LLM Evaluation Services
Human LLM evaluation across 30+ languages for hallucination, RAG groundedness, instruction following, multi-turn and agent trajectory testing, plus LLM-as-a-judge calibration against measured human agreement.
Scope an Evaluation Calibrate Your LLM Judge
What LLM Evaluation Actually Measures
Evaluation asks whether the model is good at the job it was built for. Red teaming asks whether it can be made to fail; fairness auditing asks whether it treats groups differently; and RLHF annotation creates training data.
| Not the same as | Difference |
|---|---|
| A/B testing | Measures user preference in production |
| Red teaming | Tries to make the system fail |
| RLHF annotation | Produces preference data that trains the model |
| Public benchmarks | Measure general capability on potentially contaminated data |
LLM Evaluation Capabilities
- Factual accuracy and hallucination
- Groundedness and attribution
- Instruction following
- Multi-turn coherence
- Domain correctness
- Sentiment and intent accuracy
- Safety and policy compliance
- Agent trajectory
- Language and register quality
Human Evaluation Methods
| Method | How it works | Best for |
|---|---|---|
| Reference-based scoring | Known-correct reference answer | Closed-form tasks, extraction and classification |
| Rubric scoring | Dimension-by-dimension human rating | Open-ended generation |
| Pairwise preference | Counterbalanced comparison of two outputs | Model and version comparison |
| Rating with adjudication | Senior review of disagreements | High-stakes domain evaluation |
| Error taxonomy annotation | Product-specific failure labels | Actionable improvement backlogs |
| Golden set construction | Curated, versioned reference cases | Repeatable release evaluation |
Calibrate the Judge Before You Trust the Score
Human reference labels → automated judge on the same set → overall and per-slice divergence → diagnose position, verbosity and self-preference bias → recalibrate and monitor drift.
Published research reports over 80% agreement on well-structured tasks, position effects across roughly 150,000 instances and 15 judges, no judge uniformly reliable across benchmarks and frontier-model errors above 50% on difficult bias benchmarks. The 20–25% human-review divergence range shown here is directional industry guidance, not a standards-body threshold.
Evaluate RAG and Agents as Systems
| RAG dimension | Question |
|---|---|
| Context precision | How much retrieved context was relevant? |
| Context recall | How much required evidence was retrieved? |
| Faithfulness / groundedness | Is every claim supported by context? |
| Answer relevance | Does it answer the actual question? |
| Citation accuracy | Do citations support their attached claims? |
| Noise sensitivity | Does irrelevant context change the answer? |
Agent trajectory evaluation
Review task completion, tool selection, parameter accuracy, path efficiency, error recovery, stopping behaviour, system-state changes and whether the agent's report matches what it actually did.
Golden Sets Beat Benchmark Theatre
Public benchmarks can be contaminated, English-first and unlike real traffic. We build versioned evaluation sets from representative tasks, known failures, languages and difficulty slices. The client owns the golden set and rubrics.
Multilingual LLM Evaluation
Native evaluators support 30+ global languages and 12+ Indian languages, including native-script, romanised, transliterated and code-mixed inputs. Results are reported per language so an English average cannot hide regional failure.
How an LLM Evaluation Engagement Runs
- Scope and success definition
- Golden set construction
- Rubric and rater calibration
- Blinded evaluation
- Failure analysis and taxonomy
- Report and walkthrough
Baseline evaluation is typically 5–6 weeks, judge calibration 2–3 weeks and recurring release-cycle evaluation 1–2 weeks per cycle, depending on scope.
What You Receive
- Evaluation report and per-slice results
- Versioned golden set
- Rubric and anchor examples
- Item-level scores and rationales
- Inter-rater agreement report
- Judge-calibration configuration where scoped
- Failure taxonomy and reusable regression set
- Live walkthrough with the ML team
Where LLM Evaluation Goes Wrong
- Uncalibrated judge trusted as ground truth
- Single-turn evaluation only
- Vague rubric
- Golden set built from clean questions
- Fluency mistaken for correctness
- Blended multilingual score
- Benchmarks used as product evidence
- Agent judged only on its final answer
- No agreement statistics
- Machine-translated evaluation sets
What We Do Not Do
We do not sell an evaluation platform, hide dimensions inside one unexplained score, treat an uncalibrated judge as ground truth, tune the client's model or reuse a client's golden set.
Related Services
Human Evaluation & A/B Testing AI Red Teaming AI Bias & Fairness Audit LLM & RLHF Annotation LLM Training Data Curation Dataset QA & Label Audit
LLM Evaluation Frequently Asked Questions
What is LLM evaluation?
Structured measurement of how well a language model performs the task it was built for — factual accuracy, groundedness, instruction following, multi-turn coherence, domain correctness, safety behaviour and language quality — against a written rubric on a test set that reflects real usage. It differs from red teaming, which tries to break the model, and from A/B testing, which measures what users prefer in production.
We already use LLM-as-a-judge. Why would we need human evaluation?
Because the judge needs a reference point. LLM judges reach over 80% agreement with humans on well-structured tasks, but they carry documented position, verbosity and self-preference biases, and a RAND study found no judge uniformly reliable across benchmarks. Industry guidance holds that divergence above 20–25% from human spot-checks means the judge needs recalibrating for your domain. Most teams have never measured their divergence. We baseline it, diagnose the bias pattern, recalibrate and monitor for drift — so you can keep evaluating at automated scale with numbers you can defend.
What is position bias in an LLM judge?
A systematic preference for a response based on where it appears rather than how good it is. Research across roughly 150,000 evaluation instances and 15 judges found position bias varies significantly by judge and task and is not attributable to chance — some judges favour the first response, some the last. It matters most in pairwise comparison, which is how most model-versus-model evaluation is run. We randomise and counterbalance presentation order in human evaluation, and test for it explicitly when calibrating a judge.
How do you evaluate hallucination?
We separate unsupported claims from contradicted ones, because they have different causes and different fixes. For RAG systems we check each claim against the retrieved context and verify that citations exist and actually support the claim attached to them. For open-domain generation, domain SMEs verify factual claims against authoritative sources. Fluent, confident, well-cited and wrong is the hardest case and the one that most needs a human who knows the field.
Do you evaluate RAG systems?
Yes, and we measure retrieval and generation separately — context precision, context recall, faithfulness, answer relevance, citation accuracy and noise sensitivity. A blended quality score can't tell you whether retrieval missed the document or generation ignored it, and those need completely different fixes.
Can you evaluate agents that call tools?
Yes. Agent evaluation covers task completion, tool selection, parameter accuracy, trajectory efficiency, error recovery, stopping behaviour and whether the agent's own report of what it did matches what it actually did. A correct final answer reached through a broken path passes end-state testing and fails in production.
Why can't we just use benchmark scores?
Four reasons: benchmarks are published and may be in the training data; they measure benchmark-shaped questions rather than your users' messy real ones; a single headline number averages over exactly the slices you care about; and most are English-first, so strong benchmark performance says little about Tamil or Odia. Benchmarks are useful context. A golden set built from your own traffic is evidence.
Do you evaluate in Indian languages?
Yes — 12+ Indian languages including romanised and code-mixed variants, with evaluation sets authored natively rather than translated from English. We report per language and never blend into a single multilingual score, because a strong English average routinely hides a weak result elsewhere.
How long does an evaluation take?
A first engagement covering one or two languages typically runs 5–6 weeks, most of it in golden set construction and rubric calibration. Once those exist, subsequent cycles run 1–2 weeks. Judge calibration against an existing test set runs 2–3 weeks.
Who owns the golden set and rubrics?
You do. They are built from your traffic, versioned, and delivered to you with the report — along with the calibrated judge configuration where that's in scope. The report describes one model at one moment; the golden set keeps working every release afterwards.