Language Models

How does HNSW actually work? 𝗛𝗶𝗲𝗿𝗮𝗿𝗰𝗵𝗶𝗰𝗮𝗹 𝗡𝗮𝘃𝗶𝗴𝗮𝗯𝗹𝗲 𝗦𝗺𝗮𝗹𝗹 𝗪𝗼𝗿𝗹𝗱 (𝗛𝗡𝗦𝗪) is the algorithm behind most modern vector databases, but the algorithm can seem pretty complex. Here's the breakdown of how it works, and why so many vector databases use it: 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝘁𝗵𝗲 𝗜𝗻𝗱𝗲𝘅 HNSW creates a hierarchy of layers to speed up traversal of the nearest neighbor graph. - Top layers contain only long-range connections - Bottom layer contains ALL vectors with dense local connections - Each layer down includes...

🧠 Prompt Architecture Master class by Anthropic The era of casually tossing a task into a chat window and hoping for the best is over. Believe me I have tried! Maybe after Sam unleashed the AGI! Meanwhile, If you're serious about building realworld LLM applications, especially ones that automate decisions, parse complex documents, or interact with humans, you need more than clever instructions. You need prompt architecture: structured, testable, reusable patterns and better context...

RAG is no longer a “technique.” It’s a whole ecosystem — and a developer’s stack. Over the past 18 months, Retrieval-Augmented Generation (RAG) has quietly evolved from a simple “search + LLM” pattern into a complete engineering discipline. Today, building AI systems isn’t just about choosing a model. It’s about architecting the entire pipeline — from extraction to embeddings, from vector databases to evaluation, from closed models to open-source innovators. | 52 comments on LinkedIn

Everyone in AI is talking about Context Engineering. But just a few explain what the context is. Save this template. It captures all scenarios and will help you maximize agents' performance: 1. Instructions Define: → Who: Encourage an LLM to act as a persona → Why is it important (motivation, larger goal, business value) → What are we trying to achieve (desired outcomes, deliverables, success criteria) 💡Providing strategic context beyond raw task specification improves AI autonomy...

Steal My 10 Principles of Building AI Agents: (learned the hard way) 1. Don’t Use Agents If You Don't Have To Nobody cares if it's an AI agent or a simple script, as long as it works. A good old if/else is faster, cheaper, and more reliable. And it's often all you need. Save the agents for when you really need them. 2. Small, Specialized, and Decoupled Think "team of specialists," not "one agent to rule them all." A planner plans. A summarizer summarizes. A verifier checks. Decoupled agents...

I’ve always enjoyed learning how things work beneath the surface. It’s a mindset that’s guided my entire career. I don’t just want to use tools, I want to understand the full system, end to end. Currently, I’m focused on learning everything about LLM inference, as it is where most of the heavy lifting happens in an AI system: - How it works under the hood - How it runs at scale - How it handles massive workloads and sharded models - How it performs under real-world constraints, metrics, and...

Why do LLMs Need Context Engineering and how can we make it useful for Greg the user? Well, Context Engineering is the process of gathering, organizing, and feeding relevant information to AI systems to generate more accurate, personalized, and actionable responses. Unlike basic prompting, context engineering creates a comprehensive information framework that keeps track of user history, real-time data, and environmental factors. This approach transforms generic AI interactions into highly...

🚨 LangChain just dropped probably the most useful and condensed manual for building AI agents! Forget about the hype. Most teams don’t know where to start when it comes to real, production-ready agents. This guide will help you to move from experimentation into real production Agentic AI systems at scale. 🛠️ Whether you're automating email workflows or building multi-agent systems, this 6-step framework is the clearest path from idea to impact I’ve seen: 1️⃣ Define the Job If a smart intern...

This ChatGPT hack will save you a lot of rejections! Most CVs don't make it past the ATS systems companies use. Here's a simple prompt to fix that ↓ Just paste this into ChatGPT- ------------- Prompt: You are a professional CV optimizer. Ask me to upload my CV and the job description. Then analyze both and provide: 1) Key Skills to Emphasize ↳ Identify skills, tools, and competencies from the job description I should highlight. 2) Gap Analysis ↳ Point out missing skills, qualifications, or...

How to turn ChatGPT into Your Personal Skill-Building Coach? Imagine having a tutor available 24/7 who can break down any topic into clear, structured lessons, ask the right questions, guide you through exercises and adapt based on your understanding. This is exactly what ChatGPT enables. Who it helps? → Students and lifelong learners: Receive personalized learning at your own pace with lessons that match your comprehension. → Professionals upskilling: Learn new skills quickly, from coding...

This figure provides a comparative overview, distinguishing between individual 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 and the broader 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 paradigm. On the left, an 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 is depicted as a command-driven, reactive tool that interacts with a specific task-oriented component. It typically performs tasks within predefined boundaries, acting like a highly skilled robot specialized for a particular function. Such an agent makes decisions based on narrow focus or established rules, often waiting for a trigger or...

The gpt-oss models from OpenAI are a synthesis of ideas from prior research. Here are 10 interesting papers that were directly used in gpt-oss… (1) Longformer: Introduces sliding window attention, a form of sparse attention that is utilized in alternating layers of both gpt-oss models. (2) StreamingLLM: Describes the concept of attention sinks in large language models (LLMs)—these are tokens within a sequence that the model assigns high attention or weight to, simply because the softmax...