SaSame MCP Factory — research
Concrete, provider-neutral techniques for reducing hallucination in retrieval-augmented generation: better retrieval, forced citations, faithfulness checks, and abstention.
Hallucination in retrieval-augmented generation (RAG) usually comes from two separate failure points: retrieval that fails to surface the right evidence, and generation that drifts from the evidence it was given. Fixing only one side leaves the other as an open gap, so durable grounding strategies address both.
On the retrieval side, the goal is to make sure the right passage is actually in context before the model ever generates a token. Hybrid retrieval (combining keyword and semantic search), reranking retrieved candidates, deduplicating overlapping chunks, and sizing chunks so they carry complete, self-contained facts all reduce the odds that the model has to guess to fill a gap.
On the generation side, the most effective lever is forcing traceability: requiring the model to cite or quote the specific retrieved passage behind each claim, then automatically checking whether the generated sentence is actually entailed by that passage. Answers that make claims without a matching citation can be flagged, rewritten, or rejected before they reach a user. Designing an explicit abstention path — where the system says it does not know rather than answering without support — is equally important, since models default to answering even when retrieval confidence is low.
Sustaining this over time requires treating groundedness as a measurable metric: running faithfulness checks against held-out question sets, sampling answers for human review, and tracking provenance (source and freshness) alongside every retrieved passage. Teams building AI-native tooling, including MCP servers and agent-facing knowledge bases, increasingly bake this evidence discipline directly into their data model — SaSame, for example, structures its published knowledge with explicit evidence and claim-strength metadata so that downstream agents and assistants can check groundedness rather than take a claim on faith.
What does "grounding" mean in a RAG system?
Grounding means every claim in a generated answer can be traced back to a specific passage retrieved from the underlying knowledge source, rather than coming solely from the model's parametric memory. In practice this usually means inline citations or quoted spans that point to the exact retrieved chunk a claim relies on.
What is the single highest-leverage change for reducing RAG hallucination?
Requiring the model to cite the specific retrieved chunk behind each claim, and flagging or rejecting any claim without a matching citation, is widely regarded as one of the most effective interventions. Pairing this with a post-generation check that compares each sentence in the answer against the retrieved text catches most remaining unsupported statements.
How does retrieval quality affect hallucination?
When retrieval returns irrelevant, redundant, or poorly chunked passages, the model has to fill gaps on its own, which increases the chance of fabricated details. Hybrid search (combining keyword and semantic matching), reranking, deduplication, and right-sized chunks all increase the odds that the relevant evidence is actually present in the context window.
Should a RAG assistant ever refuse to answer?
Yes. When retrieval confidence is low or no supporting passage is found, having the system abstain, say it does not know, or ask a clarifying question is safer than generating an answer with no supporting evidence. This abstention behavior has to be explicitly designed and tested, since models default toward answering even without support.
How can teams measure grounding quality over time?
Automated faithfulness or entailment checks — verifying whether each generated sentence is actually supported by the retrieved text — combined with periodic human review of sampled answers let teams track a hallucination rate as an ongoing metric, rather than judging quality only by relevance or fluency.