---
title: LLM Evaluation
category: product
entity_type: skill
price: $15
canonical: https://forgehouse.ai/skills/llm-evaluation/
lang: en
hreflang_alt: https://forgehouse.ai/tr/skiller/llm-evaluation/
last_updated: 2026-06-20
---

# LLM Evaluation

> Implement comprehensive evaluation strategies for LLM applications using automated metrics…

A comprehensive evaluation toolkit for LLM applications, combining automated metrics, human review, LLM-as-Judge scoring, and statistical A/B testing. It lets you prove that a prompt or model change is actually better, not just feels better, and catch regressions before they reach production.

## Use cases
- Comparing different models or prompt variants objectively
- Detecting performance regressions before deployment
- Validating that a prompt change is a real, measurable improvement
- Building evaluation baselines and tracking quality over time
- Measuring RAG retrieval quality with MRR, NDCG, and precision@K
- Integrating eval suites into a CI/CD pipeline

## Benefits
- Replace 'it feels better' with statistically significant proof
- Block regressions automatically before they ship to users
- Triangulate quality across multiple metrics to avoid blind spots
- Scale human-quality judgment affordably with LLM-as-Judge

## What’s included
- Automated metric implementations: BLEU, ROUGE, BERTScore, groundedness
- LLM-as-Judge patterns for pointwise, pairwise, and reference-based scoring
- Human annotation framework with inter-rater agreement (Cohen's Kappa)
- A/B testing with t-tests, p-values, and Cohen's d effect size
- Regression detection against versioned baselines
- LangSmith integration and benchmark runner

## Who it’s for
ML engineers and AI teams who need rigorous, repeatable evaluation of LLM application quality.

## How it runs
When the score rises but users get angrier, your eval set stopped representing production. Metrics are assembled per task, judges never grade their own model, and any 5 percent regression blocks the deploy.
1. Assembles an EvaluationSuite from the metrics that match the task: BLEU and ROUGE for generation overlap, BERTScore for semantic similarity, plus custom metrics like groundedness (NLI entailment against context), toxicity and factuality, because a single metric always has a blind spot.
2. Runs the model over a versioned test dataset and aggregates per-metric mean, std, min and max. The test set stays held out: it is never used to tune prompts, and 20 percent of examples rotate every quarter to prevent contamination and eval hacking.
3. Adds an LLM-as-judge pass with a stronger model as referee, never the model judging itself. Pairwise comparison is preferred over pointwise, the A/B order is randomized and each pair is also scored in reverse to cancel position bias, and the judge prompt carries an explicit rubric with 1 to 10 scales and required reasoning.
4. Samples 10 to 20 percent of outputs for human review with concrete annotation guidelines, then checks inter-rater agreement: Cohen's Kappa above 0.6 before human labels count, and correlation above 0.85 between human and judge scores before the judge is trusted at scale.
5. Compares new results against the versioned baseline with the RegressionDetector: any metric dropping more than 5 percent blocks the deployment, and A/B claims only stand when p is under 0.05 and Cohen's d is at least 0.2.
6. Feeds production failures back into the eval dataset so the suite keeps representing real traffic, and watches for the eval-hacking signal: score going up while user satisfaction goes down means the dataset no longer represents production.

## FAQ
### Does this lock me into LangSmith or a specific model provider?
No. The core is provider-agnostic: automated metrics like BLEU, ROUGE, BERTScore and groundedness, LLM-as-Judge patterns, and the A/B statistics. LangSmith appears as one integration for tracing and benchmark runs, not a requirement.

### We already eyeball outputs before shipping. What does this add?
Statistical footing. Instead of 'it feels better,' you compare variants with t-tests, p-values, and Cohen's d effect size, and the regression detector checks new runs against versioned baselines. A quiet quality drop gets caught before deploy, not after user complaints.

### Will it tell me which prompt to write?
No. It measures whether a prompt or model change is actually better; it does not generate the change. You bring the variants, it brings the evidence, including a human annotation framework with inter-rater agreement when automated metrics are not enough.

## Price
$15, one-time, no subscription. VAT included.

Related guide: [AI and LLM engineering](https://forgehouse.ai/guides/ai-llm-engineering/)