Software testing tools built for deterministic code do not work on language models. A unit test either passes or it fails. 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. That gap is where LLM QA testing lives, and it is why most AI teams eventually stop trying to automate their way through it.
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 QA requires a different methodology: define what “correct” means for your use case, build evaluation sets that surface failures in that definition, and run human reviewers with domain knowledge through the results.
That is what we do. Annotera’s LLM QA service covers five core testing disciplines:
Hallucination is not a fringe edge case. Studies across major commercial LLMs put hallucination rates between 3% and 27% depending on domain and task type. For a customer-facing application fielding thousands of queries a day, even a 3% 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.
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.
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.
