DocuLayer vs RAG
A frequent question: why not just RAG the docs?
Comparison
| RAG | DocuLayer | |
|---|---|---|
| Storage | Vector DB required | None — in-memory TTL only |
| Freshness | Depends on indexing schedule | Always live (TTL-bounded) |
| Accuracy | Semantic similarity | Verbatim text from source |
| Setup | Embedding model + DB + ingestion pipeline | pip install doculayer |
| Hallucination risk | Embedding drift, chunking artifacts | Zero — no generated text |
| ML inference | Required (embedding model) | None — BM25 only |
| Maintenance | Re-index on every doc update | No maintenance |
When RAG is Better
RAG is a good fit when:
- You have proprietary documents that aren't on the public web
- You need semantic similarity over exact keyword matching
- You're building a chat-over-documents product (not tool-augmented agents)
- Your document corpus is static (won't change frequently)
When DocuLayer is Better
DocuLayer is a better fit when:
- You want live, current documentation (not a snapshot from last week's index run)
- You want zero hallucination — the agent should read real docs, not paraphrases
- You want zero setup — no embedding model, no vector DB, no ingestion pipeline
- Your queries are API-oriented — exact function names, parameter names, version-specific behavior
- You want attribution — every response is sourced to a URL
Why Not Just Give the Agent a URL?
You could. But:
- The agent still has to know which URL. For a library with 200 pages, it guesses.
- The agent fetches the whole page and summarizes it — that's generation, which means drift.
- DocuLayer uses llms.txt to fetch only the 1–3 pages most likely to answer the query, then returns the relevant section verbatim. The agent reads real documentation, not a paraphrase.
The Core Difference
RAG answers questions. DocuLayer retrieves source material.
An agent using RAG reads a summary of documentation. An agent using DocuLayer reads the documentation itself, exactly as the package maintainer wrote it, fetched seconds ago.