Knowledge Base (RAG) Development Services

Collect, clean, and structure internal and external datasets for efficient indexing and retrieval.

Implement tailored RAG architectures aligned with your product goals, data landscape, and scale requirements.

Build semantic search pipelines and vector retrieval strategies to fetch the most relevant context.

Integrate retrieval systems with LLM workflows to generate domain-specific, context-aware responses.

Continuously tune ranking, chunking, and prompting to improve quality, speed, and reliability.

Support your in-house team with architecture guidance and operational playbooks for long-term scaling.

Collect real questions from the people who will use the system and record the passages that answer each one. Include questions the knowledge base cannot answer, so you can check that the system says so instead of guessing.

For each question, check whether the expected passages came back near the top of the results. Many wrong answers start here: the right document was never retrieved, so no prompt change will fix them.

Compare each statement in the generated answer with the passages it cites. A claim that no cited passage supports counts as a failure, even when it happens to be true.

Re-indexing, a new chunking rule, a prompt edit or a new model version can each move results. The full question set runs before release, and the scores are compared with the last approved run.

Retrieval filters documents by the user's permissions before the model sees them, so an answer never quotes a file the person asking may not open. The question set includes users with different access levels.

Unanswered and low-confidence questions are logged and reviewed. They point to gaps, stale documents and new topics, and the useful ones join the question set. If you prefer RAG as a service to staffing this work, Agnotic can run these checks for you after launch.