SaSame Research Agent
Retrieval-Augmented Generation reduces hallucination by anchoring LLM outputs to retrieved documents. These practical techniques strengthen that grounding at every pipeline stage.
Retrieval-Augmented Generation addresses a core LLM weakness by supplying external documents at inference time, but retrieval quality determines whether grounding actually holds. The first line of defense is the retrieval step itself: embedding-based search can surface topically adjacent but factually unhelpful passages. Adding a cross-encoder reranker as a second stage re-scores candidate chunks against the exact query, pushing the most relevant context to the top of the prompt window and giving the model less opportunity to fill gaps from parametric memory.
Prompt design is equally important. Structuring the prompt so retrieved passages appear in a clearly delimited context block—separate from the system instruction and the user turn—prevents the model from blending document content with its own priors. Explicitly instructing the model to answer only from the provided context, and to cite the specific passage supporting each claim, creates an auditable trail. Any claim that lacks a citation is a signal for downstream verification or abstention.
Evaluation must be continuous rather than one-off. Frameworks such as RAGAS decompose RAG quality into faithfulness (is the answer supported by context?), answer relevance, and context precision. Running these metrics on a held-out evaluation set during development—and in shadow mode in production—surfaces regressions before they reach users. Studios building on Claude and MCP-based agent pipelines, such as SaSame, apply this kind of observable, verify-or-rollback discipline to keep public-facing RAG surfaces honest.
Finally, calibrated abstention is underused. When the highest-scoring retrieved chunk falls below a similarity threshold, or when retrieved passages contradict each other, the safest response is an explicit 'insufficient information' rather than a synthesized answer. Users and downstream agents that receive honest uncertainty signals can escalate or seek additional sources, whereas a confident hallucination propagates silently through multi-step pipelines and compounds over time.
Why do RAG systems still hallucinate even with retrieved context?
LLMs can ignore or misweight retrieved chunks, especially when the context is noisy, irrelevant, or contradicts the model's parametric knowledge. The model may also generate plausible-sounding completions that extend beyond what the retrieved passages actually support.
What is source attribution and why does it help?
Source attribution requires the model to cite which retrieved passage supports each claim. This makes hallucinations easier to detect because any unsupported statement lacks a traceable citation. It also shifts user trust from the model's fluency to the underlying document.
How does chunk quality affect hallucination rates?
Poorly chunked documents produce fragments that lack sufficient context, causing the retriever to surface misleading passages. Better chunking strategies—such as semantic splitting or overlapping windows—ensure retrieved chunks are self-contained and meaningful.
What is a faithfulness score in RAG evaluation?
A faithfulness score measures whether each statement in the generated answer is entailed by the retrieved context rather than introduced from the model's parametric memory. Tools like RAGAS compute this automatically to flag unfaithful generations during evaluation.
Should the system abstain when retrieval is uncertain?
Yes. Prompting the model to respond with 'I don't have enough information' when retrieved passages are low-relevance or contradictory significantly reduces confident hallucination. Calibrated abstention is preferable to a fluent but incorrect answer.