Software testing tools built for deterministic code do not work on language models. An LLM gives you a different answer every time you ask, and “correct” depends on context, tone, and what the model was supposed to know. Annotera’s LLM evaluation services provide structured, human-in-the-loop quality control for LLMs and generative AI applications, testing what automated tools miss: factual accuracy, hallucination rates, RAG pipeline performance, prompt behavior under edge cases, and safety guardrails, directly inside your evaluation cycles.
Most teams discover the hard way that their existing QA process does not transfer. A regression suite built for a deterministic API will pass on an LLM that gives plausible-sounding but factually wrong answers, because the suite cannot read the output. LLM evaluation requires a different methodology: define what “correct” means for your use case, build evaluation sets that surface failures against that definition, and run human reviewers with domain knowledge through the results.
Annotera’s LLM evaluation services cover five core testing disciplines, each designed to catch a different class of failure that automated tooling routinely misses: hallucination and factual accuracy, RAG pipeline validation, prompt regression, safety and guardrails, and performance. Every program produces LLM evals you can rerun after each model, prompt or retrieval change, with metrics your team can track release over release. For the data side of evaluation, our guide to human-graded LLM evaluation datasets explains how reliable benchmark sets are built.
The model says something confident and specific. Is it true?
Hallucination is not a fringe edge case. Every language model produces some answers that are fluent, specific and wrong, and the rate varies widely by domain and task type. For a customer-facing application fielding thousands of queries a day, even a small rate translates to a large volume of wrong answers going out the door.
We test factual accuracy against golden datasets and verified reference sources specific to your domain. Our reviewers flag unsupported claims, fabricated citations, and plausible-sounding errors that automated metrics score as passes. Every flagged response is categorized by failure type (factual error, unsupported claim, confident speculation) and logged with the source the model should have cited.
Retrieval-augmented generation is supposed to keep models grounded in your data. In practice, RAG pipelines fail in ways that are hard to detect from outside: the retriever pulls the wrong chunk, the embeddings surface near-matches instead of exact matches, the model ignores retrieved context and answers from its weights anyway.
Annotera tests RAG pipelines end-to-end. We evaluate retrieval quality (precision and recall on query-document pairs), chunk boundary accuracy, embedding relevance at the query level, and whether the model is actually using retrieved context in its responses or generating independently. We build test query sets drawn from real user patterns and adversarial edge cases that expose retrieval gaps before they reach production.
Model behavior changes after every update, fine-tuning run, or system prompt edit. Without a structured regression suite, teams discover behavior changes in production. And most do, usually from a user report.
We build and maintain prompt test suites tailored to your application: baseline prompts, edge case prompts, adversarial inputs, and prompts designed to surface known failure modes in your task type. We run the suite before and after model updates, compare outputs semantically (not just by string match), and report on behavioral drift. Where behavior has changed, we document whether the change is an improvement or a regression against your defined acceptance criteria.
An LLM-powered application that behaves correctly on a developer’s MacBook is not necessarily behaving correctly everywhere users actually access it. Rendering differences, browser engine variations, screen size constraints, and OS-level input handling all affect how an AI interface presents and performs.
Annotera runs regression and functional QA across the full device matrix your users represent:
We test response rendering, streaming output display, input handling (voice, text, file upload), latency perception on lower-bandwidth connections, and UI behavior on small screens where response formatting often breaks. A response that looks clean on a widescreen desktop can be unreadable on a 375px mobile viewport. We catch that before your users do.
LLM QA does not have to live outside your development workflow. Annotera operates on a sprint cadence, running regression suites within your two-week release cycles and delivering reports before sign-off, not after. We attend sprint reviews when needed, flag blocking issues with severity ratings your team can triage immediately, and maintain the test library between sprints so coverage grows rather than resets with each cycle. For teams on continuous deployment, we run rolling regression checks on a defined schedule aligned to your merge frequency.
Jailbreaks, toxic outputs, policy violations, and AI bias do not show up on functional test runs. They show up when a user finds them.
Our safety evaluation team tests guardrail coverage across your defined policy boundaries. We run structured red-team testing: adversarial prompt injection attempts, jailbreak sequences, and boundary probing across sensitive categories (hate speech, personal data, regulated content, competitor references). We also evaluate AI bias and fairness across demographic and contextual variables relevant to your use case, and check for compliance failures against your stated content policies.
Accuracy is not the only thing that matters in production. A model that takes 14 seconds to respond, costs $0.08 per query, and produces verbose outputs that downstream systems cannot parse is a problem even if every answer is technically correct.
We track and report on token cost per query, response latency distribution, output length consistency, structured output compliance (JSON, XML, function call formats), and system observability signals. For teams running at scale, we can baseline these metrics during pilot and monitor for drift over time.
Automated LLM evaluation tools, including LLM-as-judge approaches, have a real role in high-volume screening. They also have a well-documented failure mode: they score based on surface fluency, not factual accuracy or policy compliance. A response that sounds authoritative and well-structured will score well even when the underlying claim is wrong.
Human reviewers with domain knowledge catch what automated metrics miss. They know when a medical claim is plausible but clinically incorrect. They recognize when a legal summary omits a material qualification. They spot a confident fabrication where an automated scorer saw a high-confidence response.
Annotera’s LLM QA reviewers are trained by domain: legal, healthcare, financial services, edtech, retail. They work from structured evaluation rubrics built around your use case, not generic quality frameworks. And they produce structured output you can act on: categorized failure logs, severity ratings, and remediation pointers, not raw scores that leave interpretation to you.
We map your use case, define what "correct" means for your application, identify the failure modes that matter most, and build the evaluation framework before any testing starts.
We run a structured pilot on a representative sample from your pipeline. You get a full evaluation report showing coverage, failure rates by category, and a clear picture of where your model is and is not performing.
We slot into your release cadence, whether that is a two-week sprint, a monthly fine-tuning cycle, or a continuous deployment setup. Test suites are maintained and updated as your model and use case evolve.
| Metric | What It Measures |
|---|---|
| Hallucination rate | % of responses containing ungrounded or factually incorrect claims |
| Retrieval precision | % of retrieved chunks relevant to the query |
| Retrieval recall | % of relevant chunks successfully retrieved |
| Context utilization rate | % of responses that actually use retrieved context |
| Guardrail pass rate | % of safety-boundary test inputs blocked correctly |
| Prompt regression score | Semantic similarity of outputs across model versions |
| Token cost per task | Average token spend per query type |
| Response latency | P50, P90, P99 latency distribution |
| Output format compliance | % of responses matching required structure |
human preference ranking and feedback annotation for alignment
ground-truth annotation for hallucination detection models
Testing an LLM application properly takes a methodology, not just a tool. Tell us what you are building and where your current evaluation process falls short. We will scope a pilot in 24 hours.
Annotera is an Omind AI brand backed by Fusion CX’s delivery network of 17,000 people across 40 delivery centers in 13 countries, inside operations certified to ISO/IEC 27001:2022, SOC 2 Type II, HITRUST CSF and PCI DSS 4.0.1.
Answers on methodology, metrics, human versus automated scoring, safety testing and how to get started.
LLM evaluation services test how a language model or generative AI application behaves against the standard your product needs: factual accuracy, grounding in retrieved sources, safety, tone and consistency across releases. Annotera designs the evaluation sets and rubrics, runs trained human reviewers through the outputs and reports metrics you can act on.
Traditional tests expect the same output for the same input. LLMs give different, plausible-sounding answers each time, so correctness has to be judged against a rubric by people who understand the domain, and tracked statistically across many prompts rather than pass or fail on one.
Typical programs report hallucination rate, retrieval precision and recall, context utilization, guardrail pass rate, prompt regression scores, cost per task and latency, plus rubric scores for helpfulness and tone. The metrics table on this page shows the full set.
Both, in the right places. Automated judges are useful for high-volume screening; human reviewers with domain knowledge check the cases automated scoring gets wrong, calibrate the judges and handle anything safety-critical.
Reviewers check model claims against trusted sources or the documents the model was given, label unsupported or false statements, and report a hallucination rate by topic and prompt type so you can see where the model invents answers.
We test retrieval and generation separately: whether the right passages were retrieved, whether the answer used them, and whether the final response is faithful to the sources. That separates search problems from generation problems.
Jailbreak attempts, toxic or biased outputs, policy violations, sensitive-topic handling and data leakage, tested against your own policies. Our article on human-in-the-loop safety testing describes the approach.
Evaluation sets are versioned and rerun on every model, prompt or retrieval change, inside your sprint cadence. A 48-hour pilot on a sample of your pipeline sets the baseline before ongoing testing starts.
AI evaluation is the process of testing how an AI model or application performs against defined criteria, such as accuracy, grounding, safety, robustness, cost and latency, before release and after every change. For generative AI it combines automated metrics with human review, because many failures only show up when a person reads the output. Annotera provides AI evaluation for LLMs, RAG pipelines and AI agents.
