Guides

Short explanations of the formats and concepts behind the tools. See theeditorial policy for how guides are written and updated.

• Choosing chunk size and overlap for RAG

  How chunk size and overlap affect retrieval quality, cost and context use, with chunk-count arithmetic and a method for testing settings on your own questions.

• Function-calling formats compared

  The same tool definition written in OpenAI, Anthropic, Gemini and MCP formats, what each field is called, and what gets lost when you convert between them.

• How LLM API pricing works: input, output and cached tokens

  What you are billed for when calling an LLM API: input, output, cache write and cache read tokens, the price modifiers, and a worked monthly cost example.

• How much VRAM does a local LLM need?

  GPU memory for a local model is weights plus KV cache plus overhead. The formula, quantisation and grouped-query attention explained, with five worked models.

• Vector database sizing: precision, dimensions, overhead

  Estimate RAM and disk for a vector index from vector count, dimensions and precision, what HNSW links add, and how quantisation changes size. With examples.