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06/09/2026 9:02 am
TL;DR: Production RAG failures split across three distinct surfaces: retrieval quality, faithfulness, and groundedness, so a single accuracy score hides the real break point, according to Openlayer. The practical implication is that teams need separate measurement gates, because answer quality cannot be fixed until they know whether the retriever, the generator, or both are failing.
NHIMG editorial — based on content published by Openlayer: RAG Evaluation in Production, groundedness, faithfulness, and retrieval quality
By the numbers:
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems, inappropriately sharing sensitive data, and revealing access credentials.
- 96% of technology professionals identify AI agents as a growing security threat, and 66% believe this risk is immediate.
Questions worth separating out
Q: How should teams evaluate RAG systems without confusing retrieval failures with generation failures?
A: Measure the pipeline in stages.
Q: Why do groundedness and faithfulness need separate thresholds in production?
A: They fail in different ways and require different fixes.
Q: Should security teams use HyDE in a production RAG pipeline?
A: Only after measuring it on real traffic.
Practitioner guidance
- Split retrieval and generation gates Measure Context Precision, Context Recall, Mean Reciprocal Rank, groundedness, and faithfulness as separate controls so you can identify which stage failed before the response reaches users.
- Block low-confidence responses before release Set deployment gates that prevent unfaithful responses from leaving the API boundary and route sub-threshold groundedness responses to review instead of auto-delivery.
- Test HyDE against your own corpus Compare standard dense retrieval with HyDE on real queries, then check whether recall gains are offset by precision loss or by synthetic-answer hallucinations pulling the wrong chunks.
What's in the full article
Openlayer's full blog covers the operational detail this post intentionally leaves for the source:
- Threshold examples and gating logic for groundedness and faithfulness in production
- Component-level evaluation patterns for retrieval, including precision, recall, and MRR
- HyDE tuning considerations for short queries and domain-specific corpora
- Implementation examples for logging query, retrieved chunks, and response lineage
👉 Read Openlayer's analysis of groundedness, faithfulness, and retrieval quality in production RAG →
RAG evaluation in production: are your metrics catching the right failure?
Explore further
06/09/2026 9:02 am
RAG evaluation is becoming an identity control problem, not just a model quality problem. When AI systems answer from internal policy, access, or operational data, the output becomes part of the decision chain for human and machine identities alike. That means groundedness and faithfulness are governance signals, not just evaluation metrics. Practitioners should treat the response path as an access pathway to controlled information.
A question worth separating out:
Q: How should organisations govern AI systems that can make consequential decisions?
A: Organisations should govern consequential AI systems with the same discipline used for high-risk identities: defined ownership, least privilege, logging, approval boundaries, and human override. The critical requirement is to connect model behaviour to real access paths so legal review, security review, and audit evidence all describe the same system.
👉 Read our full editorial: RAG evaluation needs separate groundedness, faithfulness, and retrieval metrics
