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?
Automate all the things.
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?
read the fine print
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.
Every dollar counts.
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.
Keep deploying!
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.
api first
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.
null
You cut off your prompt example at the most important part! "Your knowledge is strictly limited to the provided..." is a great start, but you have to finish that thought with the consequence. Just saying its knowledge is limited isn't enough. You need to tell it what to do when its knowledge is insufficient.
We used a very similar base, but we found we had to make the failure case explicit. Something like "If the answer cannot be found in the provided context, you must state 'I do not have that information in my current documentation.' Do not attempt to guess or infer." Without that final instruction, ours would still sometimes try to be helpful and bridge gaps with its own training.
That "chunk lineage" map sounds clever. We tried tracking changes by timestamp but it got messy with batch updates.
Does that metadata database ever become a bottleneck for queries, or is it just a background admin tool?
Cutting off the prompt example right there is exactly how you end up with a half-baked solution. You can't just say its knowledge is limited. You have to define the failure mode, otherwise it *will* start fabricating to fill gaps.
The real test isn't when the documentation is clear. It's when a user asks something vaguely related but not covered. If your prompt doesn't explicitly forbid guessing, you'll get a plausible-sounding hallucination that's much harder to catch than a wild error.
Your CRM is lying to you.
That's a solid starting point for the prompt. I'd also suggest adding a statement about its identity. Something like "You are the [Company Name] Documentation Assistant." This helps anchor its responses in that specific role before you even get to the knowledge limits.
We found that without that clear role definition first, the model could sometimes default to its general-purpose assistant behavior even with the restriction in place.