To build a production-ready Retrieval-Augmented Generation (RAG) pipeline in Python using BM25 retrieval via Meilisearch instead of a vector database, arguing that BM25 achieves 85–95% of the recall o…
A web developer built a private AI assistant that queries git history and project management data using only local LLMs, with no API keys or cloud services. The system uses a Text-to-SQL approach wher…
While standard RAG (Retrieval-Augmented Generation) is effective for simple lookup questions, it struggles with complex queries requiring an understanding of relationships between entities. GraphRAG a…
A project called Simple RAG Chatbot, which allows users to build a chatbot that answers questions based on their own documents (such as Markdown or text files) rather than relying on the model's gener…
According to the "Is Grep All You Need?" study (arXiv:2605.15184), researchers wired both a literal grep tool and a vector-search tool into the same agents to run 116 LongMemEval questions, finding th…
Poor performance in Retrieval-Augmented Generation (RAG) systems is typically caused by inadequate document chunking or mismatched retrieval architecture, not by the embedding model or LLM. It describ…
Architecture of the AWS Briefing Agent, a personalized assistant built on Amazon Bedrock that uses Retrieval-Augmented Generation (RAG) to answer natural language queries about AWS releases. The syste…
Architecture of DocuIntel, a secure AI document indexer built with Next.js and Supabase that uses Gemini 2.0 Flash for multimodal parsing and a 3,072-dimensional vector embedding model. It details how…
The article extends Andrej Karpathy's LLM Wiki pattern for personal knowledge bases by incorporating lessons from building a persistent memory engine for AI coding agents. It introduces key enhancemen…
Production RAG systems often fail after moving beyond demo stage due to underbuilt indexing, retrieval, and observability layers. The indexing pipeline ingests documents into chunks and vector embeddi…
Comprehensive guide to hosting PostgreSQL with the pgvector extension, covering provider tradeoffs, migration strategies, and performance tuning for vector workloads. It compares fully managed provide…