RAG

Avi Chawla’s 7 October explainer contrasts standard RAG with a Jev gate between retrieval and generation. What hybrid search already does, what typed probabilities add, why thresholds stay in code, and what this architecture does not replace.

Update, 2 Nov 2026: This explainer has been revised. We removed an unsupported claim about measured gains from chatbot memory features, softened claims that RAG systems do...

An AI agent is a system in which a language model decides, step by step, which tools to call and what to do next, rather than following...

The State of AI Infrastructure in 2026: What We Learned from 9 Deep-Dive Guides A synthesis of self-hosting, RAG, quantization, agents, and multimodal AI —...

Vector Databases for RAG: From Chroma to Production (2026 Guide)A beginner-friendly guide to choosing, building and deploying a vector database for retrieval-augmented generation (RAG), starting with Chroma...

Prompt Engineering for Self-Hosted LLMs: Getting the Most from Small ModelsRunning large language models locally has never been more accessible. With models like Phi-3, Llama 3.2, and...