Your AI sounds right. But is it?
An AI assistant can give a convincing answer and still miss the most important fact. A chatbot can handle hundreds of conversations perfectly, then fail on a question your users ask every day.
LLM evaluation measures how well your AI performs against defined criteria, including accuracy, relevance, faithfulness, and task completion. Unlike broader LLM testing services, which also check security, performance, and functionality, evaluation focuses on how good the outputs and decisions actually are.
QAwerk’s LLM evaluation services combine realistic datasets, automated scoring, and human judgment to uncover failures that ordinary test scripts miss. You get a clear quality baseline, prioritized issues, and evidence to support your next release or model decision.
Where Assumptions Meet Reality
Your Result
Less guesswork, a clear quality baseline, and repeatable checks for every release.
Our LLM Evaluation Services
Evaluation Strategy & Metric Design
What does a good answer mean for your product? We translate business goals into measurable criteria, scoring rubrics, acceptance thresholds, and realistic test scenarios. Your team knows exactly what to measure and when quality is good enough to ship.
Golden Dataset Creation
We build the test set your evals stand on: real user scenarios, edge cases, and adversarial inputs, each with expected behavior and severity labels. The dataset is versioned and protected, so every future release is measured against the same bar.
Output Quality & Hallucination Evaluation
We score every response for accuracy, relevance, faithfulness to source, instruction following, and format. You learn your real hallucination rate, which scenarios trigger it, and how severe the errors are, not just an average that hides the dangerous ones.
AI Agent & Chatbot Evaluation
We assess task completion, tool-call accuracy, conversation consistency, and multi-step execution. From wrong API arguments to forgotten user instructions, we expose failures that compromise autonomous workflows. Explore our dedicated AI agent testing capabilities.
LLM-as-a-Judge Setup
Automated scoring is useful only when the scores mean something. We design LLM-as-a-judge rubrics, compare automated ratings with human assessments, and adjust evaluation criteria to reduce scoring bias and unreliable results.
LLM Benchmarking Services & Model Comparison
Our LLM benchmarking services compare candidate models using your actual tasks, not just public leaderboards. We measure quality, response time, and inference cost to help you select, replace, or upgrade models with fewer surprises.
RAG Evaluation
We measure retrieval relevance, context coverage, answer faithfulness, and citation accuracy to identify where knowledge-grounded responses break down. For deeper pipeline validation, explore our RAG testing expertise.
Regression Evaluation & CI
A prompt update shouldn’t break yesterday’s working feature. We create repeatable evaluation suites and connect them to your release pipeline, helping your team detect quality drops before deployment.
Human Expert Review
Some answers require more than an automated score. We organize structured human reviews with clear rubrics, involve domain specialists where needed, and measure reviewer agreement to validate nuanced or high-risk outputs.
Selected Cases
Need LLM evaluation services before your next release?
Let’s TalkWhen You Need AI Model Evaluation
Moving to Production
Your demo impressed everyone, but nobody can say how it performs on thousands of real questions. Evaluation gives you a launch threshold and proof you met it.
Unmeasured Hallucinations
Users report made-up answers, but you don’t know if it’s 1 in 10 or 1 in 1,000. We measure the rate and show where it happens.
Switching Models
A new model is cheaper, or a vendor update actually changed behavior. A side-by-side comparison on your tasks shows what you gain and what breaks.
Changing Prompts
Every tweak fixes one case and silently breaks another. A baseline and regression suite show the net effect before you merge.
Unreliable AI Agents
“Usually” isn’t good enough when your agent issues refunds or edits records. We measure how reliably it succeeds across repeated runs, not just whether it can.
Buyers Want Proof
Enterprise security reviews and AI regulations now ask how you test your AI. We give you scored, reproducible results you can share with confidence.
What You Gain with Our AI Evaluation Services
Launch with a number, not a hunch
A clear quality baseline and pass threshold for go-live.
Catch silent regressions
Every prompt or model change is scored before it ships.
Pick models on evidence
Compare quality, speed, and cost on your real tasks.
Trust your automated scores
Judges calibrated against human experts, not taken on faith.
Answer the evidence question
Reproducible results for enterprise buyers, auditors, and your board.
Keep everything we build
Datasets, rubrics, and eval suite live in your repo.
Why Choose QAwerk for LLM Evaluation
Independent Perspective
Independent Perspective Many AI evaluation services are run by the teams that build or sell the models. We didn’t build your AI and have no stake in your model choice, so there’s no reason to grade generously. You get an independent result buyers and investors trust.
Experience With Real AI Products
Experience With Real AI Products We evaluated LLM-generated meeting summaries for Granola, now a $1.5B company, with AI-assisted checks in CI. For Sitch, we validated an AI matchmaker’s conversation logic as it scaled to 20,000 introductions a day. We know what breaks when real users arrive.
QA Discipline Since 2015
QA Discipline Since 2015 Evaluation is testing at heart: clear criteria, representative cases, reproducible runs. We’ve applied that discipline to 250+ projects since 2015. Our AI model evaluation services bring it to AI, where loose test design produces scores that look precise but mean little.
Human Plus Automated
Human Plus Automated Code checks catch format and logic errors cheaply, calibrated LLM judges cover meaning at scale, and human-in-the-loop review handles nuanced cases. We pick the mix per metric, so you don’t overpay for simple checks or leave judgment calls to code.
Senior, Recognized, and Secure
Senior, Recognized, and Secure We have 30+ senior QA engineers, a place on IAOP’s Global Outsourcing 100, and clients who win industry awards and get acquired by market leaders. We serve teams across the USA, Australia, New Zealand, and Western Europe, sign NDAs, and keep your prompts and data in environments you approve.
Product-Specific Evaluation
Product-Specific Evaluation Our LLM model evaluation services don’t force every client into one benchmark or platform. A RAG assistant, customer-support chatbot, and autonomous agent need different datasets, metrics, and evaluation workflows. We tailor the scope to your product’s risks, technical environment, and business goals, including agreed data-handling requirements.
QAwerk continuously helps test both our desktop and mobile apps. They do automated and manual testing across all functionality, which covers everything from installation to UX, all the way to AI-generated responses. The team has been able to spot bad bugs before they were released, saving us from disaster! We’ve also generally been able to keep shipping quickly and not be blocked on our limited testing availability. They’re always on time, reliable, responsive, and reactive to our needs, which often change last minute. Their communication and attention to detail in reporting bugs are impressive. They feel like they’re on our team both figuratively and literally! Would recommend!
QAwerk is doing for us testing of our cloud platform. They also helped us in annotating images and videos for machine learning purposes. I like the way QAwerk approaches this; at the end, it's again, good people, interested, motivated to do things, and sometimes also achieving more than we expect.
Other Services We Offer
AI Testing
We test AI-powered products end to end: functionality, integrations, data handling, and user experience. It’s the safety net around your LLM evals, so a strong model isn’t let down by the app around it.
Penetration Testing
Prompt injection and data leakage are security problems, not quality scores. Our pentesters probe your app, APIs, and AI features the way real attackers would and show you how to close each gap.
API Testing
We verify how your AI integrates with external tools, CRMs, search services, and business systems, including error responses, access permissions, and data consistency across workflows.
Localization Testing
We check whether your AI-powered app performs consistently across languages, dialects, regional formats, and cultural expectations, especially when response quality varies between markets and user groups.
Compliance Testing
Regulators and enterprise buyers increasingly ask how your AI is tested. We map obligations to test cases and deliver evidence packs that turn eval results into audit-ready proof.
Automated Testing
We build automation frameworks that run in your CI on every build. Paired with eval suites, they catch broken flows and quality drops before a release reaches your users.
FAQ
What is LLM evaluation?
LLM evaluation is the process of measuring how well a large language model, or the app built on it, does its intended job. It scores outputs against LLM evaluation metrics such as accuracy, relevance, faithfulness to sources, safety, and task completion, using a test set that reflects real usage. The result is a baseline you can compare across prompts, models, and releases.
How is LLM evaluation different from LLM testing?
LLM testing checks whether the system works: features function, responses come back fast, integrations hold, and attackers can’t break it. LLM evaluation measures the quality of what the model says and does, usually as scores against thresholds rather than simple pass or fail checks. Most production AI needs both.
Which metrics do you use to evaluate LLMs?
It depends on your use case. For generated answers, we typically measure correctness, relevance, faithfulness, hallucination rate, and instruction following. RAG apps add context precision and recall; agents add task completion, tool-call accuracy, and reliability across repeated runs. We also track safety, latency, and cost per successful task when they affect your release decision.
How do you evaluate AI agents and chatbots?
We look at the whole path, not just the final reply. For agents, we use AI agent evaluation metrics to check whether the task was completed, whether the right tools were called with the right arguments, how many steps were wasted, and how the agent recovers from errors. For chatbots, we run multi-turn scenarios to test consistency, context retention, and tone. We repeat every run to see how reliably the system succeeds.
What is LLM-as-a-judge, and can it be trusted?
LLM-as-a-judge means using one model to score another model’s outputs against a rubric. It makes evaluation fast and scalable, but judges have known biases: they favor longer answers, the first option shown, and text that sounds like their own. A judge can be trusted once it’s calibrated. We compare its scores with human labels and refine it until it agrees with expert reviewers on your data.
Do we need an evaluation platform or a service?
A platform like LangSmith or DeepEval gives you the infrastructure to run evals. It doesn’t decide what to measure, build your test data, or check whether your judges are right. A service does that design work. We set up and run the tools inside your stack, so you get results and a working eval suite instead of another tool to learn.
Can LLM evaluation be automated?
Largely, yes. Once metrics, datasets, and judges are in place, evals can run as automated LLM regression testing in CI on every prompt, model, or retrieval change. Human review still matters for building ground truth, calibrating judges, and checking high-stakes or ambiguous cases. A good setup automates the bulk of the scoring and sends only uncertain results to people.
How long does an LLM evaluation take?
A focused evaluation of one LLM feature usually takes two to four weeks, from defining success criteria to a scored baseline report. Agentic or multi-model systems, expert human review, or several languages can stretch that to six to eight weeks. We’ll give you a realistic timeline after a short scoping call.
Related in Our Blog
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How We QA Chatbots That Give Different Answers to the Same Question
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Most RAG failures don’t look like failures at first. The model sounds confident. The response reads well. But the retrieved context was wrong, or the answer drifted from the source entirely....
Read MoreFind Out How Good Your LLM Really Is
Not sure how to evaluate an LLM like yours? Tell us what your AI does, and we’ll suggest what to measure first and tell you honestly if we’re the right fit.
11+
YEARS IN REGULATED DOMAINS300+
PROJECTSTESTED
30%
LOWER LLM COSTS2-4
WEEKS TO A QUALITY BASELIN
