Beyond vector search: hybrid, rerank, and graph RAG
When plain vector search hits its ceiling, three upgrades actually help: hybrid search, reranking, and graph RAG. When each one earns its added complexity.
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#rag
Posts on AI engineering, LLM systems, and software development.
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.
From running models to building systems
Running a model and building with one are different skills. This post opens the AI Engineering series and lays out why the next nine posts exist.
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.
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.