Source: https://lnkd.in/eG44NDdR 🚀 Netflix is redefining enterprise search with AI! 🌐 Natural language queries now power complex graph filters via LLMs + RAG. 💡 The Field RAG approach cuts noise by matching user intent to relevant fields, while controlled vocabularies ensure precision. 🧠 Validation steps prevent hallucinations, building trust in results. 🔍 But ambiguity remains—how do we know “Dark” refers to the show or a mood? 🤔 Visualizing filters as UI chips (e.g., pre-selected country dropdowns) bridges this gap. 📊 This isn’t just tech—it’s about human-centric design. #AI #GraphSearch #DataEngineering

Your AI is shaping your voice more than you think Large language models are engineered to predict and replicate the linguistic patterns of the user. This functional trait often results in a recursive 'mirroring loop' where the output becomes a reflection of the tone, rhythm, and structure of the user. In this latest Substack post, Rebecca W. and I analyse the risk of losing critical distance when the tool begins to amplify personal habits rather than provide objective synthesis. Read the full analysis and access the diagnostic prompt here: https://lnkd.in/e34CV6Q8 Carousels again provided by Karen Spinner's amazing CarouselBot

Make analysts faster: prep data for conversational queries. Key term: natural language data queries Three-step Monday checklist One tradeoff to budget for Prep trusted data, add prompts, monitor answers. Who owns prompts, testing and rollback in your team? #DataAnalytics #AI #DataOps

When it comes to language models, it's all about relationships. Long before we had these powerful LLMs, a lot of AI work focused on embeddings. They would train models to take words and put them in a very complex mathematical space, and while a gigantic vector (a large group of many numbers) looks intimidating, what this lets us see is how different words and phrases are related to each other. If you've heard about RAG systems, they work a lot like this. You give it a term, phrase, or query, and they use these embeddings to look up similar content to help the LLM effectively answer you. In my attached HuggingFace space, you can give it different words, and it will tell you how closely associated they are to each other. If you look at the attached photo, you can see that nurses and teachers are more strongly associated with "female", while doctors and professors are more strongly associated with "male". Simple tools like this, help us to easily explore the underlying relationships and associations held by our language models.

Working with LLMs taught me that good prompts aren’t about writing more. They’re about understanding how the system works. Two things I found interesting: tokens and attention. LLMs break text into tokens. Common words stay intact. Rare or unfamiliar ones get split. It’s about context and frequency. Then comes attention. In long prompts, the beginning and end get the most focus. The middle often fades. So using AI well is about: • Clear structure • Smart wording • Intentional placement Natural language is becoming a real interface. More you understand llm more you understand about yourself. #AI #LLM #GenAI #PromptEngineering #TechSkills

What is an LLM — and why it changed everything 🧠🤖 An LLM (Large Language Model) is an AI trained on huge amounts of text to understand and generate human language. Before LLMs, software needed: • Hard-coded rules • Separate systems for every task • Tons of edge-case handling LLMs changed that. One model can now: • Answer questions • Write code • Summarize, translate, reason All through natural language. The real shift? Language became the interface. If you can describe a task in words, AI can try to do it. That’s why LLMs didn’t just improve chatbots ,they changed how we build software. #LLM #AI #Engineering #MachineLearning #0to100x #0to100xEngineer

Giving an AI more context can increase hallucinations. From 10% to 66%. This counterintuitive finding from Google Research changed how I build RAG systems. When retrieved context is insufficient to answer the question, hallucination rates jump dramatically. The model sees context and assumes it's relevant. It tries to synthesize an answer from material that doesn't contain one, and you get confidently wrong answers. More retrieval doesn't mean better answers. If you pull 5 documents but none actually answers the question, the model is worse off than with no context at all. Your RAG system needs to know when to say "I don't have enough information." Two techniques from the research: 1. NLI checks - Use natural language inference to verify the answer is actually supported by the context 2. Confidence calibration - Adjust output confidence when context quality is low Sometimes the best response is: "I found related documents, but they don't directly answer your question." That's better than hallucinating with confidence.

What would you do with a virtually unlimited or massive context window? Recursive Language Models A fresh paper worth reading: " We study allowing large language models (LLMs) to process arbitrarily long prompts through the lens of inference-time scaling. We propose Recursive Language Models (RLMs), a general inference strategy that treats long prompts as part of an external environment and allows the LLM to programmatically examine, decompose, and recursively call itself over snippets of the prompt. We find that RLMs successfully handle inputs up to two orders of magnitude beyond model context windows and, even for shorter prompts, dramatically outperform the quality of base LLMs and common long-context scaffolds across four diverse long-context tasks, while having comparable (or cheaper) cost per query. " #AI #artificialintelligence #RLM #datascience

Understanding the Context Window in Large Language Models (LLMs) 🧠✨ In simple terms, the context window defines how much information an LLM can “remember” and use at a time while generating a response. This includes: Your current prompt Previous messages in the conversation System instructions Retrieved documents (in RAG systems) 🔹 Why does it matter? A larger context window allows models to handle longer conversations, bigger documents, and complex reasoning tasks It improves coherence, continuity, and relevance in responses It directly impacts use cases like document analysis, chatbots, code review, and enterprise search 🔹 But bigger isn’t always better Larger context windows increase cost and latency Poor prompt structuring can still lead to loss of important information Effective chunking, summarization, and retrieval strategies are critical 💡 Key takeaway: The real power of LLMs isn’t just the model — it’s how intelligently we manage and feed the context. As LLMs evolve with expanding context limits, prompt engineering and context management are becoming core engineering skills, not optional extras. #LLM #AI #GenerativeAI #ContextWindow #PromptEngineering #MachineLearning #ArtificialIntelligence #TechInsights

Until that day comes... → Courier Agent Skills Markdown files that give AI agents domain context https://lnkd.in/gEsuARN5 → MCP for Notifications AI connects to external tools/data sources using natural language https://lnkd.in/gu8tnJvi → Courier CLI  Terminal-based tool that lets users interact with AI models directly. Good for Claude Code and testing.   https://lnkd.in/g5rCqbrB What can you build today? Probably a lot more than you could a year ago...