What is retrieval-first coding?
Generation is powerful for reasoning and adaptation. It is wasteful when the answer is a reviewed implementation pattern your team already solved.
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.
From search to an auditable implementation
1. Search
Start with a short intent query. Search is cheap and returns compact metadata before the agent spends tokens on a full retrieval.
2. Retrieve
Open the strongest match only when provenance, license, review state and project fit justify reuse.
3. Apply and learn
Adapt the known pattern, record the outcome and turn misses or corrections into better future retrieval signals.
What Moresq Corpus is not
Corpus is not a promise that every retrieved snippet is correct, a replacement for code review, or a silent training dump of private chats. It is a governed reuse layer: the process verifies provenance and review evidence, while the developer remains responsible for fit, testing and the final change.
Questions developers ask
How does Moresq Corpus help?
Moresq Corpus lets coding agents search reusable implementation cards, retrieve relevant snippets with proof receipts, and learn from misses, applies and corrections.
Does retrieval remove the need for code review?
No. Retrieval can reduce repeated work and expose provenance, but developers still need to check project fit, security implications and test results before shipping.
Related guides
AI coding context loss
Why AI coding assistants lose context, repeat code and forget repo decisions — and how retrieval-first repo memory fixes the workflow.
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.
MCP corpus
An MCP corpus for AI coding agents: search reusable implementation cards, retrieve snippets and attach proof receipts.