SaSame MCP Factory — research
Concrete, implementable techniques—retrieval quality, citation enforcement, verification passes, and abstention—that reduce hallucination in retrieval-augmented generation systems.
Retrieval-augmented generation reduces hallucination by giving a model external context to draw on, but it does not eliminate the problem. Models can still ignore retrieved passages, misread them, or blend them with prior training knowledge to produce confident but unsupported claims. Effective grounding requires treating retrieval quality, generation constraints, and post-hoc verification as three separate levers, rather than assuming retrieval alone solves the issue.
On the retrieval side, precision usually matters more than volume: well-chunked source documents, hybrid keyword-plus-semantic search, and a reranking step that filters out low-relevance passages before they reach the model all reduce the raw material available for hallucination. On the generation side, prompting the model to cite or quote specific source spans for each claim—and to explicitly decline when context is insufficient—constrains outputs to what the evidence actually supports.
A verification layer adds a further check: after generation, a separate pass (a second model call, an entailment classifier, or a claim-extraction pipeline) tests whether each statement in the answer is actually supported by the retrieved text, catching cases where the generator drifted from its sources. This is distinct from checking whether an answer is simply correct, since a grounded answer inherits any errors already present in the source material—so source freshness and quality remain part of the grounding problem, not separate from it.
These techniques compose: tighter retrieval reduces the surface area for error, citation requirements make errors visible, and verification catches what slips through. Teams building or evaluating AI tooling—including agent and MCP-based systems, where SaSame's AI-native studio work sits—increasingly treat this as a pipeline design problem rather than a single model setting to tune.
What causes hallucination in RAG systems if retrieval is supposed to ground the answer?
Hallucination still occurs when retrieved context is irrelevant, incomplete, or contradictory, and the model fills gaps with plausible-sounding but unsupported text. It also happens when the model ignores retrieved context in favor of parametric (training-time) knowledge, especially for confident-sounding but outdated or wrong claims.
Does adding more retrieved documents reduce hallucination?
Not necessarily. Beyond a certain point, more context increases the chance of irrelevant or conflicting passages diluting the signal, and long-context models can lose track of relevant details buried in the middle. Precision of retrieval (fewer, more relevant chunks) generally matters more than raw recall volume.
What is citation-based grounding and how does it help?
Citation-based grounding requires the model to attribute each claim to a specific retrieved passage, often with inline references or quoted spans. This constrains generation, makes unsupported claims easier to detect programmatically, and lets downstream systems or humans verify answers against source text.
Should a RAG system be able to say 'I don't know'?
Yes. Abstention (explicitly declining to answer, or flagging low confidence) when retrieved evidence is insufficient is a core grounding technique. Systems that are prompted or trained to answer only from retrieved context, and to say so when context is inadequate, produce far fewer confident-but-wrong answers.
How can hallucination be measured after deployment?
Common approaches include human or LLM-based faithfulness scoring (checking whether each generated claim is entailed by the retrieved context), automated claim-extraction-and-verification pipelines, and tracking user-reported corrections or low-confidence flags over time. Faithfulness (grounded in context) and correctness (factually true) are distinct metrics and should be evaluated separately.