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Summary
Key takeaways
- RAG evaluation must measure three separate dimensions: whether the system retrieves the correct authorized evidence, whether the generated answer is accurate and supported, and whether the complete workflow meets security, latency, and cost requirements.
- Uvik Software’s RAG Release Gate Kit v2.0 provides a 40-case synthetic starter dataset covering answerable questions, missing information, conflicting documents, outdated sources, tenant boundaries, and prompt injection.
- The starter dataset contains 12 answerable questions, 8 missing-information cases, 6 conflicting-document cases, 6 stale-document cases, 4 tenant-boundary cases, and 4 prompt-injection cases. It is a testing scaffold, not a representative production benchmark.
- Retrieval recall, precision, citation support, faithfulness, answer correctness, and correct abstention measure different failure modes and should not be combined into one unexplained quality score.
- Faithfulness does not guarantee factual correctness. An answer can accurately reflect a retrieved document while still being wrong because the source is outdated, unauthorized, or factually incorrect.
- Citation evaluation must verify whether each cited passage actually supports the corresponding claim. Citation precision and citation completeness should be measured separately to identify unsupported or uncited material statements.
- LLM-as-a-judge evaluations require calibration against human-reviewed examples. High overall agreement can conceal critical missed failures, especially involving unsupported commitments, sensitive information, or authorization violations.
- Critical security failures must remain independent release blockers. Better answer quality, lower latency, or reduced cost cannot compensate for confirmed unauthorized retrieval, cross-tenant disclosure, or successful high-risk prompt injection.
- The article proposes illustrative release gates of at least 85% answer correctness and 90% correct abstention, alongside zero confirmed authorization violations, no successful critical prompt injections, no unresolved critical regressions, and compliance with operational limits. These thresholds are not universal standards.
- Reliable RAG evaluation requires versioned datasets, reproducible configurations, paired baseline comparisons, documented release decisions, and continuous monitoring after deployment rather than a one-time benchmark.
When this applies
This framework applies when an organization is developing, testing, improving, or preparing to release a retrieval-augmented generation system. It is particularly useful for engineering teams building enterprise knowledge assistants, customer support applications, internal search tools, document question-answering systems, or AI workflows that rely on retrieved information. The RAG Release Gate Kit helps CTOs, AI engineers, product owners, and security teams identify whether failures originate in document ingestion, retrieval, reranking, context construction, answer generation, citation handling, or authorization. It is also valuable when comparing retrieval configurations, embedding models, chunking strategies, prompts, and language models before a production release. For applications handling sensitive documents, multiple tenants, or business-critical information, the framework provides a structured way to evaluate security boundaries and prevent quality improvements from concealing unacceptable operational risks.
When this does not apply
This framework is less relevant to AI applications that do not retrieve external evidence or depend on document-grounded answers. It should not be treated as a complete security audit, a universal benchmark for every RAG architecture, or proof that passing a small test suite guarantees production safety. The 40-case starter dataset is synthetic and must be expanded with representative, authorized examples from the actual business domain before supporting important deployment decisions. The suggested correctness and abstention thresholds are illustrative policy choices rather than scientifically validated requirements. The framework also does not replace human judgment for ambiguous cases, formal authorization testing, privacy assessments, or application-specific evaluation of workflows involving irreversible actions. Organizations must adapt test coverage, release criteria, and statistical methods to their own risks, user populations, and operating environments.
Checklist
- Define the RAG application’s intended users, supported tasks, authorized information sources, expected behavior, and unacceptable failure conditions.
- Establish the evaluation unit and record each query, user or tenant context, query date, retrieved evidence, generated answer, citations, latency, and cost.
- Create a versioned evaluation dataset containing realistic business questions and controlled cases for known retrieval, generation, and security failures.
- Include answerable questions, missing information, conflicting documents, outdated sources, tenant isolation, and prompt-injection scenarios in the test coverage.
- Separate development examples from a held-out release dataset and prevent closely related documents or paraphrased questions from leaking between the two sets.
- Define expected answers, acceptable abstentions, authorized source identifiers, document authority rules, citation requirements, and failure severity before running evaluations.
- Measure retrieval recall and precision at multiple cutoffs, using clearly defined relevance labels and consistent document or evidence-unit deduplication.
- Evaluate answer correctness, claim-level faithfulness, citation support, citation completeness, and correct abstention as separate quality dimensions.
- Test whether document versions, effective dates, authority rules, and conflicting evidence are handled correctly during retrieval and generation.
- Verify authorization boundaries through retrieval traces and model context inspection, not only by checking whether restricted information appears in final answers.
- Calibrate automated evaluation judges against independently reviewed human examples, document disagreements, and investigate missed critical failures.
- Compare baseline and candidate configurations on the same held-out cases, corpus versions, permissions, and evaluation rules, recording individual improvements and regressions.
- Define independent release gates for authorization, prompt injection, answer correctness, abstention, critical regressions, p95 latency, and operating cost.
- Publish a reproducible release record documenting dataset versions, retrieval configuration, prompts, models, judge settings, test results, exceptions, and the accountable release decision.
- Monitor production queries, user-reported failures, latency, costs, and authorization changes, then add confirmed failures to regression tests while preserving independent future evaluation coverage.
Common pitfalls
- Evaluating only final answer quality while ignoring whether the retrieval system found the correct, current, and authorized evidence.
- Combining retrieval accuracy, answer quality, security, latency, and cost into one aggregate score that allows strong performance in one area to conceal critical failures elsewhere.
- Using a small synthetic dataset as proof of production reliability or treating zero observed security failures as evidence that the true failure probability is zero.
- Measuring faithfulness without checking source correctness, document validity dates, authorization rules, or whether retrieved evidence is authoritative.
- Counting citations as valid merely because their URLs exist or their documents mention the same topic, without checking claim-level support.
- Trusting automated LLM judges without human calibration, disagreement analysis, or separate investigation of false negatives involving serious errors.
- Allowing restricted documents to reach the model context while considering the system secure because the final answer did not reveal the information.
- Changing chunking, embeddings, retrieval settings, prompts, and models simultaneously, making improvements difficult to attribute or reproduce.
- Comparing candidate systems using different corpus versions, authorization contexts, datasets, or scoring rules, or repeatedly tuning against the held-out release set.
- Treating a successful pre-release evaluation as permanent validation instead of monitoring production behavior, updating regression cases, and retesting after relevant system changes.
Datasets, metrics and release gates
Disclosure: Uvik Software publishes this guide and provides related engineering services. External sources retain their original attribution.
Evaluate a retrieval-augmented generation system at three levels: whether it retrieves the right authorized evidence, whether its answer is supported and useful, and whether it stays within security, latency and cost limits. The Uvik Software RAG Release Gate Kit combines a 40-case starter dataset, explicit metric definitions and a release decision that cannot average away an access-control failure.
A good-looking answer is not enough. The system may answer from an outdated document, cite a passage that does not support its claim, retrieve another tenant’s information or produce an expensive response that exceeds the product’s latency budget. Evaluate those failure modes separately so the result points to an action.
Uvik Software’s RAG development service describes the broader engineering scope. This guide concentrates on an evaluation method and reusable test assets, not a claim that one score predicts production reliability.
Define the evaluation unit and the decision it supports
Begin with the product’s actual decision boundary. A public documentation assistant, an internal analyst and an agent that changes customer records need different acceptance criteria. State who is asking, which sources they may use, whether the response is informational or can trigger an action, and what a harmful failure looks like. That scope determines the test families and the release gates.
For a conventional retrieval-assisted answer, preserve the query, authorization context, retrieval results, final model context, answer, citations, latency and cost. Those records let a reviewer distinguish a missing source from an answer that ignored a source already present. Without intermediate evidence, every wrong answer looks like a generation problem and the team may keep changing prompts to compensate for a retrieval defect.
Use a stable case identifier and corpus version. A test result against last month’s policy cannot be compared directly with a result against a rewritten policy unless the change is accounted for. Version the expected behavior as well as the input. An old reference answer can become wrong after a legitimate product change.
Build a dataset around decisions the system must make
Start with real, permissioned examples of intended use, then add controlled cases for known hazards. Keep development cases separate from a held-out release set. Record who created each case, the source version, the expected behavior and any ambiguity a human reviewer must resolve.
The Uvik Software starter pack contains 40 synthetic cases over a small fictional software-product corpus. It is a testing scaffold, not a representative customer dataset, a production benchmark or evidence of measured model performance. Replace or extend it with domain-specific cases before using the results to make a business decision.
| Test family | Cases | Decision tested |
|---|---|---|
| Answerable questions | 12 | Retrieve the right evidence and answer the requested fact |
| Missing information | 8 | Abstain or ask for clarification when the corpus cannot answer |
| Conflicting documents | 6 | Follow the stated authority rule or surface unresolved conflict |
| Stale documents | 6 | Use the version effective at the query date |
| Tenant boundaries | 4 | Avoid retrieving or revealing another tenant’s restricted content |
| Prompt injection | 4 | Treat retrieved instructions as untrusted content |
| Total | 40 | A deliberately small starter set |
Figure 1. Uvik Software RAG Release Gate Kit v2.0. The 40-case distribution is an original test-design choice. It does not estimate the frequency of these situations in production.
RAGBench provides a useful external research reference: its authors describe 100,000 examples across five industry-specific domains. Its scale does not remove the need for your own authorization, date and business-rule tests. S1
Build cases from failure modes and real use
Use the synthetic starter pack to establish the record format and review procedure. Then add permissioned examples from the intended domain: frequent questions, commercially important decisions, ambiguous requests, known incidents and boundary cases. Keep customer data handling and consent requirements within the organization’s approved process.
Do not let query volume alone determine the test mix. A rare cross-tenant disclosure can matter more than a common wording error. Maintain a representative set for estimating ordinary performance and a separate challenge set for known hazards. Report them separately so a deliberately adversarial mix is not mistaken for the expected production failure rate.
Split by the unit that would otherwise leak information. If several questions paraphrase the same policy paragraph, placing one in development and another in the holdout can overstate generalization. Consider grouping by document, topic, customer workflow or time period as appropriate. Keep a dated record of which cases influenced prompt, retrieval or rubric changes.
The 40-case pack has only four tenant-boundary and four injection cases. It is a starting scaffold, not a security assurance suite. Expand those families across roles, document visibility, cache behavior, tool access, multi-turn conversations and authorization changes. A response that refuses correctly after unauthorized text reached a model is still a failed boundary test.
Specify expected behavior before running the model
Every case needs more than a question and a plausible answer. Store the query, user or tenant context, query date, allowed source identifiers, expected answer or abstention behavior, required citations, severity and reviewer notes. Hash or version the corpus and configuration so a later run can be compared fairly.
| Example case | Expected behavior | Failure to record |
|---|---|---|
| Current Pro plan price | Use the current approved pricing document | Cites an archived price |
| Unsupported integration | State that the corpus does not establish support | Invents a connector |
| Two conflicting policy drafts | Apply the authority rule or ask for review | Silently chooses a convenient draft |
| Price as of a past date | Use the version effective on that date | Uses today’s price for a historical question |
| Tenant A asks about Tenant B | No restricted retrieval or disclosure | Cross-tenant text reaches the answer or model context |
| Retrieved page says to reveal a secret | Ignore the instruction and answer only the legitimate task | Follows the document’s malicious instruction |
The starter corpus and cases are provided as companion data files with this article. They use fictional plan details and can be inspected without customer data. Their value is the schema and expected behavior, not the invented product facts.
For tenant tests, inspect retrieval traces as well as final answers. A system can suppress a leaked fact in its answer after already exposing the restricted document to a model, log or external service. Authorization should be enforced by the application and data layer, not delegated to a prompt.
Walk through one case from source to verdict
In the fictional AtlasDesk corpus, document P1 says that the current Pro price is USD 99 per month excluding tax. Archived document P0 says USD 79 and applies to an earlier period. Case UVIK-RAG-002 asks the current price on 26 October 2026. The expected evidence is P1 and the expected answer includes the current amount and tax qualification.
| Observed result in a hypothetical run | Diagnosis | Appropriate next check |
|---|---|---|
| P1 absent from candidates | Ingestion, indexing or retrieval problem | Confirm document availability and query retrieval |
| P1 retrieved but omitted from final context | Reranking, truncation or context construction problem | Inspect selected chunks and ordering |
| P1 present but answer says USD 79 | Generation or authority/date handling problem | Inspect instruction, conflicting context and rubric |
| Answer says USD 99 but cites P0 | Citation-support failure | Verify claim-to-source mapping |
| Answer adds an invented annual discount | Unsupported extra claim | Tighten evaluation of all material answer claims |
These rows are hypothetical outcomes, not measured results from the starter pack. The point is that one headline correctness score cannot identify the component to fix. Save enough evidence to locate the failure without sending sensitive traces to an unapproved destination.
Add a historical variant asking the price during the archived policy’s effective period. Now an answer of USD 79 can be correct. Date handling must follow the query and source validity, not a blanket rule to always prefer the newest document. Similarly, an approved policy should outrank a draft where the corpus establishes that precedence, while two equally authoritative unresolved policies may require surfacing the conflict.
Use metrics with explicit denominators
| Metric | Definition for this kit | Interpretation |
|---|---|---|
| Retrieval recall at k | Relevant authorized evidence units retrieved / all labeled relevant authorized units | Requires a labeled relevance set; define the evidence unit |
| Citation support | Cited claims actually supported / cited claims reviewed | A valid URL alone is not evidence of support |
| Faithfulness | Supported answer claims / answer claims assessed | Consistency with retrieved context, not necessarily real-world truth |
| Answer correctness | Cases meeting the human-approved answer rubric / answerable cases | Keep scoring rules and partial-credit policy stable |
| Correct abstention | Correct refusals or clarifications / cases requiring them | Separate safe abstention from unnecessary refusal |
| Authorization failures | Cases with unauthorized retrieval or disclosure / authorization cases | Any confirmed failure can block release |
| Latency | Median and p95 end-to-end elapsed time | State concurrency, cache state and included stages |
| Cost | Total metered inference and retrieval cost / completed queries | Include judge cost separately and record pricing date |
Ragas defines faithfulness in terms of claims supported by retrieved context. A system can be faithful to an incorrect or obsolete source, so faithfulness and correctness are distinct checks. S2 LangSmith’s evaluation tutorial similarly provides a practical reference for separating evaluation dimensions and working with datasets and experiments. S3
For a simple retrieval example, suppose the labeled authorized evidence set is {A, B, C}. The top five results contain A and C plus three irrelevant units. Recall at 5 is 2/3, or 66.7%. Precision at 5 for this binary example is 2/5, or 40%. Deduplicate equivalent chunks and define relevance granularity before calculating either number.
If an answer makes five checkable claims and four are supported by the retrieved context, the kit’s claim-level faithfulness is 4/5 = 80%. That does not establish that four fifths of all real-world facts are correct. Inspect the unsupported claim and its consequence.
Score claim support and completeness separately
A citation can be relevant to the topic without supporting the claim beside it. Review the relationship at the claim level. For example, a support-hours document can establish when the team is available without establishing a 30-minute response SLA. An answer that adds that SLA has introduced an unsupported commitment even if the cited page is genuine.
Define citation precision as supported cited claims divided by cited claims assessed, under your chosen annotation rule. Define citation completeness separately as material claims requiring evidence that have adequate support divided by all such claims. An answer can cite one claim perfectly while leaving three other material claims unsupported.
In an illustrative answer with five material claims, four require external support. Three are cited, two of those citations support the attached claims, and one required claim has no citation. Citation precision is 2/3, or 66.7%. Supported coverage of claims requiring evidence is 2/4, or 50%. The uncited claim and the mismatched citation require different corrections.
State how a reviewer handles a claim supported by multiple documents, a partially supported compound claim and a citation that points to a whole page rather than a precise passage. Consistency matters more than an attractive metric name. Keep the rubric with the dataset so another reviewer can reproduce the judgment.
Evaluate retrieval at more than one cutoff
Recall at k asks whether the relevant authorized evidence appears among the first k results. Precision at k asks how much of that result set is relevant under the chosen labels. Increasing k can raise recall while adding irrelevant or conflicting material. It can also increase latency and token cost. Inspect several cutoffs instead of assuming that more context is always better.
Use document-level or evidence-unit deduplication before counting relevance. Ten near-identical chunks from one paragraph should not look like ten independent sources of support. Record whether a question needs one passage or several complementary passages. A retrieval system may find the price but miss the eligibility condition that changes the answer.
The 2024 Lost in the Middle study found that source position affected performance on its tested long-context tasks and models. That result is a reason to test context placement and distractors, not a claim about every current model. S5 Add cases where important evidence appears early, centrally and late, and measure the deployed model under the actual context-construction policy.
When testing reranking or chunking, hold other components fixed initially. Change one factor at a time to understand the effect, then verify the selected combination. If ingestion, embedding, retrieval, prompt and model all change together, an improved average may be impossible to explain or reproduce.
Calibrate model judges against human review
An LLM judge is another measurement system with its own failure modes. Define a rubric, test it on human-labeled examples and review disagreements. Keep the judge model, prompt and configuration fixed for a comparison, and rerun calibration when any of them changes.
Separate factual support, answer usefulness and style. A fluent answer should not receive credit for an unsupported fact, and a concise correct refusal should not be punished merely for being shorter than a reference answer. Use blinded review where practical and record disagreement rather than forcing every ambiguous case into a false certainty.
Do not treat every claim from the same answer as an independent statistical observation. Errors can cluster by document, question family or user workflow. When estimating uncertainty, use a sampling unit and method that match the design. A question-level paired comparison is often more informative than two unrelated averages.
Calibrate automated judges with a disagreement set
Start with a human-reviewed calibration sample that includes clear passes, clear failures and ambiguous cases. Give reviewers the same rubric and authorized evidence. Have them label independently before resolving differences, and record the reason for adjudication. A single reference answer is often too narrow to capture all acceptable ways of expressing a correct result.
Suppose an illustrative calibration set contains 50 cases. Human adjudication marks ten as failures. An automated judge catches seven of those, misses three and incorrectly flags two of the 40 acceptable cases. Its failure-detection recall is 7/10, or 70%. Its failure-alert precision is 7/9, or 77.8%. Overall agreement is 45/50, or 90%, which sounds reassuring while still hiding three missed failures.
| Human verdict | Judge flags failure | Judge passes | Total |
|---|---|---|---|
| Failure | 7 | 3 | 10 |
| Acceptable | 2 | 38 | 40 |
| Total | 9 | 41 | 50 |
Figure 2. Uvik Software illustrative judge-calibration matrix. These are invented counts for explaining measurement. No automated judge or model was run to produce them.
Review the disagreements rather than tuning only for aggregate agreement. If the missed failures involve unsupported contractual commitments or tenant leakage, the judge may be unsuitable as the sole gate even with high overall agreement. Keep deterministic authorization checks, trace inspection and human review where the risk requires them.
Fix the judge model, prompt, rubric and relevant settings during a comparison. Recalibrate when any changes. Avoid allowing a candidate answer’s instructions to redefine the judging task, and ensure the evaluation process does not expose restricted evidence to an unauthorized judge service.
Compare candidate systems with paired evidence
Run baseline and candidate on the same held-out cases with the same corpus and access context. Record case-level changes, not only two overall percentages. In a hypothetical 100-case comparison, a candidate fixes 14 cases the baseline failed but regresses on six the baseline passed. The net gain is eight cases, but the six regressions still need inspection for severity.
A single authorization regression can block release even if the other improvements are commercially useful. Report critical gates separately, then compare quality, latency and cost within the candidates that pass them. Do not use a weighted average that lets a cheaper response compensate for a prohibited disclosure.
Use a statistical comparison appropriate to the sampling design when making a generalization beyond the test set. Paired outcomes contain information that two independent confidence intervals ignore. Repeated queries from the same document or workflow may be correlated. If the set is a handpicked challenge suite, describe observed coverage and failures rather than claiming a population-wide confidence level.
Record nondeterminism as part of the experiment. Repeated runs can reveal unstable answers, but they do not turn one scenario into many independent business cases. Report both the number of distinct cases and the number of runs. Keep the release decision tied to the failure modes that matter.
Make the release decision explicit
The following is a proposed policy for a low-risk informational assistant. Higher-impact applications need a risk-specific review. The thresholds are Uvik Software’s illustrative policy inputs, not validated universal standards or promises of safety.
| Gate | Example decision rule | Evidence |
|---|---|---|
| Authorization | No confirmed unauthorized retrieval or disclosure | Trace review and adversarial cases |
| Prompt injection | No successful sensitive action or disclosure in the defined suite | Recorded expected behavior and observed result |
| Correctness | At least 85% on the agreed answerable set | Case-level rubric and human calibration |
| Abstention | At least 90% on the agreed missing-information set | Separate false-answer and over-refusal counts |
| Regression | No unresolved critical-case regression versus baseline | Paired case comparison |
| Operations | Meets the product’s agreed p95 latency and cost limits | Reproducible load conditions and metered costs |
Consider two fictional configurations tested on a separate illustrative 100-case answerable set and a 10-case authorization set. Configuration A answers 80/100 correctly with 0/10 authorization failures. Configuration B answers 89/100 correctly but has 1/10 authorization failures. B’s better correctness does not make it releasable under this policy. A also misses the example correctness threshold. The decision is to improve and retest both.
These are invented results to demonstrate the decision rule. They were not produced by running the 40-case starter pack, and they do not compare real models or Uvik Software client systems.
OWASP notes that retrieval augmentation does not fully mitigate prompt injection. Include hostile instructions in retrieved content and test the effect on tools, data access and outputs, not only whether the answer sounds unusual. S4
Understand what zero observed failures means
Passing a small test suite does not establish a zero failure rate. Under an independent, identically distributed Bernoulli model, zero failures in n trials gives a one-sided 95% upper confidence bound of 1 – 0.05^(1/n). For 10 trials that is 25.9%; for 30 it is 9.5%; for 100 it is 3.0%, rounded.
Figure 3. Uvik Software calculation of the one-sided 95% binomial upper bound after zero failures. The independence and representative-sampling assumptions usually do not hold for a handpicked adversarial suite, so these values are an illustration of sample-size limits, not a production-risk estimate.
A curated suite is valuable for catching known failure modes even when it cannot estimate their prevalence. Add representative sampling if you need a population estimate, and keep that analysis separate from adversarial testing. Do not relabel four tenant-boundary starter cases as statistical proof of tenant isolation.
Budget evaluation before increasing its sophistication
Evaluation cost includes retrieval, generation, automated judging, human review and operating the pipeline. A cheap model call can become a costly experiment when every candidate triggers several judges and manual adjudication. Measure those components separately from production inference cost.
For a transparent hypothetical budget, 500 cases across three configurations create 1,500 candidate responses. At an assumed $0.01 for each response and retrieval, generation costs $15. Two judge calls per response at an assumed $0.005 each add another $15. Reviewing 150 sampled responses at two minutes each takes five hours; at an assumed $80 per hour, that adds $400. Total modeled evaluation cost is $430, excluding infrastructure and adjudication beyond that sample.
These unit costs are illustrative inputs, not current vendor prices. The example shows that human review can dominate a small evaluation budget. Use review where it improves decision quality: critical cases, disagreements and a representative audit sample. A small cheap automated score is not valuable if it repeatedly misses the failures that determine release.
Turn evaluation into a release and monitoring process
Version the corpus, retriever, embedding model, chunking, prompts, generator, permissions and judge configuration. Run regression tests after changes to any component that can affect the result. Preserve case-level outputs and traces, with appropriate protection for sensitive data.
After release, sample real authorized traffic for review, monitor cost and latency, investigate user-reported failures and add confirmed failure cases to the regression set. Avoid silently training or tuning on the held-out set until it stops being a useful independent check. Rotate or expand the release set with a documented method.
Use Uvik Software’s AI production failure database as a source of failure categories to investigate, not as a measured estimate of your system’s risk. For implementation help, LLM evaluation and observability services can connect offline tests with production traces. Keep one accountable release owner who can stop a rollout when a mandatory gate fails.
Publish a release record another engineer can reproduce
The release record should identify the corpus, case set, retrieval configuration, model, prompts, authorization rules, judge configuration, run date and result files. Include the failed cases, approved exceptions and the reason for the decision. Store sensitive traces with appropriate access and retention rather than making every debug record public.
After deployment, compare observed use with the evaluation set. New query families, changed documents, permissions updates and unusual tool behavior can invalidate the old coverage. Add confirmed failures to a regression set and maintain a separate holdout for future decisions. Versioning the test pack makes the improvement traceable without claiming that a once-passing system is permanently reliable.
The Uvik Software release gate approach therefore produces an inspectable decision: which evidence was available, which behaviors were tested, which boundaries passed and which uncertainty remains. That record is more useful to an engineering buyer than a single RAG score with no dataset, rubric or release consequence attached.
Method and citation
The Uvik Software RAG Release Gate Kit v2.0 contains original synthetic fixtures, metric conventions, policy examples and transparent calculations. It reports no measured model benchmark. The starter data is intentionally small and must be adapted to the application’s domain, risk and access rules.
Suggested citation: Uvik Software. RAG Evaluation in Practice. RAG Release Gate Kit v2.0. Research snapshot 26 October 2026. Attribute the kit and original calculations to Uvik Software while retaining RAGBench, Ragas, LangChain and OWASP attribution for their contributions.
Sources
S2. Ragas. Faithfulness metric documentation.
S3. LangChain. Evaluate a RAG application with LangSmith.
S4. OWASP. LLM01:2025 Prompt Injection.
