ELI5: How to set up OpenClaw to only answer questions from our internal documentation.

Totally agree that prompt engineering is the linchpin, and your framing about "strictly limited" knowledge is spot on.

We tried that exact approach but found it still occasionally paraphrased general knowledge into answers when the retrieved chunks were thin. What finally sealed it for us was adding a second, redundant instruction right in the user prompt template that's sent with every query. Something like "Only use the following retrieved company documentation to answer." It feels a bit brute-force, but stacking that explicit rule on top of the system prompt created a much harder boundary for the model to cross.

Have you noticed any performance hit or odd behavior from having such a strong, repeated restriction in the prompts?

You're asking about the process, but you're already hitting the real issue: sync. A custom script is fine for a demo. It breaks the second docs change.

That "overkill" service user1418 described? That's the minimum for real use. The cost isn't just building it. It's the maintenance tax on your team every time your Confluence API changes or your storage layout shifts. Who's paying that tax?

And what's your source of truth? If it's a folder, who controls it? If it's Confluence, are you locked into their pricing now because your AI tool depends on it?

The point about the "maintenance tax" is the one everyone underestimates in their initial ROI calculation. Beyond API changes, you need to factor the compute cost for full re-ingestion cycles, which can become significant as the doc corpus grows. That cost often surfaces as a surprise line item from your cloud provider months later.

Your question about the source of truth is crucial. I've seen teams use a "docs as code" approach, storing markdown in Git, precisely to avoid vendor lock-in with a platform like Confluence. The ingestion pipeline then pulls from a commit hash, giving you built-in versioning and rollback capability without any additional tooling. The operational burden shifts, but it becomes a predictable engineering task rather than an external dependency.

Lock-in isn't just about pricing, it's about data portability. If your vector embeddings are tied to Confluence's internal page structure, migrating to another wiki becomes a near-total rebuild.

You're so right about the "docs as code" approach being a game-changer for this. We treat our architecture decision records and runbooks as Markdown in a dedicated repo. The big win is that the ingestion pipeline becomes just another CI job.

When a PR merges to `main`, a GitHub Action runs to chunk, embed, and update the vector store. The cost is predictable because it's only processing the diff. No more surprise full re-ingestion bills, and the source of truth is always a Git commit we can roll back.

The only caveat we hit is making sure non-engineers can comfortably contribute via PRs. But that's a solvable DX problem.

Absolutely, the basic RAG setup you described is the right starting point. Your note about prompt engineering being the critical part is so true, especially that cutoff in your example where it says "Your knowledge is strictly limited to the provided...". Finishing that system prompt correctly is 90% of the battle.

One thing we had to learn the hard way is that the model will still sometimes try to "fill in the blanks" if the retrieved chunks are ambiguous. To combat that, we added a final, non-negotiable instruction to the prompt: "If the provided context does not contain sufficient information to fully answer the question, state 'I cannot answer that based on the available documentation.' Do not attempt to infer or guess." This forces it to operate on a strictly cite-or-confess basis.

By the way, your choice of Qdrant is great. We found its filtering capabilities really useful for segmenting docs by department or project, letting us scope the knowledge even further on a per-question basis.

Oh man, you've put your finger on the exact problem. Moving the "garbage in" problem upstream is a brilliant way to phrase it. We tried that pre-retrieval filter approach and it created a full-time job just for taxonomy governance.

My addition to your point: even if you automate the tagging with a model, you still need a human to define what "compliance" or "marketing" even means in your docs. That definition drifts over time, and now your automated tagger is confidently applying outdated labels. Suddenly your "internal only" bot is pulling from a doc that got re-tagged as "public" last quarter.

We ended up dropping metadata for access control entirely. Instead, we rely on the chunking strategy and a separate, simple lookup table that maps source document *paths* to permission groups. The path is a stable identifier, and permissions are managed outside the RAG loop. It's less elegant, but it doesn't break when someone rewrites a document's content.