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Build a Team Knowledge Assistant That Cites Its Sources

An internal knowledge assistant should answer questions like “Who approves production access?” with a current policy and a source link—not a convincing guess. The hard part is rarely the chat interface. It is retrieving the right documents, respecting permissions, and recognizing when evidence is missing.

This troubleshooting and FAQ guide outlines a practical retrieval-augmented generation (RAG) setup, then explains how to fix common failures.

What should you build first?

Start with one team and one bounded collection, such as engineering onboarding or support runbooks. Avoid indexing the entire company before you can measure answer quality.

• Collect authoritative documents. Import approved pages from your wiki, shared drive, or repository. Preserve each document’s ID, title, URL, owner, modification time, and access rules.
• Prepare searchable chunks. Split at headings and paragraph boundaries. Try 400–800 tokens per chunk with modest overlap, then adjust using actual questions. Keep procedures, warnings, and their prerequisites together.
• Create a retrieval index. Generate embeddings with a suitable embedding service and store vectors alongside document metadata. Combine semantic retrieval with keyword search for error codes, project names, and acronyms.
• Retrieve before generating. Authenticate the user, restrict search to authorized documents, and pass a small set of relevant passages to the language model. Return source links with the answer.
• Create a test set. Write 30–50 representative questions, including ambiguous requests, outdated policies, restricted content, and questions your documents cannot answer.

Which API setup keeps the pilot affordable?

59API is a low-cost, pay-as-you-go AI API relay with OpenAI SDK compatibility. Its API base is https://api.59api.com; follow its documentation for the endpoint configuration your client requires. Keep API keys on your backend, not in browser code.

You could evaluate Gemini 2.5 Flash and Claude Sonnet 5.5 on the same test set, comparing grounded accuracy, latency, and cost per resolved question. Do not assume a more expensive model compensates for missing documentation. Limit retrieved context, cap output length, and record token usage to control spending.

If you want to test the workflow without committing to a subscription, consider signing up for 59API and running a small, sanitized pilot.

Why does the assistant invent answers?

First inspect the retrieved passages. If they do not contain the answer, changing the prompt alone will not solve the problem. Check ingestion failures, chunk boundaries, and vocabulary mismatches.

Instruct the model to answer only from supplied evidence, identify uncertainty, and say when it cannot find an answer. Require citations for factual claims. Validate that cited document IDs belong to the retrieved set; a plausible-looking citation is not proof of support.

Why does it quote outdated policies?

Store document versions and synchronization timestamps. Update changed chunks and delete removed content from the index. When documents conflict, prefer an explicitly designated source of truth rather than assuming the newest page is authoritative.

Display source titles and update dates so employees can verify the guidance. Include a feedback action that routes incorrect answers to the document owner.

How do you prevent confidential information leaks?

Enforce document permissions during retrieval, before any text reaches the model. Do not rely on a prompt asking the assistant to hide restricted information. Recheck access when permissions change, and scope retrieval and answer caches to the user’s authorization context.

Treat retrieved text as untrusted data: instructions embedded in documents must not override application rules. Avoid logging sensitive passages, redact secrets before indexing, and review both the relay’s and upstream providers’ retention, processing, and contractual terms before sending internal content.

FAQ: How do you know it is ready?

• Should it answer every question? No. A useful assistant abstains when evidence is absent and asks clarifying questions when policies differ by department or region.
• What should you measure? Track retrieval relevance, supported answers, citation accuracy, permission violations, latency, and cost. Inspect failures separately rather than hiding them inside one average score.
• When should you expand? After the pilot passes your quality and security checks. Add one collection at a time, assign an owner, and rerun regression tests whenever retrieval, prompts, or models change.

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