What It Is
The system searches a knowledge base, selects the most relevant passages, and adds them to the prompt so the model grounds its response in them. This lets the model use current or private information without being retrained.
Key Points
- Fresh and private data: no retraining required.
- Fewer hallucinations: answers rest on real sources.
- Citations: answers can point to their source.
- Pipeline: chunking, embedding, vector database, retrieval, re-ranking, prompt assembly.
Why It Matters
Quality depends on every step, so teams measure retrieval accuracy and answer faithfulness, not just fluency. A weak retrieval step undermines even a strong model, so each stage should be tested independently and tuned with real user questions.
How ClearLeaff Applies It
We engineer RAG pipelines with vector databases, careful evaluation, and LLMOps monitoring, giving enterprises accurate, traceable answers from their own data.