"Context Engineering: A New Approach to AI and RAG"

🔥 why this approach matters and how it’s better than standard RAG. 👇 🧠 Building Adaptive RAG with Self-Reflection — Why This Matters Traditional RAG (Retrieval-Augmented Generation) is powerful — it grounds LLMs with external knowledge. But it’s still static: every query follows the same pipeline — retrieve → generate → answer. What if the system could analyze the query, adapt retrieval depth, and reflect on its own output before answering? That’s where Adaptive RAG + Self-Reflection comes in. 🚀 ⚙️ My Implementation Approach Using LangGraph, I’m designing each step as a node — Retrieve, Grade, Rewrite, and Generate — and connecting them into a dynamic decision graph (as shown below 👇). Each node can run independently and learn from feedback: 🔍 Query Analysis Node → classifies queries (domain-related or general). 📚 Retrieve & Grade Nodes → assess if retrieved documents are relevant. 🧩 Re-write Node → refines the query when relevance drops. 🤖 Self-Reflection Node → checks for hallucinations or incomplete answers before final output. 💡 Why This Matters Adaptive RAG isn’t just another RAG variant — it’s a smarter retrieval loop that brings three key advantages: 1️⃣ Dynamic Context Control – Retrieves only what’s needed; saves compute and reduces noise. 2️⃣ Higher Reliability – Detects hallucinations and low-confidence outputs before returning an answer. 3️⃣ Domain Adaptability – Handles both indexed (internal) and web-based (external) information seamlessly. 🔍 The Bigger Picture By treating each step as a modular node and connecting them with LangGraph, we move from a linear RAG pipeline → to an adaptive reasoning graph that can self-evaluate and self-correct. This means RAG systems that: Learn over time Adjust retrieval depth per query Deliver more accurate, trustworthy, and efficient answers 💬 Curious to hear your thoughts: Do you think adaptive, graph-based RAG will become the new standard for enterprise AI systems? #GenAI #RAG #AdaptiveRAG #LangGraph #LLM #AIEngineering #RetrievalAugmentedGeneration #SelfReflection

🚀 Just finished building one of my services: Query your files conversationally 🔹 The Problem Extracting insights from large PDFs/docs is painful. I wanted to make this process natural and fast. 🔹 My Approach I combined: ⚡ A high-performance API (NestJS + Fastify) ☁️ Scalable storage (S3) 🧠 Embeddings + vector search 🔀 Multi-threading + caching for speed and reliability 🔹 The Result Upload a file → Ask a question → Get contextual answers instantly. 🔹 Key Learnings How RAG systems work in production Why performance optimization (multi-threading, caching) matters Building reliable AI services needs both system design + ML knowledge Super excited to continue refining this. #ArtificialIntelligence #MachineLearning #RAG #FullStackDevelopment #SystemDesign #NestJS #VectorDatabase #Innovation

Damn - a unified backend framework to build and scale AI Agents! Motia is a backend framework that brings together agents, APIs, background jobs, streams, and workflows into a single core primitive. Think of it as the missing backend for agents, a single place to build, run, and observe your entire stack. Using Motia’s new Cursor rules, I one-shotted an entire multi-agent research system with a single prompt that: • Orchestrates five core services (Agent, Fetcher, Extractor, Summarizer, Publisher) • Handles parallel source fetching with retry logic • Extracts and synthesizes data from multiple sources • Generates structured reports with citations • Manages failures gracefully (timeouts, incomplete data, partial results) Everything is event driven and observable. It’s 100% Open Source. Link to the GitHub repo in the comments!

I know this isn’t a hot topic right now, but… Bloom Filters. They’re one of those low-level architectural details most developers never touch directly, mainly because they’re abstracted away by databases and search engines. Unless you’re building your own custom engine from scratch or working at a MAANG-scale company, you’ll probably never implement one yourself. Still, building a small database engine just to experiment with Bloom filters can be a great learning exercise, it's what makes 10x engineers 10x. I’ve attached a small playground built with v0 where you can read the definition and test how they work. P.S. The definition was typed by hand, not generated by AI. https:// https://lnkd.in/ePRMfvqq

The gap between building and evaluating AI agents has always been frustrating. You write code in your IDE, then context-switch to dashboards to understand why your agent failed. You've lost momentum by the time you've diagnosed the issue and returned to your code. Today, we're launching Galileo's Agent Evals MCP to fix this. With our new MCP server, you can now access Galileo's evaluation and observability platform directly in Cursor or VS Code. Your IDE's AI assistant becomes an eval-powered copilot that helps you: ✅ Generate synthetic test datasets to simulate edge cases ✅ Get logstream insights that pinpoint exactly where agents fail ✅ Set up and validate prompt templates without leaving your editor ✅ Tab-complete from root cause analysis directly to generated fixes This is what evaluation-driven development should look like: insights where you code, not after you deploy. As agentic systems grow increasingly complex, the traditional "build first, evaluate later" approach creates unacceptable risk. Production failures that could have been caught during development slip through and erode user trust. By bringing disciplined experimentation and quality assurance into the IDE itself, teams can catch issues earlier and ship more reliable agents faster. Setup takes minutes. One config file gives you access to our complete evaluation platform from within your development environment. Ready to transform your agent workflow? Set it up for yourself👇 #AIAgents #DeveloperTools #AIEvaluation #MCP

🚀 RAG Evolution: From Classic to Advanced The landscape of Retrieval-Augmented Generation (RAG) has evolved dramatically.  Whether you're building enterprise applications or optimizing LLM performance,  understanding the nuances between RAG, GraphRAG, PathRAG, and LightRAG is crucial. 📚 CLASSIC RAG ✅ Simple & Fast | Cost-effective | Well-established | Scalable ❌ Shallow context | Ignores relationships | Embedding limitations 🎯 Best for: Q&A systems, document summarization 🕸️ GRAPHRAG ✅ Relationship awareness | Handles complex queries | Semantic richness ❌ High complexity | Extraction errors | Needs graph databases 🎯 Best for: Knowledge management, complex reasoning 🛤️ PATHRAG ✅ Interpretability | Efficiency | Explainable reasoning ❌ Path limitations | Graph dependency | Scalability challenges 🎯 Best for: Question answering, explainable AI ⚡ LIGHTRAG ✅ Lightning-fast | Handles millions of docs | Low resource usage ❌ Limited context | Accuracy trade-offs | Shallow reasoning 🎯 Best for: Real-time apps, edge deployment, high-volume queries 📊 QUICK COMPARISON: - Speed: Classic RAG ⚡ | GraphRAG 🐢 | PathRAG ⚙️ | LightRAG ⚡⚡ - Context: GraphRAG 🏆 | PathRAG 🏆 | Classic RAG ✓ | LightRAG ✓ - Simplicity: Classic RAG & LightRAG 🏆 - Scalability: Classic RAG & LightRAG 🏆 🎯 KEY TAKEAWAY: Choose based on your use case: → Real-time apps? LightRAG → Complex reasoning? GraphRAG or PathRAG → Quick deployment? Classic RAG → Balance of speed & context? LightRAG or PathRAG What's your go-to approach? Share your experiences in the comments! #AI #LLM #RAG #GenAI #MachineLearning #VectorSearch

🚀 New Publication Alert 🚀 I’ve just published a new article on Medium where I share my experience building an AI-powered knowledge assistant using Retrieval-Augmented Generation (RAG). In simple terms, it’s a system that turns a collection of documents into an interactive knowledge base — you can ask questions, and the AI answers using information directly from your files. Under the hood, it combines: 🧠 Large Language Models (LLMs) for intelligent answers 🔍 Qdrant for storing and retrieving document embeddings ⚙️ A smart architecture that connects it all together In the article, I walk through how everything works, what challenges I faced, and how the pieces fit to make the “magic” happen. If you’re curious about how RAG systems work or what it takes to build one, check it out here 👇 https://lnkd.in/d-6dYrRM

Lessons learned from building a lightweight AI with RAG Lately, I’ve been playing with how far you can push a small, open model when it’s paired with a really solid retrieval strategy. The project started as a simple local chatbot, but it quickly turned into a hands-on exploration of what matters when you’re building something lean, smart, and practical. Retrieval > Model Size My first version relied too much on the model, and it showed. When I cleaned and structured the source data, prioritized key files in the vector index, and added deterministic extractors, the accuracy jumped dramatically. The lesson: better retrieval often beats a bigger model. Hybrid Pipelines Are Your Friend I ended up with a hybrid setup: - Strict RAG for structured questions (rank data, terminology) - LLM fallback for open-ended prompts - Priority reranker + context injection to make sure the right data always wins This gave me high precision where it mattered and flexibility where it was needed. Hosting Smart, Not Big For deployment, I didn’t need a massive infra. Local hosting handled iteration and testing. A simple Render + OpenRouter setup provided low-cost hosting with the option to swap models easily. The combination gave me a real MVP with room to grow. My takeaways: - Clean, structured domain data is a superpower. - A well-designed retrieval layer is often more valuable than scaling the model. - Small models can punch way above their weight with the right architecture. - Cost-efficient hosting can be good enough for production MVPs. Building something lean like this is a great reminder of what’s possible with the right strategy and a bit of stubborn fine-tuning. For those of you experimenting with lightweight models and RAG, what tips, lessons learned, or best practices would you share? #RAG #LLM #GenAI #OpenSource #MVP #DeveloperTools #OpenRouter #VectorSearch #DevRel

Anthropic just dropped Haiku 4.5 and it’s shockingly fast with Web Design. Think frontier-level performance at a fraction of the cost. This tiny model runs complex tasks in seconds — coding, reasoning, even multi-agent orchestration. Feels like the start of a new era for lightweight AI systems. #ClaudeAI #Haiku4_5 #AI #LLMs #AgenticAI #EfficientAI #Anthropic #AIEngineering #MLOps