I'm building CortexDB — an agent-native context database for AI agents
I'm building CortexDB — an agent-native context database for AI agents Most modern RAG systems work like this: Split documents into chunks Generate em…
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Reduce LLM Token Waste in RAG with Markdown
TL;DR Feeding raw HTML to Large Language Models wastes tokens on markup, scripts, and styling. By rendering dynamic web pages in a headless browser an…
Your .NET RAG stack hides a Python sidecar. I built the engine that removes it.
TL;DR — Every .NET RAG project quietly ships a Python sidecar to do one job: chunk documents. I got rid of mine. DocNest .NET is an idiomatic C# / .NE…
Claude LLM Execution Harnesses, RAG Rerank, & Browser-based Edge AI
Claude LLM Execution Harnesses, RAG Rerank, & Browser-based Edge AI Today's Highlights This week's top stories delve into advanced LLM orchestrati…
Optimizing RAG Pipelines, Migrating AI Agents, and LLM-Powered Troubleshooting
Optimizing RAG Pipelines, Migrating AI Agents, and LLM-Powered Troubleshooting Today's Highlights This week's highlights cover advanced strategies for…
I Built 'Chat With Your Docs' From Scratch — Supabase + pgvector + a Free Local Embedder
"Chat with your PDF / your notes / your docs" is everywhere. Today we build it from scratch and you'll see it's just three moves : retrieve, then gene…
Define the state of our agent
Meta: Learn how to eliminate LLM hallucinations in career coaching apps using Agentic Workflows and RAG, as seen in the architecture of CVChatly. The …
Building Modular AI Agent Features with Pydantic AI Capabilities
If you're building AI Agents with Pydantic AI, understanding Capabilities is invaluable - it's the recommended way to add modular, reusable features t…
A Chinese 8B model beat the Western 8B models at Japanese RAG. I still wouldn't put it in the default deployment — and that distinction is the point.
Extends an earlier model-selection benchmark to three model families (Japanese / Western / Chinese) on a Japanese RAG task. Repo + raw results: https:…
Two Pre-Registered Benchmarks for Audit-Native RAG: RAB (EU AI Act 10/12/19) + LRB (Time-Travel Retrieval)
Most RAG demos answer "what's the right chunk?" Very few can answer the two questions a regulator or an auditor will actually ask: Replay this decisio…
Most RAG Problems Are Retrieval Problems. Here Are 8 Fixes That Worked for Me
The first few times a RAG system gave me a bad answer, I did what I think everyone does: I went and fiddled with the prompt. Made it stricter. Added a…
Context Compression Before the LLM: Cutting Tokens Without Cutting Recall
Book: RAG Pocket Guide: Retrieval, Chunking, and Reranking Patterns for Production Also by me: Thinking in Go (2-book series) — Complete Guide to Go P…
Query Rewriting Before Retrieval: The Cheap Recall Win Most Skip
Book: RAG Pocket Guide: Retrieval, Chunking, and Reranking Patterns for Production Also by me: Thinking in Go (2-book series) — Complete Guide to Go P…
AI Agents Level Up Workflows: Terraform MCP, WebMCP, Pinecone Integrations
AI Agents Level Up Workflows: Terraform MCP, WebMCP, Pinecone Integrations Today's Highlights This week showcases significant advancements in AI agent…
38/60 Days System Design Questions
Your LLM has 128K tokens. Your document has 150K words. Something has to give. What do you do? A) Chunk the document into fixed-size pieces and embed …
Why my first RAG layer starts in Postgres, not in a standalone vector database
When people say they are "adding RAG" to a workflow, the conversation often jumps too quickly to infrastructure choices. Should this use a vector data…
Metadata Filtering Before Vector Search: The Recall Win Nobody Measures
Book: RAG Pocket Guide: Retrieval, Chunking, and Reranking Patterns for Production Also by me: Thinking in Go (2-book series) — Complete Guide to Go P…
Local AI Coding Agents, Secure Production Deployment, and Angular-Specific AI Skills
Local AI Coding Agents, Secure Production Deployment, and Angular-Specific AI Skills Today's Highlights This week's top stories highlight practical wa…
AI Customer Service Chatbot with Demo Link
What I built A small business owner needed an automated customer support system that works 24/7, answering questions based only on their internal poli…
Build a RAG Pipeline From Scratch (Production Patterns That Actually Matter)
Most RAG tutorials stop at "embed your docs, do a similarity search, stuff the results in a prompt." That gets you a demo. It does not get you somethi…
Token Cost Optimization: How to Cut LLM Inference Spend Without Cutting Quality
There is a version of token cost optimization that I do not recommend: cutting token counts by reducing the quality of your system prompt, your retrie…
RAG (Retrieval-Augmented Generation) Explained for Beginners: Build AI Applications Using Your Own Data
Introduction Large Language Models (LLMs) such as ChatGPT, Gemini, and Claude are incredibly powerful. They can answer questions, generate code, summa…
AI Agent Security, Open-Source Code Generation, and Frontier Models on Bedrock
AI Agent Security, Open-Source Code Generation, and Frontier Models on Bedrock Today's Highlights This week highlights a new security scanner for AI a…
How to make AI answer questions about your documents, by building RAG from scratch
In the previous post , we talked about context windows. The model has a fixed-size desk and everything has to fit on it at once. When too much is on t…
Production-Grade RAG: Why Vector Search Isn't Enough (and How Hybrid Search Fills the Gaps)
Imagine your team just deployed a sleek RAG-based docs assistant for the SaaS platform you develop. In testing, it worked flawlessly. It knows your fu…
Telegram-бот с RAG на Cloudflare Workers: база знаний без векторов и без базы данных
Строим Telegram-бота с RAG-поиском по базе знаний — без векторных БД, без эмбеддингов, без платной инфраструктуры. Поиск по ключевым словам через Jacc…
RAG-Based Testing Series — Part 4: Edge Cases — What Breaks RAG & How to Catch It
RAG-Based Testing Series — Part 4: Edge Cases — What Breaks RAG & How to Catch It "Your users will never read your happy path. They will, however,…
AI Output Provenance for SaaS: Trace Answers Before They Become Liability
An AI answer can look clean, confident, and helpful while hiding the exact detail your team will need later: where did this claim come from? For AI Sa…
Build Your RAG System Right the First Time: 6 Decisions That Make or Break It
After debugging 20+ broken RAG systems, I've identified the 6 decisions that determine whether yours works. Here's how to get each one right. The RAG …
Your vector memory database remembers everything. That’s exactly the issue.
There is a design assumption baked into almost every vector database and AI memory implementation that sounds reasonable until you watch it grow nodes…