TL;DR
An LLM evaluation framework is the test data, metrics, scoring methods and release gates that decide whether an LLM system ships, and it should outrank benchmark scores. Use public benchmarks only to shortlist models, then decide with tests built from your own workloads. Score checkable answers with code, open-ended text with a calibrated LLM judge, and use human review to keep that judge honest. For RAG systems, measure faithfulness and context quality; for agents, measure tool calls and task completion. Wire the suite into CI/CD so a release ships only when every gated metric meets its threshold. Keep scoring after launch, because live traffic finds the failures your test set missed.
Key Takeaways
An LLM evaluation framework has four parts: a versioned test set, metrics tied to the use case, scoring methods and pass/fail release gates.
Public benchmarks such as MMLU and TruthfulQA help screen models, but only tests on your own workloads show whether a system is ready.
Deterministic checks are the cheapest, LLM-as-a-judge scales to open-ended text, and human review calibrates the judge.
RAG systems need faithfulness and context metrics, while agents need tool-call, trajectory and task-completion checks.
Compare models on cost per passed test, not price per token, and only among models that clear the quality gate.
Evaluation continues after launch: shadow tests, live scoring and failed cases fed back into the golden set keep quality measurable.
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In October 2025, Deloitte agreed to partially refund the Australian government over an A$440,000 report, as reported by AP . Reviewers had found fabricated references in it, as well as a fabricated quote attributed to a court. Later, the revised report disclosed that a generative AI system, Azure OpenAI, had been used in writing it.
Catching that failure did not need a new benchmark. A plain check that every citation points to a real source would have flagged it before delivery. That is the gap an LLM evaluation framework closes: it turns “the output looks right” into tests that run on every release. Using one model to grade another is explained in LLM as a judge , including how reliable it is.
What Is an LLM Evaluation Framework?
An LLM evaluation framework is a repeatable system for testing whether a language model application gives correct, safe and useful outputs for one specific job. It defines what you test, how you score it and what score a release must reach before it ships.
Think of it as the test suite for a system that never answers the same way twice. For example, a unit test checks that a function returns 4 for 2 + 2. In the same way, an evaluation framework checks that a support assistant answers refund questions from the right policy, without inventing terms.
Every working framework has the same four parts:
Test data: a versioned set of real inputs, expected answers and edge cases drawn from your own workload.Metrics: measures matched to the use case, such as faithfulness for retrieval or schema validity for extraction.Methods: the way each metric gets scored, whether by deterministic code, an LLM judge or a human reviewer.Gates: the thresholds a release must meet, agreed with the use case owner before any test runs.
These parts sit inside the wider enterprise LLM stack. Each one changes only through a versioned update, so scores stay comparable from one release to the next.
Benchmarks vs. Evaluation Frameworks: Model-Level vs. System-Level
A benchmark measures a model in isolation, on a fixed public test. A framework, on the other hand, measures your whole system: the prompt, the retrieval step, the tools, the guardrails and the model working together.
The difference shows up fast in practice. Two models can post close scores on a public leaderboard. Still, they can behave very differently once they meet your documents, users and formatting rules.
Dimension Benchmark (model-level) Evaluation framework (system-level)
What is tested The base model alone Model plus prompts, retrieval, tools and guardrails Test data Fixed public datasets Your own traces, golden answers and edge cases Question answered Which models are worth a trial? Is this release ready for these users? When it runs Once, during model selection On every change, then continuously in production
Benchmarks still earn a place in the process. They just belong at the start, as the first of three layers.
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Explore LLM Development The 3 Evaluation Layers Every Enterprise Needs
Enterprise evaluation works best as three layers, each answering a different question at a different stage. Screening narrows the field, system tests make the release decision and monitoring then keeps that decision true after launch.
Benchmarks for screening: which models deserve a trial at all?System-level evaluation: does this configuration meet our bar on our own tasks?Continuous monitoring: is quality holding up on live traffic?
Skipping a layer is how evaluation programs go wrong. For instance, a model picked on leaderboard scores alone has never been tested on your workload. A system that passed every pre-release test can still drift once real users arrive with questions nobody wrote down.
Layer 1: Benchmarks for Screening
Public benchmarks give a quick, shared yardstick for general ability. MMLU , published by Hendrycks et al. in 2020, spans 57 tasks across subjects from math to law. TruthfulQA , from 2021, asks 817 questions across 38 categories built to catch models repeating common falsehoods.
Stanford’s Holistic Evaluation of Language Models (HELM) , released in November 2022, looks wider and scores models on several dimensions beyond accuracy. In short, treat all of them as a screen, never as a verdict.
Two caveats limit how far a benchmark score travels. To begin with, test questions can leak into training data, which lifts scores without improving real ability. Older benchmarks also saturate as frontier models bunch near the top, so small gaps stop meaning much.
Layer 2: System-Level Evaluation Against Your Workloads
This is the layer where the release decision gets made. In practice, you run the actual application, with its prompts, retrieval and tools, against a test set built from your own traffic.
Take a claims assistant at an insurer. It would be scored on pulling policy numbers in the right format, citing the correct clause and declining to guess when a document is missing. None of those behaviors appears in a public benchmark.
The golden set and the release gate that make this layer work are covered step by step in the sections below.
Layer 3: Continuous Monitoring in Production
Live traffic always contains inputs your test set never imagined. Monitoring scores a sample of production traces with the same metrics used before release, so drift shows up as a number instead of a complaint.
The tooling for this layer is covered in our guide to LLMOps observability . The rule is simple: every failed live case should become a new test case.
LLM Evaluation Metrics by Use Case
LLM evaluation metrics only mean something when they match what the application is for. A summarizer, a SQL generator and a support agent fail in different ways, so each needs its own model evaluation metrics .
Start from the failure that would hurt the business most, then pick the metric that detects it. The table maps common enterprise use cases to the metrics that matter and the KPI each one protects.
Use case Primary metrics Business KPI it protects
Customer support assistant Answer relevancy, faithfulness to policy, tone, escalation accuracy Resolution rate, repeat contacts RAG knowledge search Faithfulness, context precision, context recall Time to answer, wrong-answer incidents Text-to-SQL and analytics SQL execution success, result match, numeric difference Report accuracy, analyst hours Document extraction Field-level exact match, JSON schema validity Straight-through processing rate Summarization Factual consistency, coverage, length compliance Review time per document Agents and workflows Tool call accuracy, task completion, step count Tasks finished without human help
Deterministic and Format Metrics
Deterministic metrics compare an output with a known answer using plain code. Examples include exact match, JSON validity and a pattern check on a policy number. SQL that returns the expected rows also counts, as does numeric difference within a tolerance.
They are fast, cheap and fully repeatable, which makes them the first metrics to automate. Their limit is that they only work when a correct answer can be written down. “Summarize this contract” has thousands of acceptable answers, so exact match would fail all but one.
So use them wherever an output is checkable, and keep them strict. A JSON response that misses one required field should fail, even when the prose around it reads well.
RAG Metrics: Faithfulness, Context Precision and Context Recall
Retrieval-augmented generation adds a second place to fail. The system can fetch the wrong passages, or it can fetch the right ones and then ignore them. For that reason, RAG evaluation scores the retriever and the generator separately.
Ragas defines faithfulness as the claims in a response supported by the retrieved context, divided by the total claims in the response. Suppose, for example, an answer makes four claims about a leave policy and the retrieved passages support three. Its faithfulness is then 3 / 4, or 0.75, and the unsupported claim is a likely LLM hallucination .
Context precision asks whether the retrieved chunks are relevant to the question. Context recall, in contrast, asks whether they hold everything needed for a full answer. Low recall points at the RAG pipeline rather than the model, and the techniques in advanced RAG target exactly that layer.
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Check data readiness, security, governance and evaluation coverage before a generative AI system goes live.
Get the Checklist → Mapping Metrics to Business KPIs
Metric scores earn budget only when someone can tie them to a business result. Agree the mapping with the use case owner before the first test runs.
Faithfulness: fewer escalations due to wrong answers.Task completion rate: hours of manual work removed from a process.Format validity: documents processed straight through, without rework.Latency at the 95th percentile: fewer abandoned sessions in customer-facing chat.Refusal accuracy: compliance incidents avoided when requests fall out of scope.
Our guide to AI performance metrics covers the business side of this mapping in detail.
Evaluation Methods: Deterministic Checks, LLM-as-a-Judge and Human Review
There are three ways to score an output, and a sound framework uses all three. The right choice per metric depends on whether the answer is checkable, how much nuance it needs and how many cases you must score.
A contract-review assistant shows the split well. Code confirms that every clause reference exists, while an LLM judge rates whether the summary captures the obligations. Then a lawyer reviews a small sample to confirm the judge matches expert opinion.
Method When to use Cost Failure mode
Deterministic checks When answers are checkable: formats, values, SQL results Lowest; plain code Cannot score open-ended text LLM-as-a-judge When open-ended text needs scoring at scale: relevance, tone, faithfulness Model tokens for every scored case Bias, including a preference for outputs like its own Human review Calibration and high-risk outputs, such as legal or medical answers Highest; expert time Slow, and inconsistent between reviewers unless a rubric exists
How LLM-as-a-Judge Works and How Reliable It Is
An LLM judge is a second model that receives the input, the output and a rubric, then returns a score or a preference. It can grade thousands of open-ended answers in the time a review team grades a handful.
The approach has solid evidence behind it. For instance, the 2023 MT-Bench study found that strong LLM judges agreed with human preferences over 80% of the time. That is about the same rate at which humans agree with each other.
Judges also carry known biases. Wataoka et al. showed in October 2024 that LLM judges tend to favor outputs resembling their own, a pattern called self-preference bias. A judge therefore needs checking before its numbers can gate a release.
Calibrating the Judge
Calibration turns a judge from an opinion into an instrument you can trust. Three habits do most of the work.
Use a judge from another model family: pair a Claude generator with a GPT judge, or the reverse, so self-preference has less room.Check agreement against human labels: score a fixed, human-labeled sample at regular intervals and recalibrate when agreement drops.Version the rubric and judge prompt: version both so a score change means the system changed, never the scorer.
Langfuse , for example, supports calibrating LLM judges against human labels on the same traces it scores.
When Human Evaluation Is Still Necessary
Human review stays essential in three places. New use cases need people to define what good looks like before a rubric exists. High-risk outputs, such as medical, legal or financial guidance, need expert sign-off.
The judge itself also needs a human baseline to be measured against. The goal is never to review everything, because a small, well-chosen human sample keeps the automated layers honest.
Building a Domain-Specific Evaluation Dataset
The evaluation dataset, often called a golden set, is the part of the framework that makes it yours. Public datasets cannot know your products, policies or customers, so the test set has to come from your own work.
An insurance team, for instance, might pull a few hundred real claims questions from support logs. Claims specialists then write the correct answers, and the team adds the odd cases that once caused escalations. That set becomes the bar every future release must clear.
Seed from production traces: sample real requests across intents, channels and user types instead of writing invented prompts.Write golden answers with domain experts: record the correct answer or a rubric, as well as the source document it depends on.Add edge and adversarial cases: include cases such as missing data, conflicting sources, out-of-scope requests and known past failures.Version the set: store it with the code, tag each case by intent and risk, and log every change.
There is no universal right size for a golden set. Coverage of real intents and known failure modes matters more than a round number. The set should also grow whenever production reveals a new failure.
Edge Cases, Adversarial Inputs and Red-Teaming
Edge cases test the boundaries of normal use: empty documents, mixed languages, contradictory sources. Adversarial cases test deliberate misuse, such as prompt injection or attempts to pull data the user should never see.
Keep both in a separate, tagged slice of the golden set so their pass rates stay visible. LLM red teaming covers attack testing in detail. Our guide to LLM security controls covers the defenses those tests should confirm.
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Book a Meeting → LLM Evaluation Tools Compared
LLM evaluation tools fall into two broad groups. Open-source libraries run inside your own pipeline, while platforms add tracing, dashboards and team workflows, and many enterprises end up using one of each.
The table compares eight common options using only what their own documentation states, checked November 2026. Where a vendor page does not state something, the table says so instead of guessing.
Tool Primary use and how it runs Judge and CI/CD support Published pricing
DeepEval Metrics for LLM apps and agents, including trajectory metrics; open source with Pytest integration LLM-as-a-judge metrics with per-metric thresholds; CI/CD via deepeval test run Open source Ragas RAG metrics such as faithfulness LLM-driven metrics; CI/CD not stated Open source (Apache 2.0) Promptfoo Prompt testing and automated red teaming with side-by-side matrix views; runs as a CLI, a library or in CI/CD CI/CD supported; judge support not stated Open source (MIT) LangSmith Evaluation and tracing from LangChain LLM-as-judge evaluators, offline and online; Pytest integration for CI Developer $0 per seat (1 seat, up to 5k base traces a month); Plus $39 per seat a month (up to 10k base traces), then pay as you goBraintrust Evaluation platform LLM-as-a-judge scorers; GitHub Action posts eval results on pull requests Starter plan with $0 platform fee; Pro $249 a monthLangfuse Code evaluators and LLM judges on live traces Judge calibration against human labels; GitHub Action fails a CI job on a score regression Open source and free to self-host; Cloud Hobby free, Core $29 a month Arize Phoenix Tracing and evaluation; open source, built on OpenTelemetry with OpenInference LLM-based evaluators; CI/CD not stated Open source, self-hosted (Elastic License 2.0) Microsoft Foundry Quality, RAG, safety and agent evaluators on Azure, including production traces Custom evaluators; automated quality gates in CI/CD pipelines Consumption-based billing
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Explore Microsoft Foundry Services How to Choose an LLM Evaluation Tool
No single tool wins every job, so choose by the job you need done first:
Tests inside a Python CI pipeline: start with a library such as DeepEval, and add Ragas metrics if the app uses retrieval.Prompt comparisons and red-team probes: Promptfoo runs from the command line as well as from a CI/CD job.Judges on live traces with calibration: a tracing platform such as Langfuse or Arize Phoenix.Apps built on Azure: Microsoft Foundry’s built-in evaluators cover quality, RAG and safety, together with agent checks.Apps built with LangChain and LlamaIndex : LangSmith sits close to that stack, with published per-seat pricing.
For the monitoring side of the same decision, see our comparison of AI observability tools .
How to Wire LLM Evaluation Into CI/CD
Wiring evaluation into CI/CD means the test suite runs on every change, so its results decide whether the change merges. It turns evaluation from a quarterly project into a routine check that nobody has to remember.
Picture a developer editing a system prompt to make answers shorter. Without a gate, that edit ships once it reads well in three manual tries. With a gate, it runs against the full golden set, and a drop in faithfulness blocks the merge before any customer sees it.
Trigger the suite on any change to prompts, models, retrieval settings, tools or the dataset itself.
Run deterministic checks first, since they are fast and cheap and catch format breaks early.
Then run judge-scored metrics on the full golden set, or on a fixed slice for quick feedback.
Compare each metric with its threshold as well as the last released version.
Block the merge on any failed gate, and report exactly which cases failed.
Finally, add every production failure to the golden set as a new, tagged case.
A pipeline like this is only as good as the data flowing into it. That is why the first step is instrumentation, not metrics.
Traces Come First
A trace records each step of a request: the prompt, retrieved chunks, tool calls, model output and latency. Without traces, a failed test tells you that something broke but not where it broke.
So instrument the application before building the gate. The same traces then feed debugging, production monitoring and new test cases, as our LLM observability guide explains.
A Minimal Regression Gate in Code
DeepEval runs evaluation as Pytest tests. Each test case holds the input, the actual output and, for RAG, the retrieved context, while each metric carries its own threshold. The example below gates a refund-policy answer on relevance and faithfulness.
# test_support_bot.py
from deepeval import assert_test
from deepeval.test_case import LLMTestCase
from deepeval.metrics import AnswerRelevancyMetric, FaithfulnessMetric
from my_app import answer_question # your RAG application
def test_refund_policy_answer():
question = "Can I get a refund after 30 days?"
answer, chunks = answer_question(question)
test_case = LLMTestCase(
input=question,
actual_output=answer,
retrieval_context=chunks,
)
# Starting thresholds set by the team, not universal values
relevancy = AnswerRelevancyMetric(threshold=0.7)
faithfulness = FaithfulnessMetric(threshold=0.8)
assert_test(test_case, [relevancy, faithfulness])Run it in the CI job with deepeval test run test_support_bot.py, so a failed metric fails the build. The 0.7 and 0.8 thresholds are starting points a team sets per use case. Raise them later, as the golden set and the system mature.
Evaluating Agents and Multi-Turn Workflows
Agents add a new dimension to evaluation, because the path matters as much as the answer. An agent can reach the right result through an unsafe tool call, or fail because one step in a long chain went wrong.
Take a procurement agent asked to raise a purchase order. It must look up the supplier and check the budget before it creates the order and notifies the approver. Scoring only its final message would miss a skipped budget check entirely.
Tool call accuracy: the right tool, with the right arguments, in the right order.Task completion: whether the workflow actually reached its goal state.Trajectory: whether the steps were efficient and stayed within policy.Memory and multi-turn coherence: whether the agent keeps facts from earlier turns.
DeepEval includes trajectory metrics for agents, while Microsoft Foundry ships tool call accuracy and task completion evaluators. The design choices behind these behaviors sit in AI agent architecture , and the policy side sits in agentic AI governance .
Multi-turn tests need scripted conversations rather than single prompts. Each script plays a user across several turns and checks the agent at every step, not only at the end. Our guide to AI agent evaluation covers trajectory scoring in detail.
Comparing Models on Quality-Adjusted Cost
Model prices are published per million tokens, but what a business buys is correct answers. Comparing models on quality-adjusted cost means dividing what a test run costs by the number of tests it passed.
The rule is short: cost per passed test = cost of the run / tests passed. Apply it only to models that clear your quality gate, because a model below the threshold is not cheap at any price.
Model Input per 1M tokens Output per 1M tokens Notes
GPT-6.1 Sol $2 $10 1,050,000-token context; prompts over 272K bill 2x input and 1.5x output GPT-6 Astra $10 $50 Highest list price in this table GPT-6 Luna $0.10 $0.50 Tied with Claude Haiku 5.5 for the lowest list price in this table Claude Opus 5.5 $4 $20 1M context; Batch API 50% off Claude Sonnet 5.5 $2 $10 1M context Claude Haiku 5.5 $0.10 $0.50 1M context; output $2.50 above a 100K prompt Gemini 4 Argon $2 through Nov 30, 2026; $4 from Dec 1, 2026 $10, then $20 Price doubles on December 1, 2026 Gemini 3.8 Flash $0.75 through Dec 31, 2026; then $1.50 $3.75, then $7.50 Price doubles on January 1, 2027
All prices above are list prices as of November 2026.
A Worked Example on 500 Test Cases
Now run the rule on a 500-case golden set where each case sends about 2,000 input tokens and returns about 500. That is 1 million input and 250,000 output tokens per run.
At $2 / $10, a run costs $2.00 + $2.50 = $4.50. At $4 / $20 it costs $9.00, and at $0.10 / $0.50 it costs about $0.23.
Then add illustrative pass counts against an 85% gate, which means 425 of 500 cases. If the $4 / $20 model passes 480, it costs $9.00 / 480, about $0.019 per passed test. If a $2 / $10 model passes 440, it costs $4.50 / 440, about $0.010, and still clears the gate.
A $0.10 / $0.50 model that passes 350 is cheaper per pass, but it fails the gate and drops out. Dates matter as well. Gemini 4 Argon’s list price doubles on December 1, 2026, so its cost per passed test doubles with no change in quality.
Teams act on these numbers by routing each task to the cheapest model that clears its gate, often through an LLM gateway . Self-hosted open-source LLMs add another option, priced in GPU hours instead of tokens.
Budgeting the Cost of Evaluation Itself
Evaluation carries its own bill, mostly in judge tokens. If a judge reads about 2,500 tokens and writes 200 per case, a 500-case run uses 1.25 million input and 100,000 output tokens. At GPT-6.1 Sol’s $2 / $10, that is $2.50 + $1.00 = $3.50 per run.
Two levers keep that bill flat. OpenAI lists Batch and Flex processing at 50% lower for GPT-6.1 Sol. Similarly, Anthropic’s Batch API is 50% off for Claude Opus 5.5. Both suit nightly suites that need no instant answer.
In production, score a sampled share of live traffic instead of every request. Size the sample to the risk of the use case, and raise it after any model or prompt change.
From Evaluation to Deployment: Gates, Shadow Testing and AI Governance
Evaluation pays off at the moment of release, when scores turn into a go or no-go decision. Three practices make that decision safe: fixed thresholds, shadow testing and an evidence trail.
Setting Pass/Fail Thresholds Before Deployment
Set thresholds before the first run, together with the use case owner, so nobody tunes the bar to fit the results. The table shows example thresholds; real values are set per use case and tightened over time.
Gate Metric Example threshold Action on fail
Format JSON schema validity 100% of cases Block release Grounding Faithfulness 0.8 average, no case below 0.5 Block release Relevance Answer relevancy 0.7 average Block release Safety Policy violations in the adversarial slice Zero Block and escalate Regression Change from the last release No metric drops more than 2 points Review before release Latency 95th percentile response time Set by the product SLA Review before release
Gates stop bad releases, while runtime AI guardrails catch the single bad response that slips past a passing release. A mature program needs both.
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The policies, controls and records an AI program needs before auditors ask for them.
Get the Checklist → Shadow Deployment and A/B Validation
Shadow deployment runs a new version alongside the live one on real traffic, without showing its answers to users. Both outputs get scored, so you see how the candidate handles live inputs before anyone relies on it.
Once shadow scores hold, an A/B test sends a small share of users to the new version. It compares business outcomes, not just metric scores, and promotion to full traffic follows only when both checks agree.
Evaluation Evidence for AI Governance
Auditors and regulators increasingly ask for proof that an AI system was tested, and evaluation records are that proof. Versioned datasets, scorer versions and results, together with release decisions, form an audit trail.
The NIST AI Risk Management Framework , released on January 26, 2023, is intended for voluntary use. Later, NIST added a Generative AI Profile, NIST AI 600-1, on July 26, 2024, and evaluation results are natural evidence for both.
In the EU, Article 15 of the AI Act requires high-risk AI systems to achieve an appropriate level of accuracy, robustness and cybersecurity. They must also perform consistently throughout their lifecycle. Teams working with ISO/IEC 42001, the AI management system standard, can draw on the same records.
Our AI governance framework guide shows where these records fit within a wider governance program.
An LLM Evaluation Maturity Path
Maturity here means how much of the evaluation runs without a person remembering to do it. Programs tend to move through four stages, and each stage needs evidence in place before the next one pays off.
Stage What it looks like Signal you are here Next step
1. Ad hoc spot checks Engineers try a few prompts by hand Quality questions get answered with “it looked fine” Write down real cases and expected answers 2. Scripted test sets A versioned golden set runs on demand Scores exist, but nobody blocks a release on them Agree thresholds with the use case owner 3. Gated releases CI/CD blocks changes that miss thresholds Failed merges cite specific test cases Add shadow tests and live sampling 4. Continuous evaluation Live traces get scored, and failures flow back into the set Drift shows up as a metric before users complain Recalibrate judges and extend to new use cases
Move up one stage only when the current stage’s evidence is automatic. A change such as fine-tuning or a new model is far easier to justify when stage 3 or 4 can prove it helped.
5 Common LLM Evaluation Mistakes
The same handful of mistakes appears across enterprise programs. Each one is cheap to avoid once it has a name.
1. Over-Relying on Public Benchmarks
A high MMLU score says a model knows a lot in general. It says nothing about your documents, formats or users, and training-data leakage can also inflate it.
2. Evaluating the Model Instead of the System
Failures often start in retrieval, prompts or tool wiring rather than in the model. Test the whole system; otherwise, a model swap will not fix what is actually broken.
3. Stopping After Launch
User behavior shifts, documents change and providers update their models. Without production scoring, quality decays quietly until a customer notices first.
4. Unversioned Scorers and Datasets
If the judge prompt or the golden set changes between runs, a score change means nothing. Version both alongside the application code, so you can record which versions produced each result.
5. Ignoring the Dimensions Regulators Ask About
Accuracy alone is not enough for high-risk uses. Track consistency, safety and security results too, since those are what Article 15 names. Our overview of generative AI risks lists the failure types worth a test slice of their own.
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AI Audit Framework: EU AI Act, NIST, ISO 42001 & the 6-Step Blueprint
How the EU AI Act, NIST and ISO 42001 fit into one six-step audit blueprint.
How Kanerika Approaches LLM Evaluation
Kanerika treats evaluation as part of the build, not a test phase bolted on at the end. Our LLM development engagements follow six stages, each with an owner and an output.
Define the stakes: agree the use case’s failure cost and the business KPI with its owner.Build the golden set: create a versioned test set from real traces and known edge cases.Choose the scoring: pick deterministic, judge and human checks per metric, then calibrate the judge.Gate the release: wire the suite into CI/CD so failed metrics block a merge.Shadow-test: run the candidate on live traffic before users see its answers.Monitor and feed back: score production traces and turn every failure into a new test case.
We build and evaluate our own AI agents, such as Karl and Alan , through the same stages. For clients on Azure, our Microsoft Foundry practice draws on its built-in quality, RAG, safety and agent evaluators.
Evaluation evidence also feeds our generative AI and AI governance work, so the records a release produces are the records auditors ask for. Kanerika holds ISO 27001 and SOC II Type II certifications.
Case Study: Context-Aware AI Agent for Expert Matching
Challenge
A global expert network with access to over one million subject-matter experts had to match client requests with the right people. Search was poor, expert recommendations were often inaccurate, and validation happened by hand across disconnected tools.
Solution
Kanerika deployed a context-aware AI agent to recommend experts for each request. Because accuracy was the core problem, the results were measured against the earlier baseline rather than judged by impression.
Results
40% increase in mapping accuracy
80% decrease in mismatch tickets
22% bandwidth savings
The full story is in the expert matching case study .
Case Study
40% Higher Mapping Accuracy With a Context-Aware AI Agent
A global expert network gained a 40% increase in mapping accuracy and an 80% decrease in mismatch tickets.
Read the Case Study → Wrapping Up
An LLM evaluation framework replaces opinion with evidence at the moment of release. Benchmarks shortlist, system tests on your own data decide, and production monitoring then keeps the decision true.
Start small with a versioned golden set, a few metrics tied to a business KPI and one gate in CI/CD. Then add calibrated judges, shadow tests and live scoring as the program matures.
Where does your LLM evaluation framework stand today?
If your honest answer is “it looked fine in testing”, start with stage 2 of the maturity path. If releases already pass through gates, the next gain is live scoring that feeds the golden set. Either way, the right LLM evaluation framework is the one that can block a bad release before your users find it.
Frequently Asked Questions
What is an LLM evaluation framework?
An LLM evaluation framework is a repeatable way to test whether a language model application is ready to ship. It combines a versioned test set, metrics matched to the use case, scoring methods and pass or fail thresholds. Teams run it on every change and keep scoring live traffic after launch, so quality stays measured rather than assumed.
How is LLM evaluation different from traditional ML evaluation?
Traditional ML evaluation compares predictions with fixed labels using metrics such as accuracy or F1. LLM outputs are open-ended text, so many correct answers exist for one input. LLM evaluation therefore mixes deterministic checks with LLM judges and human review. It also tests the whole system, including prompts, retrieval and tools, rather than the model alone.
How do you add LLM evaluation to a CI/CD pipeline?
Trigger the evaluation suite on any change to prompts, models, retrieval or tools. Run cheap deterministic checks first, then judge-scored metrics on the golden set. Compare each result with its threshold and the last release, and block the merge on any failure. Tools such as DeepEval run as Pytest tests inside the CI job.
What is shadow deployment and why does it matter?
Shadow deployment runs a new model or prompt version alongside the live system on real traffic, without showing its answers to users. Both outputs are scored, so teams see how the candidate handles real inputs before anyone relies on it. It catches failures that a pre-release test set missed, at no risk to customers.
How does LLM evaluation connect to AI governance?
Evaluation records give governance teams proof that a system was tested. The NIST AI Risk Management Framework is voluntary, and its Generative AI Profile appeared in July 2024. The EU AI Act asks high-risk systems for appropriate accuracy, robustness and cybersecurity. Versioned datasets, scores and release decisions form the audit trail both need.
How much does LLM evaluation cost to run?
Most of the cost is judge tokens. A judge reading 2,500 tokens and writing 200 per case uses 1.25 million input and 100,000 output tokens for 500 cases. At GPT-6.1 Sol’s $2 and $10 per million, that is about $3.50 per run. OpenAI Batch and Flex pricing, 50% lower for Sol, cuts that further for nightly suites.
What is the difference between model-level and system-level LLM evaluation?
Model-level evaluation tests a base model on public benchmarks such as MMLU or TruthfulQA. It helps shortlist candidates. System-level evaluation tests your full application, including prompts, retrieval, tools and guardrails, against your own data and edge cases. The release decision should rest on system-level results, because benchmark scores rarely predict behavior on your workload.
What is LLM-as-a-judge and how reliable is it?
LLM-as-a-judge uses a second model to score outputs against a rubric. The 2023 MT-Bench study found strong LLM judges agreed with human preferences over 80% of the time, about the rate humans agree with each other. Judges can favor outputs like their own, so use a different model family and check agreement against human labels regularly.
What are the most important metrics for LLM evaluation?
The right metrics depend on the use case. Retrieval systems need faithfulness, context precision and context recall. Extraction needs exact match and schema validity. Support assistants need answer relevancy, tone and escalation accuracy. Agents need tool call accuracy and task completion. Tie every metric to a business KPI, such as resolution rate or processing time.
How should teams build an LLM evaluation dataset?
Start by sampling real requests from production traces across intents and user types. Domain experts then write golden answers or rubrics with the source documents they rely on. Add edge cases, adversarial inputs and past failures. Version the set with your code. Coverage of real intents matters more than any fixed size, and the set should keep growing.
How do you evaluate an LLM without ground-truth answers?
When no single correct answer exists, score outputs against a rubric instead. An LLM judge can rate relevance, tone or completeness at scale. For retrieval systems, faithfulness checks whether claims are supported by the retrieved context, which needs no reference answer. Calibrate the judge against a small set of human ratings before trusting its scores.
How do you evaluate a RAG application?
Score the retriever and the generator separately. Context precision checks whether retrieved chunks are relevant, and context recall checks whether they hold everything needed. Faithfulness checks whether the answer’s claims are supported by that context. Low recall points to retrieval problems, while low faithfulness points to the generation step or the prompt.
How do you evaluate an LLM agent?
Evaluate the path as well as the final answer. Check tool call accuracy, meaning the right tool, arguments and order. Check task completion against the goal state, and review the trajectory for wasted or unsafe steps. For multi-turn agents, run scripted conversations and check memory and coherence at every turn, not only at the end.
Which open-source LLM evaluation framework is best?
No single open-source option wins every job. DeepEval suits teams that want Pytest-style tests in CI, with metric thresholds and agent trajectory metrics. Ragas focuses on retrieval metrics such as faithfulness. Promptfoo fits prompt comparisons and automated red teaming from the command line. Arize Phoenix suits tracing built on OpenTelemetry. Choose by the job.
