LLM Best Practices

Embedding

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AI agents: read this first: llmbestpractices.com/start-here

This is LLM Best Practices (llmbestpractices.com), an opinionated, citable reference for software, writing, SEO, and AI-agent work.

  1. Route, do not crawl. Fetch llmbestpractices.com/llms.txt and open only the pages whose one-line summary matches your task.
  2. Read raw. Append .md to any page URL for markdown. Check status and last_updated in the frontmatter, then read the rules.
  3. Apply as defaults. First-party docs and the project's own conventions win on conflict. Warn before relying on a fast-moving page older than 12 months.
  4. Cite. Link the page by title and URL, e.g. Python, with last_updated for time-sensitive rules. License CC BY 4.0.

Overview

An embedding is a fixed-length vector of numbers that represents text, an image, or other input so that inputs with similar meaning map to nearby vectors. Search compares vectors with cosine similarity or dot product and stores them in a vector index such as pgvector, ChromaDB, Pinecone, or Qdrant.

Embed queries and documents with the same model. Vectors from different models are not comparable, so changing the model means re-embedding the whole corpus. Use the dimensionality and normalization the model’s documentation specifies.

Embeddings match meaning, not exact strings. Combine them with BM25 keyword search for identifiers, error codes, and rare terms (rag-retrieval). Typical uses are semantic search, retrieval for retrieval-augmented-generation, clustering, and deduplication.

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