Tips for Improving Robot Language Understanding

Researchers from Salesforce AI have just unveiled SFR-RAG, a groundbreaking 9B parameter language model that's pushing the boundaries of contextual understanding and retrieval-augmented generation (RAG)! SFR-RAG-9B outperforms larger models like Command-R+ (104B) on multiple benchmarks, achieving SOTA results in 3 out of 7 tasks in the newly introduced ContextualBench evaluation suite. The model excels at faithful comprehension of provided contexts, minimizing hallucination, and handling unanswerable or counterfactual scenarios. To create a contextually faithful language model like SFR-RAG, here are the key steps: 1. Design a novel chat template   - Introduce "Thought" and "Observation" roles in addition to standard System, User, and Assistant roles.   - Use "Thought" for internal reasoning and tool-use syntax.   - Use "Observation" for external information and function call results. 2. Prepare training data   - Synthesize diverse instruction-following data mimicking real-world retrieval QA applications.   - Include scenarios for extracting information from long contexts, handling unanswerable queries, recognizing conflicting information, and dealing with distracting or out-of-distribution content. 3. Fine-tune the model   - Use supervised fine-tuning and preference learning techniques.   - Train on the prepared instruction-following dataset.   - Focus on context-grounded generation and hallucination minimization. 4. Implement function-calling capabilities   - Train the model to use external tools and perform multi-hop reasoning.   - Incorporate strategies similar to Self-RAG, ReAct, and other agentic approaches. 5. Evaluate the model   - Use ContextualBench, a compilation of 7 popular RAG and contextual benchmarks.   - Ensure a consistent evaluation setup across all tasks.   - Measure performance using multiple metrics (Exact Match, Easy Match, F1 score). 6. Test for resilience   - Evaluate the model's performance on the FaithEval suite.   - Test its ability to handle unknown, conflicting, and counterfactual information. 7. Assess general capabilities   - Evaluate on standard LM benchmarks (e.g., MMLU, GSM8K).   - Test function-calling abilities using the Berkeley function-calling benchmark. 8. Iterate and refine   - Analyze results and identify areas for improvement.   - Adjust training data, fine-tuning processes, or model architecture as needed.

For years now, prompt engineering shaped how people worked with large language models. It was about finding the right phrasing to get predictable outputs. That approach worked for small tasks, but as models turned into agents that plan, use tools, and retain memory, the limits became obvious. One of Anthropic’s latest articles “𝘌𝘧𝘧𝘦𝘤𝘵𝘪𝘷𝘦 𝘤𝘰𝘯𝘵𝘦𝘹𝘵 𝘦𝘯𝘨𝘪𝘯𝘦𝘦𝘳𝘪𝘯𝘨 𝘧𝘰𝘳 𝘈𝘐 𝘢𝘨𝘦𝘯𝘵𝘴”, introduces the next phase in this evolution, called context engineering. It explains that success now depends on how well we manage what goes inside the model’s attention window rather than how we word instructions. Anthropic describes context as everything the model sees while reasoning, including prompts, data, retrieved results, tool outputs, and message history. Every token consumes a portion of the model’s attention, and as the window expands, its focus gradually weakens. The new challenge is to curate that space carefully. Below are the main lessons from Anthropic’s work that stand out for anyone building practical AI systems. 1. Treat context as a limited resource. Adding more information does not improve accuracy. Use only what directly supports the current reasoning step. 2. Write system prompts like structured briefs. Divide them into clear parts for background, instructions, tools, and expected output. 3. Build small, distinct tools. Each tool should solve one problem and return compact, unambiguous results. 4. Use a few canonical examples instead of long lists of edge cases. Examples should teach reasoning, not overwhelm the model with detail. 5. Retrieve data just in time rather than all at once. Lightweight references such as file paths or queries keep the model’s focus clear. 6. Compact long interactions. Summarize the conversation and restart with the essentials so that the model stays coherent over long sessions. 7. Store information outside the context window. Structured notes or state files help maintain continuity across projects. 8. Use sub-agents for large tasks. Specialized agents can work on details while a coordinator manages direction and synthesis. 9. Balance autonomy with reliability. Some data should stay fixed for consistency, while other parts can be fetched dynamically when needed. 10. Focus attention on signal, not volume. Every token should contribute to the next action or decision. Prompt writing will still matter, but the real skill now lies in shaping context and deciding what enters the model, what stays out, and how information evolves as the agent works. The next generation of LLM Agents will depend less on clever wording and more on precise design of memory, retrieval, and context. Context engineering is becoming the foundation for reliable agents that think and act across long horizons with consistency and purpose.

When it comes to building truly reliable AI agents, I’ve realized that prompting isn’t just about giving instructions, it’s about crafting intentional conversations that guide the model with clarity, structure, and context. These prompt engineering techniques have shaped the way we should think about deploying LLM-powered systems in the real world. The goal isn’t just output, it’s precision, traceability, and contextual awareness baked into every generation It starts with being hyper-specific and detailed—think of your LLM like a new team member. The clearer you are about their task, constraints, and tone, the better they perform. Pair that with persona prompting to set the right expectations, and suddenly your LLM behaves more like a domain expert than a chatbot. From there, you outline the task and give it a plan, making even the most complex workflows feel digestible for the model. Structuring the prompt with bullet points, Markdown, or even XML-like tags makes the output predictable and parseable, especially when dealing with automation pipelines. I often add few-shot examples directly in the prompt to guide the model with real-world context. These examples anchor behavior and dramatically reduce misunderstanding. Things really start to scale with prompt folding and dynamic generation. In multi-stage flows, I let earlier outputs shape the next prompt. It’s how you make agents more adaptive. Still, I always include an escape hatch—asking the LLM to admit when it doesn't know something. It’s a small tweak that prevents hallucinations and builds trust. For deeper insight, I include debug info or thinking traces. Asking the LLM to explain its logic is like reading the footnotes of its thought process—great for debugging and refinement. But the real crown jewel? Your eval suite. Prompting without evaluation is like flying blind. Having test cases lets you track improvements, regressions, and stability across iterations. Finally, LLM personalities and distillation matter more than people think. Some models need more hand-holding; others just “get it.” I often use a bigger model to refine prompts and then distill them down for faster, cheaper inference with smaller models. Building reliable AI agents, don’t overlook the prompt. Get intentional, get structured.

Mastering Conversations with AI 🤖💬 Here’s a guide to making the most of AI conversations: 1. Be Clear and Specific: Narrowing the Probability Space 🎯 Instead of vague requests like “Tell me about cars,” ask specific questions: “Explain the top technological advancements in electric vehicles in the last decade, focusing on batteries and autonomous driving.” Why it works: Specific prompts narrow the range of possible responses, making it easier for the AI to give you a relevant and accurate answer. 2. Provide Context & Examples: Optimizing the Input Window 🧠 Provide context and examples to ensure the AI understands your request. For instance, in legal tasks, context-specific details improve results. Why it works: LLMs process information within a context window, and context helps them make better-informed connections between concepts. 3. Break Complex Tasks into Smaller Steps: Computational Efficiency ⚙️ Rather than asking an AI to do everything at once, break tasks down. Start with an outline, then expand on each part. Why it works: Breaking tasks into steps helps the AI focus and reduces the risk of errors, making the process more efficient. 4. Use the Politeness Principle: Pattern Recognition in Training Data 🙏 Being polite, using "please" and "thank you," can improve the AI’s responses. Why it works: Polite queries activate patterns linked to higher-quality responses, providing more thoughtful and detailed output. 5. Iterate Through Follow-up Questions: Feedback Loop Optimization 🔄 If the first answer doesn’t quite hit the mark, refine your question and ask again. Use follow-ups to clarify or dive deeper. Why it works: Each follow-up helps refine the AI’s understanding, gradually leading to a more accurate answer, much like optimization in machine learning. 6. Encourage Creativity: Activating Diverse Neural Pathways 🎨 Ask the AI to think "outside the box" when you need creative ideas. Why it works: This broadens the AI’s output range, leading to more unconventional and creative ideas, perfect for brainstorming. 7. Treat Each AI as an Individual 👤 Each model has its strengths. Some are great at writing, others at technical tasks. Use the right assistant for the right job. Why it works: Different LLMs are fine-tuned for various tasks, so knowing their strengths helps you maximize their potential. 8. Consider Starting Fresh When Needed 🔄 If the conversation becomes irrelevant or cluttered, start fresh to reset the context. This ensures the AI’s full attention on your new prompt. Why it works: LLMs have limited context windows, and starting fresh ensures the AI processes your input without prior distractions. 9. Engage in Two-way Communication 💬 Don’t just ask and move on. Keep the conversation going with follow-ups to refine the answers and explore deeper. Why it works: Ongoing dialogue helps the AI adjust to your preferences, leading to more relevant and refined responses.

OpenAI just dropped a Prompting Guide for Voice AI Agents Here're 10 Actionable Insights: 1. Iterate and Test Relentlessly > Small wording changes dramatically impact behavior, like swapping "inaudible" to "unintelligible" improves noisy input handling. > Test every prompt modification thoroughly as minor adjustments can make or break performance. 2. Structure Prompts with Clear Sections > Use labeled sections (Role, Personality, Tools, Instructions) to help the model find and follow guidance efficiently. > Organize into focused sections rather than long paragraphs to improve comprehension and consistency. 3. Define Clear Role and Objectives > Pin the agent's identity explicitly to ensure responses stay conditioned to that role throughout. > Specify what "success" means for the agent to maintain focus on achieving goals. 4. Control Personality and Tone Precisely > Set explicit parameters for voice warmth, brevity, and pacing to ensure natural-sounding responses. > Add specific instructions for speech speed and emotional tone rather than relying on playback parameters. 5. Handle Pronunciation Challenges > Provide phonetic hints for brand names and technical terms to improve trust and clarity. > Force character-by-character pronunciation for critical alphanumeric data like phone numbers. 6. Optimize Tool Usage > Align tool descriptions in prompts with actual available tools to prevent non-existent function calls. > Add explicit "when to use" and "when not to use" instructions for each tool. 7. Design Conversation Flow States > Break conversations into clear phases with specific goals, instructions, and exit criteria. > Use state machines or dynamic updates to expose only relevant rules and tools per phase. 8. Implement Variety and Natural Speech > Add variety rules to prevent robotic repetition of the same phrases across turns. > Provide sample phrases as inspiration but instruct the model not to always use exact wording. 9. Handle Unclear Audio Gracefully > Create specific instructions for responding to background noise, partial words, or silence. > Define whether the model should ask for clarification or repeat questions when input is unclear. 10. Enable Proactive Tool Calling > Remove unnecessary confirmation loops by instructing proactive behavior for obvious tool calls. > Add preambles before tool calls to mask latency and improve user experience. 11. Establish Clear Escalation Paths > Define explicit thresholds for human escalation including safety risks and repeated failures. > Specify exact phrases the model should use when escalating to maintain consistency. P.S. Check out 200+ such guides on my profile 👋

The ability to effectively communicate with generative AI tools has become a critical skill. A. Here's some tips on getting the best results: 1) Be crystal clear - Replace "Tell me about oceans" with "Provide an overview of the major oceans and their unique characteristics" 2) Provide context - Include relevant background information and constraints Structure logically - Organize instructions, examples, and questions in a coherent flow. 3) Stay concise - Include only the necessary details. B. Try the "Four Pillars:" 1) Task - Use specific action words (create, analyze, summarize) 2) Format - Specify desired output structure (list, essay, table) 3) Voice - Indicate tone and style (formal, persuasive, educational) 4) Context - Supply relevant background and criteria C. Advanced Techniques: 1) Chain-of-Thought Prompting - Guide AI through step-by-step reasoning. 2) Assign a Persona - "Act as an expert historian" to tailor expertise level. 3) Few-Shot Prompting - Provide examples of desired outputs. 4) Self-Refine Prompting - Ask AI to critique and improve its own responses. D. Avoid: 1) Vague instructions leading to generic responses. 2) Overloading with too much information at once. What prompting techniques have yielded the best results in your experience? #legaltech #innovation #law #business #learning

Learning to effectively communicate with AI is an essential skill in today's tech landscape, and prompt engineering is at its heart. This is more than just giving instructions; it's about mastering the art of dialogue with AI models like ChatGPT. So, why is prompt engineering important, and how does it improve your productivity? Prompt engineering is about shaping questions or commands to elicit precise and relevant responses from AI. It's akin to learning a new language, where you become more fluent in interacting with AI language models, achieving high-quality results more efficiently. OpenAI recently released a great guide to refine your prompt engineering skills and here are the top 6 recommendations: 1. Write Clear Instructions: Precision in your requests is key. Specify your needs, whether simple answers or deep insights. Clear delimiters and context lead to better understanding. 2. Provide Reference Text: Supplying references can guide the AI, especially on niche topics or when accuracy and citations are vital. 3. Break Down Complex Tasks: Tackle intricate problems by dividing them into simpler parts, ensuring more detailed and accurate responses. 4. Allocate Processing Time: Giving the AI time to ‘think’ can improve the quality and accuracy of its responses. 5. Leverage External Tools: Use integrations like OpenAI's Code Interpreter to broaden ChatGPT's capabilities, especially for calculations or specialized queries. 6. Systematic Testing: Continually assess and refine your prompts. This practice is akin to sharpening your language skills, ensuring you consistently get the best out of AI. Embracing these techniques not only enhances your interaction with AI models like ChatGPT but also fosters a deeper understanding of AI's language capabilities, a crucial skill in the digital era. Hours of fun in this - enjoy!! #ai #lifelonglearning #promptengineering

Your AI chatbot is not dumb. It's all about prompt engineering - and yes, it's more of an art than a science. 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗽𝗿𝗼𝗺𝗽𝘁 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴? Prompt engineering is the art and science of crafting the perfect input to get the best output from language models. Think of it as learning to speak the AI's language fluently. 𝗧𝗵𝗲 𝗧𝘄𝗼 𝗞𝗲𝘆 𝗜𝗻𝗴𝗿𝗲𝗱𝗶𝗲𝗻𝘁𝘀 🎯 𝗜𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗼𝗻𝘀: The task description at the beginning of your prompt ✨ 𝗘𝘅𝗮𝗺𝗽𝗹𝗲𝘀: Input-output pairs that show the AI what you want (few-shot learning) Let's break down 3 powerful techniques that can transform your AI interactions: 𝗖𝗵𝗮𝗶𝗻 𝗼𝗳 𝗧𝗵𝗼𝘂𝗴𝗵𝘁 (𝗖𝗼𝗧) → Instead of asking for just an answer, ask the AI to "think step by step." • Basic prompt: "What's 15% of $847?" • CoT prompt: "What's 15% of $847? Show your reasoning step by step." This simple addition makes the AI break down its thinking process, reducing errors and making responses more transparent. 𝗧𝗿𝗲𝗲 𝗼𝗳 𝗧𝗵𝗼𝘂𝗴𝗵𝘁𝘀 (𝗧𝗼𝗧) → Take it further by exploring multiple reasoning paths before settling on an answer. • Example: "Consider 3 different approaches to solve this problem. Evaluate the pros and cons of each approach, then choose the best one." This technique is perfect for complex problems where there isn't one obvious solution path. 𝗥𝗲𝗔𝗰𝘁 (𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝗔𝗰𝘁𝗶𝗻𝗴) → For tasks requiring external data or API calls. The AI reasons about a problem AND interacts with tools to solve it. Example: "To answer this question about current stock prices: 1. First, explain what information you need 2. Search for that information 3. Analyze what you found 4. Provide your final answer" This approach is particularly effective for tasks requiring external data retrieval, API calls, and multi-step interactions with tools. The beauty of these techniques?  They work with any modern LLM – GPT-4, Claude, Gemini, or open-source models. Pro tip:  Start simple with Chain of Thought for most tasks. As your problems get more complex, explore Tree of Thoughts for creative solutions or ReAct when you need dynamic problem-solving. What prompt engineering techniques have worked best for you? Feel free to share below! Get your free Advanced RAG techniques guide here: https://lnkd.in/dNef8zeQ

Most people are using AI like a search box, not like a thinking partner. And that one shift — how you prompt — is the difference between scratching the surface and unlocking the real strength of AI. Prompt engineering is the thoughtful practice of designing inputs for large language models (LLMs) so that they produce accurate, reliable, and contextually appropriate outputs, and it’s far more than just “typing what you think”; it requires understanding how models interpret instructions and structuring prompts to guide their reasoning and results effectively. 🔸️ Zero-Shot Prompting You give the model a task without any examples. It answers using what it already knows. This works well for simple tasks, but for complex problems it may struggle unless the model has been well-trained with human feedback. 🔸️ Few-Shot Prompting You include a few examples of the correct input and output. These examples guide the model and help it understand the pattern, especially when zero-shot answers aren’t good enough. 🔸️ Chain of Thought Prompting The model is asked to explain its thinking step by step before giving the final answer. This helps a lot with problems that require reasoning or multiple steps. Variations like zero-shot or automatic chain-of-thought simply add clear instructions to think step by step. 🔸️ Self-Consistency Instead of choosing the first answer it generates, the model explores multiple reasoning paths and picks the answer that appears most consistently. This improves accuracy for math and logical reasoning. 🔸️ ReAct (Reason + Action) The model not only thinks through a problem but also takes actions, such as using tools or looking up information. This leads to better decisions and more accurate, fact-based answers. 🔸️ Prompt Chaining A big task is split into smaller steps. For example, first extract important information, then answer questions using that information. This makes complex tasks easier to handle. 🔸️ Retrieval-Augmented Generation (RAG) Before answering, the model fetches relevant documents or data and uses them as context. This is especially useful when accurate or up-to-date information is required. 🔸️ Tree of Thoughts Instead of following just one line of reasoning, the model explores multiple possible paths, compares them, and chooses the best one. This helps with complex decision-making. 🔸️ Generated Knowledge Prompting The model first generates helpful background knowledge and then uses it to solve the problem. This leads to better answers when the task needs deeper understanding or context. Together, these techniques show how prompt engineering evolves from basic instructions to sophisticated frameworks for guiding generative AI to handle increasingly complex, structured, and knowledge-rich tasks. Feel free to share your thoughts. 💬

Simple prompt hack that doubled the quality of my LLM outputs. I've been testing AI tools nonstop for months. Most people focus on better models or fancier features. But the biggest improvement came from adding six words to every prompt: "Before answering, ask any clarifying questions." That's it. Why this works: LLMs are terrible at reading your mind. They'll make assumptions about scope, audience, format, constraints - usually wrong ones. By forcing them to ask questions first, you get responses that actually match what you need. Real example: Old prompt: "Write a product roadmap for our AI agent platform" New prompt: "Write a product roadmap for our AI agent platform. Before answering, ask any clarifying questions." The LLM now asks about timeline, audience, level of detail, key features to prioritize. The final output is 10x more useful. Works everywhere: Cursor asks about coding patterns and architecture choices Lovable asks about UI requirements and user flows Claude asks about tone and target audience for writing Any chat interface gets more specific before diving in Most of us rush into prompts like we're texting a friend. But LLMs aren't mind readers. They have limited context and will fill gaps with generic assumptions. Making the clarification step explicit forces better communication upfront. Bottom line: The best AI responses come from better questions, not better models. Try it on your next prompt. You'll be amazed how much clearer the output becomes when the AI actually understands what you're asking for. What prompt tricks have changed your AI workflow? Always looking for new ways to get better signal from these tools.