RAG done right: chunking and retrieval mechanics
Naive RAG breaks in practice long before the model does. Chunking strategy and embedding choice decide most of it, and here's where top-k search quietly fails.
Blog
#embeddings
Posts on AI engineering, LLM systems, and software development.
Local Llms#10
Local RAG and embeddings
Build a working local RAG pipeline in about 30 lines using nomic-embed-text, Chroma, and Llama 3.2. And why running it on your own machine beats the cloud for personal notes.
RAG: giving a model memory it doesn't have
RAG is the pattern of fetching relevant text from a search system and putting it in the LLM's context window before asking your question. Not magic, not fine-tuning, just better prompts.