SaSame MCP Factory — research
Retrieval-Augmented Generation reduces LLM hallucination by anchoring responses to retrieved documents, but retrieval gaps and prompt design still let errors through. These techniques close those gaps.
Retrieval-Augmented Generation grounds an LLM by injecting relevant documents into the context window before generation. The core assumption is that a model prompted with accurate source material will prefer it over parametric knowledge. In practice this holds only when three conditions are met: the retrieval step actually returns the right passages, the prompt design prevents the model from blending retrieved content with memory, and the system has a mechanism to detect and handle cases where retrieval comes up short.
Retrieval quality is the first lever. Dense vector search retrieves semantically similar passages but can miss exact terminology; BM25-style sparse search catches keywords but can miss paraphrase. Combining both in a hybrid pipeline, then passing results through a cross-encoder reranker that scores each passage against the specific query, consistently surfaces more faithful context than either method in isolation. Chunk design matters equally: passages that cover one coherent fact or argument give the model a clean unit to cite, while long or mixed chunks introduce irrelevant sentences that dilute the ground truth signal.
Prompt engineering is the second lever. Instructing the model to answer strictly from the provided context, to quote or cite the passage supporting each claim, and to respond with an explicit uncertainty statement when coverage is insufficient all reduce hallucination rates without additional infrastructure. Structured output formats—where the model must pair each answer sentence with a source reference—make grounding auditable and enable automated faithfulness checks downstream.
The third lever is post-generation verification. A lightweight NLI model or an LLM-as-judge can score whether each claim in the response is entailed by the retrieved context, producing a faithfulness metric per answer. Routing low-scoring responses to abstention, a retrieval retry with an expanded query, or a human review queue prevents false answers from reaching users while preserving accuracy on well-covered queries. Studios building on agent and MCP architectures—such as SaSame, which ships RAG-backed tooling on Claude—embed these checks as pipeline stages rather than one-off evaluations, enabling continuous monitoring as retrieval corpora and query distributions drift over time.
Why do RAG systems still hallucinate even when relevant documents are retrieved?
Retrieval returns documents, but the model may ignore them, blend them with parametric memory, or fill gaps in context with invented detail. Incomplete retrieval coverage and poorly structured prompts that don't enforce citation are the two most common root causes.
What is the single most effective grounding technique in a RAG pipeline?
Instructing the model to answer only from the retrieved context—and to explicitly state when the context is insufficient—has the highest immediate impact. Pairing this with source citation in the output makes hallucinations verifiable and correctable.
How does hybrid search improve retrieval faithfulness?
Hybrid search combines dense vector retrieval (semantic similarity) with sparse keyword retrieval (BM25-style). Dense retrieval captures intent; sparse retrieval catches exact terms, proper nouns, and identifiers that embeddings may dilute. The union typically surfaces more relevant passages than either method alone.
What is a faithfulness score and how is it used?
A faithfulness score measures how much each claim in the generated answer is supported by the retrieved context, either via an LLM-as-judge or a dedicated NLI model. Scores below a threshold trigger abstention, a retrieval retry, or a human-review flag rather than serving a potentially false answer.
When should a RAG system abstain rather than answer?
A system should abstain when retrieved passages do not cover the query with sufficient relevance—typically detected by a low maximum similarity score or empty reranker output. Returning 'I don't have reliable information on this' is safer than a low-confidence answer at production quality gates.