Document Search and RAG
Technical guides on Document Search and RAG.
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[Local LLM 16] Search Documents with Embeddings and Cosine Similarity
Turn document passages and a query into embeddings, rank them by cosine similarity, and inspect retrieval quality.
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[Local LLM 17] Build RAG Answers from Retrieved Documents
Retrieve relevant document passages, include them in the prompt, and check whether the answer is supported by those passages.
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[Local LLM 18] Build an Evaluation Sheet for Models and RAG
Use a fixed question set and scoring criteria to compare model answers and retrieval results before changing the application.