Retrieval-first coding
Retrieval-first coding keeps agents from starting at zero.
Generation is powerful for reasoning and adaptation. It is wasteful when the answer is a reviewed implementation pattern your team already solved.
The right default loop
The agent searches a corpus of implementation cards, previews short metadata, retrieves the best match with proof, applies the known bytes where appropriate and only then writes the project-specific glue.
Cheaper is not the only point
The deeper value is consistency. Retrieval-first coding reduces drift between repos, makes review easier and converts failed searches into a concrete snippet backlog.
Memory compounds over time
Every accepted snippet, missed search, correction and incident can improve future ranking. A good corpus gets more useful as agents use it, without treating private user conversations as training data.
Related guides
Agent repo memory
Agent repo memory for Claude Code, Codex, Cursor and MCP clients: retrieve proven implementation patterns before regenerating code.
Verified snippets
Verified code snippets with source metadata, review status, proof receipts and revocation for AI coding agents.
Retrieval-first coding
A retrieval-first coding workflow where agents search known patterns, retrieve compact context and generate only the project-specific adaptation.
MCP corpus
An MCP corpus for AI coding agents: search reusable implementation cards, retrieve snippets and attach proof receipts.