THE KEY ANSWER
RAG combines information retrieval with answer generation. The application locates fragments of relevant documents and passes them to the model as context. Quality depends on whether the appropriate, current, and user-accessible material is found.
What does the path from question to answer look like?
The user asks about a procedure. The system determines the scope of available sources, retrieves fragments, ranks the results, and passes the selected materials to the model. The answer should indicate what it is based on. This does not mean the company trained the model on all documents. Knowledge is retrieved during task processing.
When diagnosing an error, separate two questions: did the system find the right fragment, and did it use it correctly? If the procedure is not in the results, improving the answer style will not help much. If the fragment is correct but the conclusion is wrong, you must investigate the generation method and the scope of the context passed.
Context and references: Anthropic: Contextual Retrieval
Document order precedes retrieval
Start with a limited collection that has an owner and an effective date. Remove duplicates, withdrawn versions, and materials that no one can verify. Preserve headings, table structures, and the association of fragments with documents. Text extracted from a table may lose information about which product a value refers to.
Demonstration example: the service department has instructions for three generations of a device. A question without a model number should not end with a single universal procedure. The assistant can ask for clarification or present variants with a clear description. Device metadata may be more important here than increasing the number of retrieved fragments.
How to check retrieval quality?
Collect real questions along with the document containing the answer. Include abbreviations, product numbers, colloquial names, and typos. Compare retrieval by exact terms with semantic retrieval. In some collections, combining both methods better handles both symbols and descriptive questions simultaneously.
Do not evaluate solely by the number of returned results. Check whether the correct fragment is ranked sufficiently high and whether other results introduce contradictions. Additional ranking may help, but it costs time. Make the decision based on your own questions. Do not transfer percentages from a vendor's test to a knowledge base with a completely different structure.
Permissions and updates are basic features
The access filter should work before content is passed to the model. Hiding citations after the answer is generated does not undo the disclosure of information. Also check the cache, chat history, and exports. A user who lost access to a source should not regain it through an older answer shared between accounts.
Plan document deletion and refresh across the entire workflow. Show the user the source date when it affects the decision. Measure unanswered questions, irrelevant fragments, and escalations to experts. This list also suggests which materials are missing in the organization. The assistant can reveal a knowledge gap that was previously invisible.
WHERE TO START
Bring this into your project.
- Select an approved collection with an owner.
- Preserve document structure, version, and metadata.
- Evaluate retrieval and answer separately.
- Check access revocation and content deletion.
Choose one thing your process is missing today. It's a useful topic for your first conversation with the team.
QUESTIONS AND ANSWERS
Frequently asked questions.
Does RAG eliminate false answers?
No. It limits reliance on the model's memory, but the system can still find the wrong source or misinterpret it. Tests and sensible handling of missing information are needed.
Does all documentation need to be moved to a new system?
Not always. Content can be retrieved from existing sources via integration. However, synchronization, deletion, and permission mapping must be established so that the copy does not live independently of the original.
Sources and context
- Anthropic: Contextual Retrieval ↗
The material describes, among other things, combining semantic and lexical retrieval and adding context to fragments. We do not treat the results of this experiment as a forecast for ALGOV deployments.
Prepared by the ALGOV team. Current as of September 8, 2026. Examples describe possible scenarios, not results from client projects. How we create our guides.
