Local RAG and embeddings
A complete local RAG pipeline in 30 lines: nomic-embed-text for embeddings, Chroma for the vector DB, Llama 3.2 for the chat model. Why local RAG often beats cloud RAG for personal knowledge bases.
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Posts on AI engineering, LLM systems, and software development.
Prompt, RAG, fine-tune: three ways to shape a model
Three levers for shaping what an LLM does: prompting (ask better), RAG (give it the right context), fine-tuning (change the weights). What each costs, what each fixes, and how to pick.
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.