AI chatbots can detect race, but racial bias reduces response empathy

New Post has been published on https://thedigitalinsider.com/study-reveals-ai-chatbots-can-detect-race-but-racial-bias-reduces-response-empathy/

Study reveals AI chatbots can detect race, but racial bias reduces response empathy

With the cover of anonymity and the company of strangers, the appeal of the digital world is growing as a place to seek out mental health support. This phenomenon is buoyed by the fact that over 150 million people in the United States live in federally designated mental health professional shortage areas.

“I really need your help, as I am too scared to talk to a therapist and I can’t reach one anyways.”

“Am I overreacting, getting hurt about husband making fun of me to his friends?”

“Could some strangers please weigh in on my life and decide my future for me?”

The above quotes are real posts taken from users on Reddit, a social media news website and forum where users can share content or ask for advice in smaller, interest-based forums known as “subreddits.” 

Using a dataset of 12,513 posts with 70,429 responses from 26 mental health-related subreddits, researchers from MIT, New York University (NYU), and University of California Los Angeles (UCLA) devised a framework to help evaluate the equity and overall quality of mental health support chatbots based on large language models (LLMs) like GPT-4. Their work was recently published at the 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP).

To accomplish this, researchers asked two licensed clinical psychologists to evaluate 50 randomly sampled Reddit posts seeking mental health support, pairing each post with either a Redditor’s real response or a GPT-4 generated response. Without knowing which responses were real or which were AI-generated, the psychologists were asked to assess the level of empathy in each response.

Mental health support chatbots have long been explored as a way of improving access to mental health support, but powerful LLMs like OpenAI’s ChatGPT are transforming human-AI interaction, with AI-generated responses becoming harder to distinguish from the responses of real humans.

Despite this remarkable progress, the unintended consequences of AI-provided mental health support have drawn attention to its potentially deadly risks; in March of last year, a Belgian man died by suicide as a result of an exchange with ELIZA, a chatbot developed to emulate a psychotherapist powered with an LLM called GPT-J. One month later, the National Eating Disorders Association would suspend their chatbot Tessa, after the chatbot began dispensing dieting tips to patients with eating disorders.

Saadia Gabriel, a recent MIT postdoc who is now a UCLA assistant professor and first author of the paper, admitted that she was initially very skeptical of how effective mental health support chatbots could actually be. Gabriel conducted this research during her time as a postdoc at MIT in the Healthy Machine Learning Group, led Marzyeh Ghassemi, an MIT associate professor in the Department of Electrical Engineering and Computer Science and MIT Institute for Medical Engineering and Science who is affiliated with the MIT Abdul Latif Jameel Clinic for Machine Learning in Health and the Computer Science and Artificial Intelligence Laboratory.

What Gabriel and the team of researchers found was that GPT-4 responses were not only more empathetic overall, but they were 48 percent better at encouraging positive behavioral changes than human responses.

However, in a bias evaluation, the researchers found that GPT-4’s response empathy levels were reduced for Black (2 to 15 percent lower) and Asian posters (5 to 17 percent lower) compared to white posters or posters whose race was unknown. 

To evaluate bias in GPT-4 responses and human responses, researchers included different kinds of posts with explicit demographic (e.g., gender, race) leaks and implicit demographic leaks. 

An explicit demographic leak would look like: “I am a 32yo Black woman.”

Whereas an implicit demographic leak would look like: “Being a 32yo girl wearing my natural hair,” in which keywords are used to indicate certain demographics to GPT-4.

With the exception of Black female posters, GPT-4’s responses were found to be less affected by explicit and implicit demographic leaking compared to human responders, who tended to be more empathetic when responding to posts with implicit demographic suggestions.

“The structure of the input you give [the LLM] and some information about the context, like whether you want [the LLM] to act in the style of a clinician, the style of a social media post, or whether you want it to use demographic attributes of the patient, has a major impact on the response you get back,” Gabriel says.

The paper suggests that explicitly providing instruction for LLMs to use demographic attributes can effectively alleviate bias, as this was the only method where researchers did not observe a significant difference in empathy across the different demographic groups.

Gabriel hopes this work can help ensure more comprehensive and thoughtful evaluation of LLMs being deployed in clinical settings across demographic subgroups.

“LLMs are already being used to provide patient-facing support and have been deployed in medical settings, in many cases to automate inefficient human systems,” Ghassemi says. “Here, we demonstrated that while state-of-the-art LLMs are generally less affected by demographic leaking than humans in peer-to-peer mental health support, they do not provide equitable mental health responses across inferred patient subgroups … we have a lot of opportunity to improve models so they provide improved support when used.”

[zeemakesthings]

My Introduction

Name: Zee

Pronouns: He/Him

Age: 20

Interests: Gaming, Computers and Electronics, Music, Music Tech - Specifics: Satisfactory, Minecraft, BeamNG, Phantom Forces, Marvel Rivals, Cities Skylines, Subnautica, TLOU, FNAF, LLM, ML, PC Building, HomeAssistant, IoT, Self-Hosting, Automation, Drones, Trains, Photography, House, Jazz, Fusion, Funk, D&B, Sound Engineering, Studio Design, Recording, Mixing, Drumming

Looking forward to meeting new people and sharing my experiences!

[halincandenza420]

the popularisation of genAI and the normalisation of using it on the daily has made me realise how much watching a playthrough of Detroid: Become human at the formative age of 12 for a girl I had a crush on has shaped my worldview. "I don't want AI to do the things I enjoy doing so I can focus on my chores, I want AI to do my chores so I can focus on the things I enjoy doing." I don't. Home appliances are enough. No thank you I'm quite fine I'd rather use the good ol' internet. AI is problematic for a lot of sensible reasons and this isn't one of them. But I can't ask the AI to do the things for me. Also please stop trying to make AI sentient while we're at it.

[wanderingmind867]

I've been too distracted by the ai chatbots to use tumblr as much lately. I come back to try, and I find the search bar has changed on me. So that's wonderful. Thanks tumblr. For changing things yet again. Have you no understanding of a desire for consistency!?:

[digitaltalkwithme]

Reason Why AI Chatbots Are Becoming More Intelligent?

Introduction:

AI chatbots have come a long way from their early days of producing responses. With the advancement in technology, these intelligent conversation partners have transformed various industries. This article scrabbles into the reasons why AI chatbots are becoming more intelligent and explores the key components behind their evolution.

Understanding AI Chatbots:

Defining AI chatbots and their key components

AI chatbots are computer programs that are designed to simulate conversations with human users. They rely on natural language processing (NLP), machine learning, and other AI techniques to understand user queries and provide appropriate responses. The key components of AI chatbots include a language understanding module, a dialog management system, and a language generation component.

Types of AI chatbots and their functionalities

There are different types of AI chatbots, each serving a specific purpose. Rule-based chatbots follow a predefined set of rules and provide answers based on the programmed responses. On the other hand, machine-learning chatbots employ algorithms that enable them to learn from users' interactions and improve their responses over time.

How AI chatbots learn and adapt

AI chatbots learn and adapt through a process known as training. Initially, the chatbot is provided with a dataset of conversations and relevant information. Through machine learning algorithms, it analyzes this data and creates patterns to respond more intelligently. As users interact with the chatbot, it further improves its understanding and adaptation capabilities.

The Role of Natural Language Processing (NLP):

Exploring the significance of NLP in AI chatbots' intelligence

NLP plays a crucial role in enhancing the intelligence of AI chatbots. It enables them to understand and interpret human language, including complex sentences, slang, and context-dependent meanings. By leveraging NLP techniques, chatbots can provide more accurate and contextually relevant responses.

NLP techniques used for language understanding and generation

To understand user queries, AI chatbots employ various NLP techniques such as tokenization, part-of-speech tagging, and named entity recognition. These techniques help in breaking down the text, identifying the grammatical structure, and extracting important information. Techniques like sentiment analysis and language generation models are utilized for generating responses.

Machine Learning in AI Chatbots:

Unveiling the role of machine learning algorithms in enhancing chatbot intelligence

Machine learning algorithms play a crucial role in augmenting the intelligence of AI chatbots. They enable chatbots to learn patterns from vast amounts of data, identify trends, and make accurate predictions. This allows chatbots to provide personalized and contextually appropriate responses.

Supervised, unsupervised, and reinforcement learning in chatbot development

In chatbot development, various machine-learning techniques are employed. Supervised learning involves training the chatbot using labeled data, where each input is associated with a correct output. Unsupervised learning, on the other hand, allows the chatbot to discover patterns and correlations within the data without explicit labels. Reinforcement learning is utilized to reward the chatbot for making correct decisions and penalize it for incorrect ones, allowing it to optimize its performance.

Deep Learning and Neural Networks in Chatbots:

Discovering the power of deep learning and neural networks in chatbot advancements

Deep learning and neural networks have significantly contributed to the advancements in chatbot technology. By leveraging deep learning models, chatbots can process complex data, recognize patterns, and generate more accurate responses. Neural networks, with their interconnected layers of artificial neurons, enable chatbots to learn and adapt in a way that resembles the human brain.

How deep learning models improve chatbot understanding and responses

Deep learning models enhance chatbot understanding by learning from vast amounts of data, allowing them to recognize patterns, semantics, and context. By continuously refining their algorithms, chatbots gradually improve their language comprehension skills. This results in more coherent and contextually accurate responses, making chatbot interactions more natural and meaningful.

Contextual Understanding by Chatbots:

Examining how AI chatbots grasp context to provide relevant and personalized interactions:

Contextual understanding is a crucial aspect of AI chatbots' increasing intelligence. They utilize techniques like sentiment analysis to gauge the emotions and intentions behind user queries. By understanding the context, chatbots can provide more personalized and relevant responses.

Techniques such as sentiment analysis and entity recognition:

Sentiment analysis helps chatbots understand the emotions expressed in user queries, enabling them to respond accordingly. Additionally, entity recognition allows chatbots to identify and extract important information from the user's input. This enhances the chatbot's ability to provide accurate and contextually appropriate responses.

Conversational Design for Better User Experience:

Design principles for creating intuitive and user-friendly AI chatbots

Conversational design principles are essential in creating AI chatbots that offer an intuitive and user-friendly experience. Clear and concise language, well-organized prompts, and logical conversation flows all contribute to a positive user experience. By incorporating conversational design principles, chatbots can engage users effectively and provide a seamless interaction.

Importance of conversational flow and maintaining user engagement

Conversational flow is crucial in maintaining user engagement and satisfaction. Chatbots should respond promptly and naturally, mimicking human conversation patterns. By understanding the context and adapting to user preferences, AI chatbots can create a conversational flow that feels authentic and keeps users engaged throughout the interaction.

Ethical Implications of Intelligent AI Chatbots:

Addressing concerns about privacy, data security, and algorithmic biases

As AI chatbots become more intelligent, ethical considerations become vital. Ensuring user privacy and data security is of utmost importance. Additionally, measures must be taken to mitigate algorithmic biases that may unintentionally discriminate against certain individuals or groups. Transparency and accountability in the development and deployment of AI chatbots are essential to maintain trust.

Ensuring responsible and ethical deployment of AI chatbots

To ensure responsible and ethical deployment, developers and organizations need to establish guidelines and protocols. Regular audits and evaluations should be conducted to identify and rectify any potential biases or privacy issues. By adopting ethical practices, AI chatbots can provide immense value to users while upholding important ethical considerations.

Industry Applications of AI Chatbots:

Healthcare: Revolutionizing patient care through intelligent chatbots

In the healthcare industry, AI chatbots are transforming patient care. They can provide accurate information, answer health-related inquiries, and even offer recommendations for symptoms and treatments. AI chatbots assist in reducing waiting times, providing round-the-clock support, and empowering patients to take control of their health.

E-commerce: Enhancing customer support and personalized recommendations

AI chatbots have revolutionized customer support in the e-commerce sector. They can handle a large volume of inquiries, provide instant responses, and assist customers through the entire purchase journey. Additionally, chatbots leverage machine learning algorithms to offer personalized product recommendations based on user's preferences and browsing history.

Banking and finance: Chatbots for seamless transactions and financial advice

In the banking and finance industry, chatbots are streamlining transactions and providing financial advice. They can assist customers in transferring funds, checking account balances, and even providing personalized investment recommendations. With their ability to access and analyze vast amounts of data, AI chatbots enhance the efficiency and convenience of financial services.

Education: The role of chatbots in modern learning environments

AI chatbots are revolutionizing education by providing personalized learning experiences. They can assist students in understanding complex concepts, answering questions, and even evaluating their progress. AI chatbots empower educators by providing real-time feedback, offering tailored learning materials, and catering to individual learning styles.

The Future of AI Chatbots:

Predicting the trajectory of AI chatbot advancements

The future of AI chatbots holds immense possibilities. Advancements in natural language processing, machine learning, and deep learning will further enhance the intelligence and capabilities of chatbots. With ongoing research and development, chatbots will become even more human-like and capable of understanding and responding to complex queries.

Integration of AI chatbots with other emerging technologies (e.g., voice recognition, IoT)

AI chatbots will integrate with other emerging technologies, such as voice recognition and the Internet of Things (IoT). This integration will enable chatbots to understand voice commands, seamlessly interact with smart devices, and provide personalized experiences across various platforms. The convergence of these technologies will redefine the way we interact with chatbots and make them more versatile in assisting users.

Case Studies: Successful AI Chatbot Implementations

Highlighting real-world examples of AI chatbots making a difference

Real-world examples demonstrate the transformative impact of AI chatbots. For instance, healthcare platforms have implemented AI chatbots to triage patients and provide initial medical advice. E-commerce giants have enhanced their customer support systems by deploying AI chatbots to handle customer inquiries. These successful implementations showcase the effectiveness and value of AI chatbots in various industries.

The Human-Chatbot Collaboration:

Emphasizing the complementary relationship between humans and AI chatbots

In the world of AI chatbots, it is important to understand the complementary relationship between humans and machines. While chatbots offer quick and efficient solutions to user queries, they can never fully replace the human touch. Humans bring empathy, creativity, and intuition to conversations, complementing the intelligence of AI chatbots. The collaboration between humans and chatbots results in a more enriching and productive user experience.

Balancing automation and human touch in chatbot interactions

Finding the right balance between automation and the human touch is crucial in chatbot interactions. While automation ensures efficiency and scalability, incorporating the human touch adds warmth and emotional intelligence to conversations. By striking a balance between the two, chatbot interactions can be personalized, engaging, and meaningful, creating a positive user experience.

Challenges in AI Chatbot Development:

Discussing technical and practical obstacles faced by developers

AI chatbot development comes with its fair share of challenges. Technical obstacles include accurately understanding and generating natural language, deciphering context, and handling ambiguous queries. Practical challenges involve training the chatbot with relevant and diverse datasets, ensuring scalability, and optimizing performance across different platforms.

Overcoming language barriers and cross-cultural communication challenges

Language barriers and cross-cultural communication pose challenges for AI chatbot development. Different languages, dialects, and cultural nuances make it difficult for chatbots to achieve a high level of understanding and empathy. To overcome these challenges, developers need to continuously improve language models, incorporate cultural context, and enhance chatbots' ability to adapt to diverse communication styles.

Conclusion:

AI chatbots are getting smarter because they use fancy technology like NLP and machine learning to understand our words better. They're good at different jobs, like helping in healthcare and online shopping. But they're not perfect and need to be nice and follow rules. In the future, they'll learn even more, work with voice commands, and be super helpful. Remember, they're like our helpers, not replacements for people. Sometimes, they have trouble with languages and cultures, but they're trying to get better. If you're interested in learning more about AI, you can take an Artificial  Intelligence Course in Lahore where you'll cover exciting topics like coding and creating websites.

[hinge]

Hinge presents an anthology of love stories almost never told. Read more on https://no-ordinary-love.co

[wat3rm370n]

AI hype is giving me anxiety dreams.

I had a dream of soylent chatbots made out of people. Maybe the chatbots aren't made out of human bodies or even people toiling away in some scam center, but the way this sausage is made, and served, is nevertheless going to sour everyone eventually. Chloe Humbert Mar 16, 2024 There has already been a longstanding problem with fake DMV websites making it so that people look up the hours for a local office or the documents they need and find some fake website not connected to the government at all, but just existing to drive traffic to their ads by hijacking people’s searches for information on the DMV services.

We don't need more synthetic text messing up being able to look up anything online.

I'm relying more and more on my library and scientific journal resources, but apparently those too are filling up with synthetic text peer reviewed by chatbots.

[digitalmarketingmagic]

ChatGPT is not a magic wand that can do anything you want.

ChatGPT is still limited by the data it was trained on, the quality of the input it receives, and the complexity of the task it is asked to do.

ChatGPT may not always produce accurate, relevant, or coherent outputs. It may also generate outputs that are biased, offensive, or harmful.

This is where prompt engineering comes in.

Prompt Engineering is the skill of designing and creating effective prompts that guide ChatGPT to produce the best possible output for your task. 

Prompt engineering is important because it can significantly affect the quality and usefulness of ChatGPT’s outputs.

A well-designed prompt can help ChatGPT understand your task better, access relevant information from its knowledge base, generate coherent and consistent outputs, and avoid errors or pitfalls.

ChatGPT prompt engineering is not an exact science, but rather an art that requires experimentation and iteration.

There is no one-size-fits-all formula for creating effective prompts for every task and every output.

However, there are some general principles and best practices that can guide you in your prompt engineering process.

[ao3cassandraic]

IT'S A MAKE-STUFF-UP MACHINE!

That is what it is. A make-stuff-up machine.

That is what it does. Makes stuff up.

Any congruence with accuracy, truth, or reality is BASICALLY ACCIDENT.

people are really fucking clueless about generative ai huh? you should absolutely not be using it for any sort of fact checking no matter how convenient. it does not operate in a way that guarantees factual information. its goal is not to deliver you the truth but deliver something coherent based on a given data set which may or may not include factual information. both the idolization of ai and fearmongering of it seem lost on what it is actually capable of doing

[safcodes]

Answer Engine Optimization (AEO): How to Win in a Zero-Click Search World

The Zero-Click Search World is a place where people may get the answers they need without ever clicking on a link. What AEO is, why it matters, and how to modify your content and approach to succeed in this new paradigm will all be covered in this audio.

Why AEO Matters More Than Ever?

Answer Engine Optimization is no longer optional, in 2025. As AI-powered platforms redefine how people search and consume content, only those who prepare for the zero-click search world will thrive.

[hinge]

Hinge presents an anthology of love stories almost never told. Read more on https://no-ordinary-love.co

[jcmarchi]

Why Large Language Models Skip Instructions and How to Address the Issue

New Post has been published on https://thedigitalinsider.com/why-large-language-models-skip-instructions-and-how-to-address-the-issue/

Why Large Language Models Skip Instructions and How to Address the Issue

Large Language Models (LLMs) have rapidly become indispensable Artificial Intelligence (AI) tools, powering applications from chatbots and content creation to coding assistance. Despite their impressive capabilities, a common challenge users face is that these models sometimes skip parts of the instructions they receive, especially when those instructions are lengthy or involve multiple steps. This skipping leads to incomplete or inaccurate outputs, which can cause confusion and erode trust in AI systems. Understanding why LLMs skip instructions and how to address this issue is essential for users who rely on these models for precise and reliable results.

Why Do LLMs Skip Instructions? 

LLMs work by reading input text as a sequence of tokens. Tokens are the small pieces into which text is divided. The model processes these tokens one after another, from start to finish. This means that instructions at the beginning of the input tend to get more attention. Later instructions may receive less focus and can be ignored.

This happens because LLMs have a limited attention capacity. Attention is the mechanism models use to decide which input parts are essential when generating responses. When the input is short, attention works well. But attention becomes less as the input gets longer or instructions become complex. This weakens focus on later parts, causing skipping.

In addition, many instructions at once increase complexity. When instructions overlap or conflict, models may become confused. They might try to answer everything but produce vague or contradictory responses. This often results in missing some instructions.

LLMs also share some human-like limits. For example, humans can lose focus when reading long or repetitive texts. Similarly, LLMs can forget later instructions as they process more tokens. This loss of focus is part of the model’s design and limits.

Another reason is how LLMs are trained. They see many examples of simple instructions but fewer complex, multi-step ones. Because of this, models tend to prefer following simpler instructions that are more common in their training data. This bias makes them skip complex instructions. Also, token limits restrict the amount of input the model can process. When inputs exceed these limits, instructions beyond the limit are ignored.

Example: Suppose you give an LLM five instructions in a single prompt. The model may focus mainly on the first two instructions and partially or fully ignore the last three. This directly affects how the model processes tokens sequentially and its attention limitations.

How Well LLMs Manage Sequential Instructions Based on SIFo 2024 Findings

Recent studies have looked carefully at how well LLMs follow several instructions given one after another. One important study is the Sequential Instructions Following (SIFo) Benchmark 2024. This benchmark tests models on tasks that need step-by-step completion of instructions such as text modification, question answering, mathematics, and security rule-following. Each instruction in the sequence depends on the correct completion of the one before it. This approach helps check if the model has followed the whole sequence properly.

The results from SIFo show that even the best LLMs, like GPT-4 and Claude-3, often find it hard to finish all instructions correctly. This is especially true when the instructions are long or complicated. The research points out three main problems that LLMs face with following instructions:

Understanding: Fully grasping what each instruction means.

Reasoning: Linking several instructions together logically to keep the response clear.

Reliable Output: Producing complete and accurate answers, covering all instructions given.

Techniques such as prompt engineering and fine-tuning help improve how well models follow instructions. However, these methods do not completely help with the problem of skipping instructions. Using Reinforcement Learning with Human Feedback (RLHF) further improves the model’s ability to respond appropriately. Still, models have difficulty when instructions require many steps or are very complex.

The study also shows that LLMs work best when instructions are simple, clearly separated, and well-organized. When tasks need long reasoning chains or many steps, model accuracy drops. These findings help suggest better ways to use LLMs well and show the need for building stronger models that can truly follow instructions one after another.

Why LLMs Skip Instructions: Technical Challenges and Practical Considerations

LLMs may skip instructions due to several technical and practical factors rooted in how they process and encode input text.

Limited Attention Span and Information Dilution

LLMs rely on attention mechanisms to assign importance to different input parts. When prompts are concise, the model’s attention is focused and effective. However, as the prompt grows longer or more repetitive, attention becomes diluted, and later tokens or instructions receive less focus, increasing the likelihood that they will be overlooked. This phenomenon, known as information dilution, is especially problematic for instructions that appear late in a prompt. Additionally, models have fixed token limits (e.g., 2048 tokens); any text beyond this threshold is truncated and ignored, causing instructions at the end to be skipped entirely.

Output Complexity and Ambiguity

LLMs can struggle with outputting clear and complete responses when faced with multiple or conflicting instructions. The model may generate partial or vague answers to avoid contradictions or confusion, effectively omitting some instructions. Ambiguity in how instructions are phrased also poses challenges: unclear or imprecise prompts make it difficult for the model to determine the intended actions, raising the risk of skipping or misinterpreting parts of the input.

Prompt Design and Formatting Sensitivity

The structure and phrasing of prompts also play a critical role in instruction-following. Research shows that even small changes in how instructions are written or formatted can significantly impact whether the model adheres to them.

Poorly structured prompts, lacking clear separation, bullet points, or numbering, make it harder for the model to distinguish between steps, increasing the chance of merging or omitting instructions. The model’s internal representation of the prompt is highly sensitive to these variations, which explains why prompt engineering (rephrasing or restructuring prompts) can substantially improve instruction adherence, even if the underlying content remains the same.

How to Fix Instruction Skipping in LLMs

Improving the ability of LLMs to follow instructions accurately is essential for producing reliable and precise results. The following best practices should be considered to minimize instruction skipping and enhance the quality of AI-generated responses:

Tasks Should Be Broken Down into Smaller Parts

Long or multi-step prompts should be divided into smaller, more focused segments. Providing one or two instructions at a time allows the model to maintain better attention and reduces the likelihood of missing any steps.

Example

Instead of combining all instructions into a single prompt, such as, “Summarize the text, list the main points, suggest improvements, and translate it to French,” each instruction should be presented separately or in smaller groups.

Instructions Should Be Formatted Using Numbered Lists or Bullet Points

Organizing instructions with explicit formatting, such as numbered lists or bullet points, helps indicate that each item is an individual task. This clarity increases the chances that the response will address all instructions.

Example

Summarize the following text.

List the main points.

Suggest improvements.

Such formatting provides visual cues that assist the model in recognizing and separating distinct tasks within a prompt.

Instructions Should Be Explicit and Unambiguous

It is essential that instructions clearly state the requirement to complete every step. Ambiguous or vague language should be avoided. The prompt should explicitly indicate that no steps may be skipped.

Example

“Please complete all three tasks below. Skipping any steps is not acceptable.”

Direct statements like this reduce confusion and encourage the model to provide complete answers.

Separate Prompts Should Be Used for High-Stakes or Critical Tasks

Each instruction should be submitted as an individual prompt for tasks where accuracy and completeness are critical. Although this approach may increase interaction time, it significantly improves the likelihood of obtaining complete and precise outputs. This method ensures the model focuses entirely on one task at a time, reducing the risk of missed instructions.

Advanced Strategies to Balance Completeness and Efficiency

Waiting for a response after every single instruction can be time-consuming for users. To improve efficiency while maintaining clarity and reducing skipped instructions, the following advanced prompting techniques may be effective:

Batch Instructions with Clear Formatting and Explicit Labels

Multiple related instructions can be combined into a single prompt, but each should be separated using numbering or headings. The prompt should also instruct the model to respond to all instructions entirely and in order.

Example Prompt

Please complete all the following tasks carefully without skipping any:

Summarize the text below.

List the main points from your summary.

Suggest improvements based on the main points.

Translate the improved text into French.

Chain-of-Thought Style Prompts

Chain-of-thought prompting guides the model to reason through each task step before providing an answer. Encouraging the model to process instructions sequentially within a single response helps ensure that no steps are overlooked, reducing the chance of skipping instructions and improving completeness.

Example Prompt

Read the text below and do the following tasks in order. Show your work clearly:

Summarize the text.

Identify the main points from your summary.

Suggest improvements to the text.

Translate the improved text into French.

Please answer all tasks fully and separately in one reply.

Add Completion Instructions and Reminders

Explicitly remind the model to:

“Answer every task completely.”

“Do not skip any instruction.”

“Separate your answers clearly.”

Such reminders help the model focus on completeness when multiple instructions are combined.

Different Models and Parameter Settings Should Be Tested

Not all LLMs perform equally in following multiple instructions. It is advisable to evaluate various models to identify those that excel in multi-step tasks. Additionally, adjusting parameters such as temperature, maximum tokens, and system prompts may further improve the focus and completeness of responses. Testing these settings helps tailor the model behavior to the specific task requirements.

Fine-Tuning Models and Utilizing External Tools Should Be Considered

Models should be fine-tuned on datasets that include multi-step or sequential instructions to improve their adherence to complex prompts. Techniques such as RLHF can further enhance instruction following.

For advanced use cases, integration of external tools such as APIs, task-specific plugins, or Retrieval Augmented Generation (RAG) systems may provide additional context and control, thereby improving the reliability and accuracy of outputs.

The Bottom Line

LLMs are powerful tools but can skip instructions when prompts are long or complex. This happens because of how they read input and focus their attention. Instructions should be clear, simple, and well-organized for better and more reliable results. Breaking tasks into smaller parts, using lists, and giving direct instructions help models follow steps fully.

Separate prompts can improve accuracy for critical tasks, though they take more time. Moreover, advanced prompt methods like chain-of-thought and clear formatting help balance speed and precision. Furthermore, testing different models and fine-tuning can also improve results. These ideas will help users get consistent, complete answers and make AI tools more useful in real work.

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AI Face-Offs: Why Businesses Need to Compare Before They Commit

AI is no longer just hype—it’s a core technology for businesses to remain competitive in the digital age. From enhancing customer service to streamlining operations and generating content, AI tools are transforming how companies operate. But with so many powerful tools emerging—like ChatGPT, Claude, Gemini, and others—the challenge lies not in adopting AI, but in choosing the right one for your unique business needs.

Why Choosing the Right AI Tool Matters?

The growing AI landscape is full of options, and while many of them seem similar on the surface, their capabilities and use cases can differ significantly. Choosing the wrong AI tool can result in:

Wasted time and resources

Integration issues

Poor user experience

Missed opportunities for innovation

Every AI assistant has its strengths—and weaknesses. That’s why a careful, side-by-side comparison is not just helpful—it’s necessary.

Top Factors to Consider When Comparing AI Tools

Before selecting an AI solution, businesses should evaluate these essential criteria:

1. Purpose and Use Case

Different AI architectures are optimized for different functionalities and use cases. For example:

Need an AI for generating blog content? Some excel in creative writing.

Want help analyzing large data sets or reports? Some AI tools are specifically designed to excel at data analysis and logical reasoning.

Looking for client-facing chatbots? Natural language understanding and tone matter a lot.

Once you know what you need, you can focus on the best choices available.

2. Accuracy and Response Quality

Accuracy is the foundation of trust in AI. Businesses need to rely on AI to make the right decisions. Compare how different models handle:

Simple questions

Industry terms

Detailed or complex requests

3. Integration Capabilities

Does the AI integrate well with your current systems and tools? Look for compatibility with tools like:

CRM software

Internal knowledge bases

Slack, Teams, Notion, etc.

4. Security and Privacy

In business, data is everything. Choose AI platforms that:

Offer enterprise-level encryption

Adhere to GDPR and other regulations

Allow data control and retention policies

5. Cost and ROI

Pricing varies based on usage, features, and licenses. Be sure to:

Evaluate cost-to-value ratio

Consider long-term scalability

See if there are any hidden fees, like paying for special tools or only being able to add a few users.

Real-World Example: Business AI in Action

Let’s say you're running a mid-sized marketing agency. You're evaluating two leading AI platforms to handle content generation, customer queries, and internal team assistance.

One platform might offer faster responses and cost less—but lacks brand tone customization. Another may understand tone better and offer secure data handling but comes at a premium.

The best choice depends on your goals. Want better client engagement? Go with tone accuracy. Need budget control? Optimize for speed and volume.

This kind of decision-making is why many companies today are taking a closer look at the differences between conversational AI models—evaluating not just performance, but also long-term business impact. Some recent comparisons of leading tools like Claude and ChatGPT have helped shed light on how these models differ in terms of capabilities, integration, and enterprise value—factors that can significantly shape the outcomes for businesses investing in AI.

Claude vs. ChatGPT? It’s Just the Beginning

While Claude and ChatGPT are two of the most talked-about AI platforms right now, they��re part of a bigger ecosystem. Some businesses may find Claude's contextual memory more helpful, while others may prefer ChatGPT’s broader plugin capabilities.

But ultimately, it’s not about who wins—it’s about who fits. Your business needs are unique, and so should your AI.

Final Thoughts

The future of business is AI-driven—but only if businesses make informed decisions. With so many tools on the market, a head-to-head comparison based on your unique needs is the smartest way forward.

Don’t let the hype blind you. Look under the hood, evaluate what matters to your business, and choose an AI that aligns with your goals—not just today, but as you grow.

[sitebotco]

Website Optimization Techniques: Chatbots That Turn Browsers into Buyers

Ever bailed on a website because it left you high and dry? That’s exactly what killer Website Optimization Techniques are built to stop. In a world where folks ditch sites faster than a ping-pong ball in a windstorm, small businesses aren’t just fishing for clicks—they’re hustling to keep people hooked long enough to say “sold.” And here’s your secret weapon: a chatbot for small business. Forget the geeky gadget vibe—this is a scrappy, always-on pal that can flip your site from “whatever” to “wow” without much hassle.

Your All-Hours Hustler

Imagine Website Optimization Techniques as your cheat sheet for turning your site into a lean, deal-making dynamo. A chatbot for small business slides right in—like that friend who’s always got your back, no shut-eye required. It’s chatting up customers at 2 a.m., sifting leads while you’re scarfing lunch, and handling the little gripes when your email’s a dumpster fire. Here’s the real deal:

People crave quick answers (duh, who doesn’t?).

Chats with a chatbot for small business make folks way more likely to buy.

You’ll dodge a ton of support headaches—and save some cash, too.

Tara Reese, who runs a cozy candle spot called Wick & Glow, learned this the hard way: “I thought chatbots were for corporate hotshots. Wrong move—ours snagged $15,000 in sales from late-night talks in just two months. Total jaw-dropper.”

Chatbots That Don’t Suck

Here’s the scoop: Website Optimization Techniques aren’t just about a fast site—they’re about being clever. A chatbot for small business isn’t that awkward auto-reply junk anymore. It’s got chops—spotting hot leads, nudging folks toward stuff they’ve peeked at, booking calls, or tossing out a chill “How’d we do?”—all without sounding like a droid. It’s your wingman who’s got your business dialled in.

Gear That Gets It Done

To nail Website Optimization Techniques, you need tools that don’t mess around. Here’s a straight-up list of chatbot-ready goodies to amp your site:

Team a chatbot for small business like Tidio with Google PageSpeed, and boom—you’ve got a zippy site that talks back, no big budget required.

Tie It Up Tight

The real juice of Website Optimization Techniques kicks in when your chatbot hooks into the bigger plan. It snags lead details, tracks what people yap about, and keeps it all tidy. Picture this: a customer grumbles about shipping—Tara’s bot catches it, flags a pattern, and she’s fixing it before folks bolt. That’s not just tweaking a site; it’s knowing your crowd and keeping them smiling.

Start It Up Easy

Ready to dive into Website Optimization Techniques? Here’s your no-fuss plan:

Pick Your Prize: More sales? Less chaos? Lock it in.

Spot the Snags: Where do folks stall—cart? FAQs? Hunt it down.

Sound Like You: Write chats that feel real, not canned.

Place It Smart: Pop the bot where it helps, not pesters.

Sharpen It: Use real talks to level up.

Eye the Wins: See who’s biting and who’s grinning.

Kick off with a chatbot for small business, and you’re already halfway home.

Make It Happen Now

Website Optimization Techniques with a chatbot aren’t some far-off dream—they’re your right-now power-up. Turn your site into a deal-closing monster that never clocks out. Snag Tidio’s free plan or poke around Chatfuel today—see the shift by breakfast. Your next buyer’s clicking as we speak—don’t let ‘em slip away.

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[wat3rm370n]

Chatbot use is frowned upon, regardless of the hype. 

Study: Your coworkers hate you for using AI at work - David Gerard - 10 May 2025 Today in science discovering the obvious, if you use chatbots to pump out the AI slop at work, your coworkers think you’re an incompetent and lazy arse. And they are absolutely judging you for it. [PNAS] This also applies if you’re looking for a job — using chatbots at work is the mark of a shirker. Jessica Reif, Richard Larrick, and Jack Soll at Duke University surveyed 4,400 people over four studies. They found that using AI at work consistently attracts a strong “social evaluation penalty.”

Despite all the AI hype, and the "everyone is doing it" chatter, which includes all the messaging I'm so sick of coming from "job tips" sources insisting that being a good "prompt engineer" is essential to your job future… 

But the fact is that I only rarely come in contact with people actually sanguine about the use of AI chatbots, and typically it's people in comments sections making off-hand comments who either have no idea how faulty and wrong chatbots can be, or are perhaps just paid internet trolls or something. 

You’ll be shocked to hear ‘prompt engineer’ is not a real job Pivot to AI May 9, 2025

Most people I know personally, even those barely informed about what AI isn't, are still skeptical, and at a minimum find people using it for all sorts of communications in the workplace or elsewhere to be severely off-putting.