# RAG Accuracy Readiness Assessment: Will Your System Actually Be Accurate? > Source: https://netray.co/tools/rag-accuracy-readiness-assessment > Publisher: Netray (netray.co), AI and integration engineering for ERP and manufacturing systems. > Section: Free Tools ## Short answer Score your retrieval-augmented generation system across eight dimensions that actually predict production accuracy, from chunking strategy to groundedness verification. ## Category on-prem-ai ## Tool **Kind:** assessment **Questions:** #### Has your chunking strategy been tested against real questions, or set once and left alone? **Help:** Chunk size and boundaries are the single most common cause of poor RAG recall. **Options:** ##### Tested against a golden question set and tuned per document type **Score:** 10 ##### Tested informally against a handful of examples **Score:** 6 ##### Set once using a default value and never revisited **Score:** 2 ##### No conscious chunking decision was made **Score:** 0 #### Do you have a labeled evaluation set with known correct source passages for each question? **Options:** ##### Yes, 100+ questions with expert-labeled relevant passages **Score:** 10 ##### Yes, a smaller set of 20-50 questions **Score:** 6 ##### An informal list without labeled correct passages **Score:** 2 ##### No evaluation set exists **Score:** 0 #### Do you measure recall at k for your retrieval layer specifically, separate from final answer quality? **Help:** Most RAG failures are retrieval failures. Measuring only the final answer hides where the system actually broke. **Options:** ##### Yes, we track recall and precision at k on a defined schedule **Score:** 10 ##### We check whether the right document appeared in results, informally **Score:** 6 ##### We only look at retrieval when an answer looks wrong **Score:** 2 ##### We evaluate only final answers, never retrieval in isolation **Score:** 0 #### Does your pipeline include a reranking step after initial retrieval? **Options:** ##### Yes, a dedicated reranker scores and reorders candidates before generation **Score:** 10 ##### Yes, a simple heuristic reranker (recency, metadata boost) **Score:** 6 ##### No reranking, we pass raw vector search results directly **Score:** 2 ##### We are not sure what reranking is or whether we have it **Score:** 0 #### Do you measure groundedness, meaning whether the generated answer is actually supported by the retrieved passages? **Options:** ##### Yes, every answer is scored for citation support with a tracked hallucination rate **Score:** 10 ##### Yes, on a sampled basis periodically **Score:** 6 ##### We require citations but do not verify they are accurate **Score:** 2 ##### No groundedness measurement at all **Score:** 0 #### How current is your indexed content relative to the source documents? **Options:** ##### Near real-time; changes propagate to the index within minutes to hours **Score:** 10 ##### Within a day via a scheduled batch re-index **Score:** 6 ##### Weekly or on an ad hoc manual trigger **Score:** 3 ##### We are not sure how stale the index currently is **Score:** 0 #### Does retrieval respect the same access permissions as the source documents? **Help:** A common and serious failure mode: the assistant surfaces content a user could not see in the source system. **Options:** ##### Yes, entitlement-aware filtering happens at query time before generation **Score:** 10 ##### Partially, some document sets are excluded but not fully entitlement-aware **Score:** 5 ##### No, the corpus is indexed as a single flat permission space **Score:** 0 #### Is there a working feedback loop from production failures back into your evaluation set? **Options:** ##### Yes, flagged failures are triaged and added to the eval set on a regular cadence **Score:** 10 ##### Yes, reviewed and added periodically in batches **Score:** 6 ##### Feedback is collected but rarely turned into new test cases **Score:** 2 ##### No production feedback capture at all **Score:** 0 **Bands:** #### Not ready: accuracy is currently unmeasured **Min:** 0 **Max:** 24 **Verdict:** You cannot currently tell whether your RAG system is accurate, only whether it produces plausible-sounding answers. This is the highest-risk posture for a production launch, since failures will surface first through user complaints or, worse, a wrong answer someone acted on. **Recommendations:** - Build a 50-100 question golden set with subject matter experts labeling the correct source passages. - Add recall at k measurement for retrieval before touching chunking or reranking. - Do not launch to production users until groundedness is at least measured, even informally. - Verify retrieval respects source-system permissions before any broader rollout. - Assign a single owner for RAG accuracy rather than treating it as a side task. #### Early stage: real gaps remain **Min:** 25 **Max:** 49 **Verdict:** You have started measuring accuracy but coverage is thin or inconsistent, and at least one high-impact gap remains, commonly reranking, groundedness verification, or entitlement-aware retrieval. This is workable for a limited pilot but not for a broad production rollout. **Recommendations:** - Add a reranking step if you do not have one; it is one of the highest-leverage accuracy improvements available. - Expand your evaluation set to 100+ questions covering edge cases and out-of-scope queries. - Start tracking groundedness and hallucination rate as a headline, reported metric. - Close any gaps in entitlement-aware retrieval before expanding to a wider user population. - Establish a re-indexing cadence tied to how often source documents actually change. #### Solid foundation: tighten the details **Min:** 50 **Max:** 74 **Verdict:** You have the core pieces in place: measured retrieval, reranking, and some groundedness tracking. The remaining gaps are usually the feedback loop speed and index freshness. Closing them turns a good pilot into a system that keeps improving after launch instead of quietly drifting. **Recommendations:** - Shorten the production feedback cycle so flagged failures reach the evaluation set within a week. - Automate re-indexing so content freshness matches how quickly your source documents actually change. - Calibrate any automated groundedness scoring against human judgment on a sample. - Stress test entitlement-aware retrieval with adversarial queries designed to probe permission boundaries. - Document your accuracy metrics so business stakeholders can see the trend over time. #### Mature and production-ready **Min:** 75 **Max:** 100 **Verdict:** Your retrieval, reranking, groundedness, freshness, and access control practices are strong enough to support production use in a regulated environment. The work now is maintaining this discipline as the corpus and user base grow, not building new capability. **Recommendations:** - Template your evaluation harness so new document sets and use cases inherit the practice on day one. - Review evaluation set representativeness quarterly against real production query patterns. - Extend groundedness testing to any agentic or multi-step retrieval workflows you add. - Publish accuracy metrics to business stakeholders regularly to sustain trust and continued investment. - Periodically red-team the retrieval layer for prompt injection and permission bypass attempts. ## Content **Intro:** This free RAG accuracy readiness assessment scores your retrieval-augmented generation system across the eight dimensions that actually predict production accuracy: chunking validation, evaluation set quality, retrieval measurement, reranking, groundedness, index freshness, entitlement-aware retrieval, and the production feedback loop. It is built for AI engineers and platform leads who need an honest read on whether a RAG system is ready for real users before it ships. Most RAG accuracy problems are diagnosable and fixable, but only if the system is instrumented well enough to reveal which of these eight layers actually broke. **Sections:** #### Why RAG accuracy fails silently **Body:** Unlike a crashing application, a RAG system with poor accuracy keeps running and keeps producing fluent, confident-sounding answers, which is exactly what makes it dangerous. The failure hides inside one of a small number of layers: the wrong chunks were retrieved, the right chunks were retrieved but ranked too low to reach the model, the model was given the right context but generated an unsupported answer anyway, or the index was simply stale. Without measuring each layer separately, teams cannot tell which one is broken, and they end up tuning the wrong component while the real problem persists. #### The two failure modes that matter most **Body:** Retrieval failures and groundedness failures behave very differently and need separate detection. A retrieval failure means the correct source document never made it into the context the model saw, which no amount of prompt engineering can fix because the information genuinely was not available. A groundedness failure means the correct information was retrieved but the model generated an answer not actually supported by it, which is a hallucination masquerading as a cited fact. Measuring recall at k catches the first; measuring groundedness against retrieved passages catches the second. Most teams only check the second, informally, and miss the first entirely. **Bullets:** - Recall at k answers: did we retrieve the right document at all? - Groundedness answers: did the generated answer actually rely on what we retrieved? - A system can have perfect recall and still hallucinate if groundedness is not enforced or checked. - A system can have perfect groundedness and still be wrong if recall failed and the model answered from parametric memory instead. #### Entitlement-aware retrieval is the most overlooked risk **Body:** Security and accuracy converge at one specific point: if retrieval is not filtered by the requesting user's actual permissions, the assistant will happily surface restricted engineering drawings, supplier pricing, or personnel data to anyone who asks the right question, regardless of how well the rest of the pipeline performs. This is not a hypothetical edge case; it is the default behavior of a naively indexed corpus, and it is the single finding most likely to stop a production launch in a regulated industry once security review catches it. #### How Netray builds and validates accurate RAG systems **Body:** Netray builds retrieval systems for manufacturers where wrong answers have real operational consequences: misread specifications, incorrect part substitutions, or outdated procedures presented as current. We instrument all eight dimensions in this assessment before calling a system production-ready, build entitlement-aware retrieval that mirrors your ERP and PLM permissions, and set up a production feedback loop so real failures continuously strengthen the evaluation set. Engagements typically start with an accuracy audit of an existing pilot or a from-scratch evaluation harness for a new build. **Cta:** Get a full accuracy audit of your RAG pipeline with a prioritized plan to close the gaps that matter most. **Faq:** #### What is the difference between a retrieval failure and a groundedness failure? **Answer:** A retrieval failure means the system never fetched the document that actually contains the answer, so the model was working blind regardless of how it was prompted. A groundedness failure means the correct document was fetched and made available, but the model generated a response that goes beyond, or contradicts, what that document actually says. They require different fixes: retrieval failures point to chunking or reranking, groundedness failures point to prompting, generation constraints, or citation verification. #### How many questions should be in a RAG evaluation set? **Answer:** Start with at least 50 questions written or reviewed by subject matter experts, covering common queries, edge cases, and out-of-scope questions the system should decline to answer. Mature production systems typically maintain 100-300 questions, continuously expanded from real production failures. Fewer than 50 makes it hard to detect regressions reliably, since a handful of questions can pass or fail somewhat randomly between pipeline changes. #### Why does index freshness matter for accuracy, not just relevance? **Answer:** A stale index does not just miss new content, it actively serves outdated content as if it were current, which is arguably worse than missing it entirely because the answer looks complete and authoritative. A revised safety procedure, updated part specification, or superseded policy that has not been reindexed will be confidently presented to users as the current version. For any content type that changes on a predictable schedule, index freshness should match that schedule, not lag behind it by choice. #### Is entitlement-aware retrieval really necessary if the corpus is mostly internal documentation? **Answer:** Yes, because 'mostly internal' documentation still typically includes supplier pricing, unreleased product specifications, personnel-adjacent records, and export-controlled technical data mixed in with general reference material. If retrieval treats the corpus as a single flat permission space, any user who can query the assistant can effectively read anything in the index, which is a materially different, and usually worse, permission model than the source systems that content came from. ## Search terms RAG accuracy assessment, RAG evaluation readiness, retrieval augmented generation quality, RAG recall at k, groundedness testing RAG, enterprise RAG evaluation, RAG hallucination testing --- Talk to an engineer: https://netray.co/contact?intent=erp_ai | Machine catalog: https://netray.co/agent/catalog.json