LLM Engineering / LLM Evaluation
LLM Evaluation & Testing
Most LLM features ship on gut feeling, then regress when prompts or models change. We build evaluation systems — test sets, automated scoring, LLM-as-judge checks, and human review — so you can measure quality, compare models, and catch regressions before users do.
This Is For You If...
You can't say whether your AI feature is getting better or worse
Model or prompt changes cause regressions nobody notices until users complain
You need to choose between models on quality and cost
Enterprise customers ask how you test AI accuracy and safety
Which Area Matches Your Need?
Ask Yourself | Points Toward |
Do you have test cases for your AI feature? | Evaluation Dataset Design |
Is quality judged manually? | Automated Scoring |
Are you choosing or switching models? | Model Comparison |
Do changes ship without checks? | Regression Testing in CI |
LLM Evaluation Services We Offer
Evaluation Dataset Design
Representative test cases built from real usage, including edge cases and known failures.
Automated Scoring
Exact-match, reference-based, and LLM-as-judge scoring calibrated against human ratings.
Model Comparison
Side-by-side quality, latency, and cost comparisons across providers and versions.
Regression Testing in CI
Evaluations run automatically on prompt, model, or code changes.
What We Need From You
What to Share | Why It Helps |
The AI feature and its purpose | Defines what "good" means |
Real inputs and examples | Basis for the test set |
Current prompts, models, and pipeline | What will be evaluated |
Quality concerns from users | Points to priority checks |
Common Mistakes to Avoid
Instinct | Why It Doesn't Help | Do This Instead |
Testing on a handful of examples | Misses most failure modes | Build a representative test set |
Trusting LLM-as-judge blindly | Judges can be biased or wrong | Calibrate against human ratings |
One overall score | Hides which aspect failed | Score accuracy, format, and safety separately |
Evaluating once | Quality drifts with changes | Run evaluations on every change |
How It Works
1. Define quality
What good output means for this feature
2. Build the test set
From real usage and edge cases
3. Set up scoring
Automated and human checks
4. Baseline
Measure the current system
5. Automate
Evaluations in your release process
Why Codersarts
We make AI quality measurable, so decisions about prompts, models, and releases are based on evidence your team and customers can see.
Turnaround Time
An initial evaluation set and baseline is typically delivered within one to two weeks.
FAQ
Which tools do you use?
Custom evaluation code and tools like promptfoo, Ragas, DeepEval, or LangSmith, depending on your stack.
Can you evaluate RAG systems?
Yes — retrieval and answers measured separately; see RAG Systems.
Do you include safety checks?
Yes — harmful output, data leakage, and prompt injection tests.
Is our project kept confidential?
Yes. Your data, code, and results stay confidential, and an NDA is available on request.
Related Services
Is Your AI Feature Getting Better or Worse?
Measure it — and catch regressions before users do.