#Build AI Chatbots

[aichatbot08]

NeuroBot: The AI of Tomorrow

A next-gen AI chatbot designed to revolutionize communication with unparalleled intelligence and efficiency. Always here for a chat, a question, or a helping hand – making your day smarter and easier. Experience hyper-intelligent dialogues with the best AI chatbot engineered for peak performance.

[aidevelop]

AI Chatbot Development Services: Enhancing Customer Experience Like Never Before

Leverage the power of AI Chatbot Development that provide 24/7 support, personalized responses, and seamless user experiences for your business.

[anti-cyra]

New Series: Halen Academy

Welcome to Halen Academy, a magic school where only the best and brightest from all walks of life are allowed to enter these hallowed halls. The academy strictly prohibits any form of discrimination — giving anybody whether human, vampire, demon, beastkin and otherwise a chance to learn and grow. However, humans haven’t been admitted into the academy for centuries… until you came along. Nevertheless, your professors are dedicated to providing you quality education and upholding the academy’s tradition of allowing accessible magic for all. 

Be fairly warned, human. Your peers may not all agree; some may accept you, some may find a way to cast you out. After all, the darkest mage of all time was a human who took the knowledge of magic and twisted it for his own greed, staining Erachar’s history. At the end of the day, it is up to you to prove them wrong… or right.

· · ─ ·𖥸· ─ · ·· · ─ ·𖥸· ─ · ·· · ─ ·𖥸· ─ · ·· · ─ ·𖥸· ─ · ·· · ─ ·𖥸· ─ · ·

Released characters: Halen Academy faculty members

⊱ ۫ ׅ ✧ 2nd POV Singular User (one character across multiple bots) ⊱ ۫ ׅ ✧ Gender neutral human user x he/him bots. ⊱ ۫ ׅ ✧ Bots from this series will have "HA" as a label along with HALEN ACADEMY in the tagline. ⊱ ۫ ׅ ✧ Deals with themes of magic, school life, and various species such as vampires, beastkin, demons, etc. ⊱ ۫ ׅ ✧ On-going series with more to be added along the way! ⊱ ۫ ׅ ✧ Bots may know of each other vaguely, but plots are generally unrelated to each other unless mentioned otherwise.

For a detailed overview and lorebook, please click here. Characters will also be linked and updated in the doc.

[nerdy-hyperfixations]

It is genuinely a weird feeling to have been tracking AI development since 2014 and seeing all the people just find out about it in the past 4 years because the same people that will call you a horrible degenerate for going anywhere near AI are literally the same people who praise Tiktok for its algorithm and they don't even know. They don't even *know* what the algorithm is.

[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.

[full-stackmobiledeveloper]

Elevate Your Mobile App with AI & Chatbots Build Your AI-Powered App: Unlock Next-Gen Capabilities Master the integration of AI and chatbots with our 2025 guide, designed to help you create next-gen mobile applications boasting unmatched intelligence. Ready to elevate? This comprehensive guide equips you with the knowledge to seamlessly integrate AI chatbots and advanced AI into your mobile app for a truly intelligent and future-ready solution.

[technologyequality]

Train Your First AI Chatbot in 6 Steps: Delegate Tasks, Reclaim Your Time

Train Your First AI Chatbot in 6 Steps Delegate Tasks, Reclaim Your Time Let AI Handle the Busywork So You Can Lead the Big Vision! No seriously, if you’re still stuck answering the same 5 questions in your inbox, or manually booking calls at 11 PM (even though you swore you’d set boundaries)… this is your sign to stop. Because automation isn’t just about efficiency, it’s about leadership. And…

[theaiwordsmith]

How to Train AI Assistant?

Genieus AI makes it easy to train your own personal AI assistant with a simple, no-code approach. Upload FAQs, documents or website links to build a smart AI that understands your business.

The platform continuously learns and improves, ensuring accurate and relevant responses. Easily integrate it into your website or messaging platforms for seamless interactions.

Need updates? Modify the AI’s knowledge base anytime to keep it aligned with your needs. Whether for customer support or business automation, Genieus AI helps you train an AI assistant that enhances engagement and efficiency — without technical expertise. Start building your AI today!

To know more, visit - https://genieusai.com/

[rajni875]

How to Build an AI Chatbot?

Learn how to build an AI chatbot with machine learning, NLP, and automation tools. From defining objectives to selecting frameworks like Dialog flow or Rasa, this guide covers essential steps, including training data, deployment, and optimization, to create a smart, interactive chatbot for seamless user engagement.

[aichatbot08]

AI Chatbot Development 101: From Concept to Deployment

Learn the fundamentals of AI chatbot development, including natural language processing, chatbot frameworks, and deployment strategies.

[olivergisttv]

How to Create Custom AI Assistants Without Coding

In the age of automation, having your own AI assistant can drastically improve productivity, streamline tasks, and enhance customer service. The best part? You don’t need to be a programmer to create one. With no-code platforms, creating custom AI assistants is easier than ever. Here’s a step-by-step guide on how to build your very own AI assistant without writing a single line of code. 1. Choose…

[ninjatech1]

The landscape of AI chatbots is evolving faster than ever, becoming indispensable tools for businesses looking to enhance customer engagement and streamline operations. In this ultimate guide, we will walk you through a nine-step process on how to build an AI chatbot that blends cutting-edge technology with user-centered design.

Whether you are an early-stage startup or at an enterprise level, this guide will equip you with the knowledge and skills to create a chatbot that not only meets expectations but exceeds them.

[jcmarchi]

The OpenAI Files: Ex-staff claim profit greed betraying AI safety

New Post has been published on https://thedigitalinsider.com/the-openai-files-ex-staff-claim-profit-greed-betraying-ai-safety/

The OpenAI Files: Ex-staff claim profit greed betraying AI safety

‘The OpenAI Files’ report, assembling voices of concerned ex-staff, claims the world’s most prominent AI lab is betraying safety for profit. What began as a noble quest to ensure AI would serve all of humanity is now teetering on the edge of becoming just another corporate giant, chasing immense profits while leaving safety and ethics in the dust.

At the core of it all is a plan to tear up the original rulebook. When OpenAI started, it made a crucial promise: it put a cap on how much money investors could make. It was a legal guarantee that if they succeeded in creating world-changing AI, the vast benefits would flow to humanity, not just a handful of billionaires. Now, that promise is on the verge of being erased, apparently to satisfy investors who want unlimited returns.

For the people who built OpenAI, this pivot away from AI safety feels like a profound betrayal. “The non-profit mission was a promise to do the right thing when the stakes got high,” says former staff member Carroll Wainwright. “Now that the stakes are high, the non-profit structure is being abandoned, which means the promise was ultimately empty.” 

Deepening crisis of trust

Many of these deeply worried voices point to one person: CEO Sam Altman. The concerns are not new. Reports suggest that even at his previous companies, senior colleagues tried to have him removed for what they called “deceptive and chaotic” behaviour.

That same feeling of mistrust followed him to OpenAI. The company’s own co-founder, Ilya Sutskever, who worked alongside Altman for years, and since launched his own startup, came to a chilling conclusion: “I don’t think Sam is the guy who should have the finger on the button for AGI.” He felt Altman was dishonest and created chaos, a terrifying combination for someone potentially in charge of our collective future.

Mira Murati, the former CTO, felt just as uneasy. “I don’t feel comfortable about Sam leading us to AGI,” she said. She described a toxic pattern where Altman would tell people what they wanted to hear and then undermine them if they got in his way. It suggests manipulation that former OpenAI board member Tasha McCauley says “should be unacceptable” when the AI safety stakes are this high.

This crisis of trust has had real-world consequences. Insiders say the culture at OpenAI has shifted, with the crucial work of AI safety taking a backseat to releasing “shiny products”. Jan Leike, who led the team responsible for long-term safety, said they were “sailing against the wind,” struggling to get the resources they needed to do their vital research.

Another former employee, William Saunders, even gave a terrifying testimony to the US Senate, revealing that for long periods, security was so weak that hundreds of engineers could have stolen the company’s most advanced AI, including GPT-4.

Desperate plea to prioritise AI safety at OpenAI

But those who’ve left aren’t just walking away. They’ve laid out a roadmap to pull OpenAI back from the brink, a last-ditch effort to save the original mission.

They’re calling for the company’s nonprofit heart to be given real power again, with an iron-clad veto over safety decisions. They’re demanding clear, honest leadership, which includes a new and thorough investigation into the conduct of Sam Altman.

They want real, independent oversight, so OpenAI can’t just mark its own homework on AI safety. And they are pleading for a culture where people can speak up about their concerns without fearing for their jobs or savings—a place with real protection for whistleblowers.

Finally, they are insisting that OpenAI stick to its original financial promise: the profit caps must stay. The goal must be public benefit, not unlimited private wealth.

This isn’t just about the internal drama at a Silicon Valley company. OpenAI is building a technology that could reshape our world in ways we can barely imagine. The question its former employees are forcing us all to ask is a simple but profound one: who do we trust to build our future?

As former board member Helen Toner warned from her own experience, “internal guardrails are fragile when money is on the line”.

Right now, the people who know OpenAI best are telling us those safety guardrails have all but broken.

See also: AI adoption matures but deployment hurdles remain

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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[techavtar]

Tech Avtar is renowned for delivering custom software solutions for the healthcare industry and beyond. Our diverse range of AI Products and Software caters to clients in the USA, Canada, France, the UK, Australia, and the UAE. For a quick consultation, visit our website or call us at +91-92341-29799.

[innovaticsblog]

A Chatbot Analytics Specialist is a data-driven professional who delves into the world of chatbot conversations to extract valuable insights.

[rajaniesh]

Empowering Your Business with AI: Building a Dynamic Q&A Copilot in Azure AI Studio

In the rapidly evolving landscape of artificial intelligence and machine learning, developers and enterprises are continually seeking platforms that not only simplify the creation of AI applications but also ensure these applications are robust, secure, and scalable. Enter Azure AI Studio, Microsoft’s latest foray into the generative AI space, designed to empower developers to harness the full…