ImplCache vs conventional RAG

People often ask whether ImplCache is “just RAG.” The short answer: it uses retrieval, but it is not trying to be a general RAG stack. The product goal is different.

What conventional RAG optimizes for

Many RAG systems optimize for answering questions over a corpus: embed documents, retrieve top-k chunks, stuff them into a prompt, and generate a reply. That works well for Q&A and research assistants. For implementation work, it can still fail in familiar ways—too many chunks, weak ordering, missing constraints, or citations that are hard to act on.

What ImplCache optimizes for

ImplCache optimizes for implementation-ready context under a token budget. The primary tool, get_implementation_context, is designed to return a structured package: APIs, symbols, sequence, examples, constraints, pitfalls, and source citations.

Practical differences

• Local-first by default — SQLite on your machine; no cloud index required.
• Package shape — structured fields for coding, not only unstructured passages.
• Staged depth — start with a package; deepen with find_symbol / search_knowledge; open full documents only when needed.
• Corpus roots — portable URI trees (project://, git://, pdf://) so agents can choose a root instead of mixing product families.
• Optional semantics — sparse term similarity can be enabled; neural embeddings are deferred.

When RAG-style search is still useful

If you need open-ended exploration across many narrative documents, a classic RAG workflow may still be the right tool. ImplCache is a better fit when the agent must implement against a known SDK or codebase and you care about citations, constraints, and keeping the prompt small.