Most teams launching an LLM feature can't answer a simple question: is it actually good? A chatbot demo looks impressive, a few test prompts pass, and the feature ships — until users find wrong answers, hallucinated sources, or unsafe responses. Every prompt change, model upgrade, or new document set can quietly make things better or worse, and without evaluation, nobody knows which.
Our LLM evaluation services give you a reliable way to measure and improve AI quality. We build evaluation datasets from real use cases, define scoring rubrics, combine automated metrics with calibrated human review, evaluate RAG retrieval and answers, compare models and prompts, and set up regression testing so every change is measured before it reaches users.
What are LLM evaluation services? LLM evaluation services measure the quality, accuracy, safety, and reliability of large language model applications such as chatbots, RAG systems, and AI agents. They include building evaluation datasets with reference answers, defining rubrics, running automated metrics and LLM-as-judge scoring calibrated against human reviewers, evaluating retrieval quality for RAG, red teaming for safety, comparing models and prompts, and automating regression tests in development pipelines.
LLM Evaluation at a Glance
Question | Answer |
|---|---|
What it is | Measuring and improving the quality of LLM applications with test sets, metrics, and human review |
Core deliverables | Evaluation dataset, scoring rubric, automated evaluation pipeline, human review results, model and prompt comparison, regression tests, quality report |
What we evaluate | Chatbots, RAG systems, AI agents, summarization, extraction, classification, code generation |
Quality dimensions | Correctness, groundedness, relevance, completeness, safety, tone, format, latency, cost |
Tools | Ragas, DeepEval, promptfoo, LangSmith, Arize Phoenix, custom evaluation code |
Engagement models | Evaluation setup, model or vendor comparison, ongoing evaluation and human review |
Signs You Need LLM Evaluation
You can't say how accurate your AI is. Quality is judged by a few manual tests.
Changes break things silently. A new prompt or model fixes one case and breaks others.
Users report hallucinations. Answers cite sources that don't say what the AI claims.
You're choosing between models. You need evidence, not vendor benchmarks, for your use case.
Stakeholders need sign-off. Leadership, legal, or customers want proof of quality and safety before launch.
Costs and latency are rising. You want to know if a smaller, cheaper model would be good enough.
Our LLM Evaluation Services
Evaluation dataset creation
We build test sets from real user questions, documents, and edge cases, with reference answers and expected sources, reviewed by people who understand the domain.
Rubric design
We define clear scoring criteria for each quality dimension, with examples of good, acceptable, and failing responses.
Automated evaluation
We implement automated metrics and LLM-as-judge scoring, and calibrate them against human ratings so automated scores can be trusted.
Human evaluation
Trained reviewers score responses against your rubric for cases where automated metrics aren't enough, through our RLHF services.
RAG evaluation
We evaluate retrieval separately from generation — did the system find the right content, and did the answer stay faithful to it — so you know which part to fix. This connects to our RAG data pipeline work.
Agent evaluation
We test multi-step AI agents on task success, tool use, error handling, and safety across realistic scenarios.
Model and prompt comparison
We compare models, prompts, and configurations on your test set for quality, latency, and cost, so you choose with evidence.
Red teaming and safety testing
We probe for harmful, biased, off-topic, or policy-breaking outputs and prompt injection risks, and document the failures found.
Regression testing in CI/CD
We add evaluation to your development pipeline, so every prompt, model, or data change is tested before release.
Production monitoring
We sample live responses for ongoing scoring and flag quality drops over time.
Evaluation Methods Compared
Method | How it works | Strengths | Limitations |
|---|---|---|---|
Reference-based metrics | Compare output to a reference answer | Fast and cheap | Misses valid answers worded differently |
LLM-as-judge | A model scores outputs against a rubric | Scales well, handles nuance | Needs calibration against humans |
Human review | Trained reviewers score outputs | Most reliable for nuanced quality | Slower and more costly |
Task success checks | Verify the outcome, such as correct extraction or passing code tests | Objective | Only works where outcomes are checkable |
Strong evaluation combines all four, using automation for scale and humans for calibration and hard cases.
What We Measure
Dimension | Question |
|---|---|
Correctness | Is the answer factually right? |
Groundedness | Is every claim supported by the retrieved sources? |
Retrieval quality | Did the system find the right documents? |
Relevance and completeness | Does the answer address the whole question? |
Safety | Does it avoid harmful, biased, or policy-breaking content? |
Format and tone | Does it follow required structure and style? |
Latency and cost | Is it fast and affordable enough? |
How It Works
Step 1: Define success
We agree the use case, quality dimensions, and minimum acceptable scores.
Step 2: Build the test set
We collect and write evaluation examples with reference answers.
Step 3: Baseline
We evaluate your current system and report results by dimension and case type.
Step 4: Diagnose
We identify the biggest failure patterns and their likely causes — retrieval, prompt, model, or data.
Step 5: Improve and compare
We test fixes and alternatives against the same test set.
Step 6: Automate
We add evaluation to CI/CD and production monitoring.
What You Get
An evaluation dataset tailored to your use case
A scoring rubric with examples
Baseline quality scores and failure analysis
Model and prompt comparisons on quality, latency, and cost
Automated regression tests in your pipeline
A quality report for stakeholders
Who This Is For
Teams launching AI features | Companies with RAG chatbots | AI product teams | Enterprises selecting models |
|---|---|---|---|
Need proof of quality before release | Need to reduce wrong and unsupported answers | Need regression testing for every change | Need evidence-based model and vendor decisions |
Why Work With Codersarts
We build LLM applications, RAG systems, and AI agents ourselves, so our evaluations focus on the failures that matter in production. We calibrate automated scoring against real human judgment, separate retrieval problems from generation problems, and leave you with an evaluation setup your team runs on every release.
Frequently Asked Questions
What is LLM evaluation?
LLM evaluation is the process of measuring how well a language model application performs on your use cases — accuracy, groundedness, safety, format, latency, and cost — using test sets, metrics, and human review.
What is LLM-as-judge?
LLM-as-judge uses a language model to score outputs against a rubric. It scales well but must be calibrated against human ratings to be trusted.
How many test cases do we need?
It depends on the use case and risk. A focused starting set covering common questions and important edge cases is usually enough to find major issues, and it grows as you learn.
How do you evaluate RAG systems?
We measure retrieval — whether the right content was found — and generation — whether the answer is correct and supported by that content — separately, so you know what to fix.
Can you compare OpenAI, Anthropic, Google, and open-source models?
Yes. We compare models on your own test set for quality, latency, and cost, rather than relying on public benchmarks.
Can evaluation run automatically?
Yes. We add evaluation to your CI/CD pipeline so prompts, models, and data changes are tested before release, and sample production traffic for ongoing monitoring.
How long does an evaluation setup take?
A first test set, rubric, and baseline evaluation typically take a couple of weeks. Automation and ongoing review follow.
How much do LLM evaluation services cost?
An evaluation setup is a fixed-scope engagement. Ongoing human review is priced by volume and complexity.
Related Data Services
AI Data Services — data preparation for AI systems
Data Labeling — labeled data for training and testing
LLM Development — build and fine-tune LLM applications
Vector Database Development — improve retrieval quality
Ready to Know How Good Your AI Really Is?
Tell us about your AI application and how you test it today. We'll show you how to measure it properly and what it would take.
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