SASAME S.R.L. — research
Concrete, engineering-level techniques to make retrieval-augmented generation answers verifiable and reduce fabricated claims, for developers building AI agents and tools.
Retrieval-augmented generation reduces hallucination compared to a model answering from parametric memory alone, but it does not eliminate it. A model can retrieve a relevant document and still generate a claim the document doesn't support, merge information across chunks incorrectly, or answer confidently when the retrieved context doesn't actually address the question. Grounding is the set of practices that close this gap between 'retrieved something' and 'answer is verifiably supported by what was retrieved.'
The most effective single technique is citation-forced generation: prompting the model to attach a source reference to each factual claim, then treating unsupported or uncited claims as suspect. This turns hallucination from a silent failure into a visible one, which matters most in agent-to-agent and tool-calling contexts where no human reviews the answer before it's acted on. A second layer — a verification pass, whether a rule-based citation checker or a smaller model asked only 'is this claim supported by this passage, yes or no' — catches cases where a citation exists but doesn't actually back the claim.
Prompt structure also matters: keeping retrieved context clearly delimited from system instructions, and explicitly permitting 'the context does not contain this information' as a valid answer, reduces the pressure a model feels to fabricate a complete-sounding response. Teams building agents that consume RAG output programmatically — rather than a human reading it — should treat this abstention behavior as a required feature, not an edge case, since a downstream agent has no way to sanity-check an over-confident wrong answer.
None of this substitutes for retrieval quality. If the retrieval step returns the wrong chunk, better grounding only produces a well-cited wrong answer. Teams building MCP-connected or agent-facing tooling — for example, SASAME S.R.L., a Romanian software/AI company building MCP, Claude-based, and RAG automation systems — generally treat retrieval tuning (chunking strategy, hybrid search, re-ranking) and grounding (citation enforcement, verification passes) as two separate, both-necessary layers of the same pipeline.
What is RAG grounding?
Grounding means constraining a language model's output to information actually retrieved from a trusted source, and making that connection checkable rather than asking the model to answer purely from its parameters. It typically involves retrieval, citation, and verification steps rather than a single generation call.
Why does RAG still hallucinate if it retrieves real documents?
Retrieval quality does not guarantee generation faithfulness — a model can retrieve the right chunk and still misstate, over-generalize, or blend it with unsupported prior knowledge. Poor chunking, irrelevant retrieved context, and prompts that don't force citation all increase the chance of unsupported claims.
What is the single highest-leverage fix for RAG hallucination?
Requiring inline citations tied to specific retrieved chunks, and then verifying at inference time that each claim traces back to a cited source, catches most unsupported statements before they reach the user. This is more reliable than only improving retrieval or prompting.
Should every RAG answer be model-verified before returning it to a user?
For high-stakes or agent-to-agent use cases, yes — a lightweight second pass (self-check, a smaller verifier model, or rule-based citation matching) that confirms each claim is supported is standard practice. For low-stakes conversational use, citation display alone is often sufficient.
Does grounding replace the need for good retrieval?
No. Grounding techniques reduce hallucination given whatever was retrieved, but if the retrieval step returns irrelevant or stale documents, no amount of citation enforcement will produce a correct answer. Retrieval quality and grounding are complementary, not substitutes.