RAG Without Hallucinations: Enterprise Knowledge Layer¶

RAG is the most practical way to bring AI into an enterprise environment safely. If you want results without hallucinations and with traceability, build the knowledge layer as a product — not as a side effect of a chatbot.

1 Knowledge Inventory¶

Before you start indexing, you need to know what you have. Where are the documents? Who owns them? How do you tell if a document is valid — and not an outdated version from 2019?

An inventory is not a technical step — it’s an organizational one. Talk to the people who create and use the documents. Find out where the authoritative sources are and where the copies of copies live.

• Map sources: SharePoint, Confluence, Git, internal wiki, emails, tickets
• Identify owners: who creates the document, who approves, who updates
• Define validity criteria: date, version, status (draft/approved/archived)
• Decide what belongs in RAG and what doesn’t — not every document is suitable

2 Classification and Access Rights¶

Every document needs metadata. Without it, the knowledge base is just a pile of text that the model draws from without context — and that’s a recipe for hallucinations and security incidents.

Metadata enables filtering retrieval: a user from department A must not receive answers from department B’s documents. Sensitivity, department, validity, and language are the minimum.

• Sensitivity: public, internal, confidential, strictly confidential
• Department: HR, finance, legal, product, engineering
• Validity: current, archived, draft
• Language: CS, EN, DE — retrieval must respect the language context
• Access rights in RAG must match access rights in the source system

3 Chunking and Context¶

Bad chunking is the most common cause of poor answers. Splitting a document into 500-token pieces without regard for structure is like cutting a book with scissors and hoping the excerpts will make sense.

A good chunk is self-contained — it makes sense even without surrounding text. At the same time, it needs context: which document it comes from, what the chapter is, what’s above and below it.

• Split by document structure: headings, sections, paragraphs — not fixed token count
• Each chunk must be understandable on its own
• Add surrounding context: document title, parent section, date
• Overlapping chunks: 10–20% overlap prevents context loss at boundaries
• Test: display your chunks and ask — “does this make sense without context?”

4 Indexing and Retrieval Strategy¶

Vectors alone aren’t enough. Vector search is strong for semantic similarity but weak for exact terms, numbers, and specific names. That’s why you need a hybrid approach.

A combination of vector search and full-text search with metadata filters is the standard. You tune the weights between them based on the types of queries your users make.

• Vector search: semantic similarity, good for “how” and “why” questions
• Full-text search: exact terms, product names, contract numbers, codes
• Metadata filters: department, language, validity — narrow the retrieval scope
• Re-ranking: cross-encoder or LLM-based re-ranking of top-K results
• Track recall and precision — not just “did it return something”

5 Source Citations and “I Don’t Know”¶

Every answer must have a source. The user must see which document the AI drew from — and have the ability to verify it. An answer without a citation is a claim without evidence.

And when retrieval returns no relevant results? The agent must say “I don’t know” or “I don’t have enough information.” Hallucinations arise when the model answers even without supporting material — and that’s exactly what you want to prevent.

• Every answer = text + citation (document name, section, link)
• Confidence score: if retrieval returns low scores, don’t answer
• Fallback: “I don’t have enough information for this question. Try contacting [department].”
• Never answer from the model’s “general knowledge” — only from sources in the knowledge base

6 Evals¶

You have two types of evals: retrieval tests and answer tests. You need both. Retrieval eval measures whether the system returns the right documents. Answer eval measures whether the model generates the correct answer from them.

A golden dataset is the foundation: a set of questions → expected source documents → expected answers. Without it, you don’t know if the system is improving or deteriorating.

• Retrieval evals: “For this query, it must return these documents” (precision, recall, MRR)
• Answer evals: “The answer must contain this information” (faithfulness, relevance)
• Golden dataset: 50–200 pairs covering key scenarios
• Run evals after every change: new document, chunking change, model upgrade
• Automate: evals in the CI/CD pipeline, not manual testing

7 Operations¶

The knowledge layer is a product. And products are operated: monitoring, maintenance, optimization. Deploying RAG and “letting it run” is a path to gradual quality degradation.

Track what users ask about. Identify top queries (to optimize them), unsuccessful queries (to add sources), and costs (to manage the budget).

• Monitoring: response time, retrieval quality score, error rate
• Top queries: what users ask about most often — optimize these paths
• Unsuccessful queries: where the agent says “I don’t know” — missing documents or bad retrieval?
• Costs: embedding costs, LLM calls, storage — per-request and overall
• Versioning: the knowledge base has a release cycle like software
• Regular review: are documents current? Have new sources appeared?

Conclusion¶

RAG without hallucinations is not about a better model. It’s about a better system around the model: clean sources, proper metadata, good chunking, hybrid retrieval, mandatory citations, and continuous evals. Build the knowledge layer as a product — with its own team, metrics, and release process.