AI Evaluation Frameworks

Performing AI model evaluations manually becomes prohibitively expensive and time-consuming as the number of samples grows. Using evaluation frameworks automates and standardizes the evaluation process, enabling continuous quality management. This page organizes the features and how to choose between major AI evaluation frameworks.

Target audience: Those developing or operating LLM applications, or those considering evaluation automation.

Estimated learning time: 25 minutes to read

Prerequisites: What Is AI Evaluation?

Why Evaluation Frameworks Are Needed

Manual evaluation has the following limitations:

• Scale problem: Evaluating hundreds to thousands of samples by hand is not realistic
• Reproducibility problem: Standards vary between evaluators
• Continuity problem: Re-evaluating from scratch every time the model is updated is difficult
• Cost problem: Expert evaluation is expensive and cannot be conducted frequently

Evaluation frameworks are the toolset that solves these problems.

Key Frameworks

1. LM Evaluation Harness (EleutherAI)

LM Evaluation Harness is an open-source LLM evaluation framework developed by EleutherAI.

• Number of supported benchmarks: 100+ (covers major benchmarks like MMLU, HellaSwag, TruthfulQA, GSM8K)
• Evaluation methods: Supports both zero-shot and few-shot evaluation
• License: MIT License (commercial use allowed)
• Main use: Research-purpose model comparison, comprehensive performance evaluation of new models

Features

- Supports both Hugging Face models and API models
- Run multiple benchmarks in batch with a single command line
- Adopted as the evaluation infrastructure for the Open LLM Leaderboard (HuggingFace official ranking)

Best suited for: Baseline comparison in research/papers, performance evaluation of open-source models

---

2. Ragas

Ragas is a framework specialized for evaluating the quality of RAG (Retrieval-Augmented Generation) systems.

• Developer: Exploding Gradients (open-source)
• Evaluation method: LLM-as-a-Judge (GPT-4 etc. function as evaluators)
• License: Apache 2.0
• Main use: Quality evaluation of the entire RAG pipeline

Four key RAG evaluation metrics

Metric	Definition	What It Measures
Faithfulness	Whether the answer is based only on the retrieved context	Low hallucination
Answer Relevancy	Whether the answer is appropriate for the question	Responsiveness to the question
Context Precision	Whether relevant information is included in the retrieved context	Search accuracy
Context Recall	Whether information needed to generate an answer was retrieved	Search coverage

graph LR
    Q["Question"] --> R["Retrieval"]
    R --> C["Context"]
    C --> G["Generation"]
    G --> A["Answer"]

    C --> CP["Context Precision\nContext Recall"]
    A --> F["Faithfulness\nAnswer Relevancy"]

Best suited for: Internal document search, automated FAQ responses, knowledge base integration apps

---

3. DeepEval

DeepEval is a testing and evaluation framework for LLM applications.

• Developer: Confident AI (open-source)
• License: Apache 2.0
• Main use: Quality testing of production LLM apps, incorporating into CI pipelines

Key metrics

Metric	Details
G-Eval	Metric where an LLM scores according to custom evaluation criteria
Hallucination	Proportion of content containing information that differs from facts
Toxicity	Detection of harmful or inappropriate content
Summarization	Summary quality (faithfulness, relevance)
Answer Relevancy	Whether the answer is relevant to the question

Example CI integration

# Example integration with pytest (conceptual code)
from deepeval import assert_test
from deepeval.metrics import HallucinationMetric
from deepeval.test_case import LLMTestCase

def test_hallucination():
    test_case = LLMTestCase(
        input="Who developed Claude?",
        actual_output="Claude is an AI developed by Anthropic.",
        context=["Anthropic is an AI safety research company that developed Claude."]
    )
    metric = HallucinationMetric(threshold=0.5)
    assert_test(test_case, [metric])

Best suited for: Unit testing of LLM apps, quality gates before production deployment

---

4. Braintrust

Braintrust is a platform for LLM experiment management and evaluation.

• Form: SaaS (cloud service) + open-source SDK
• Main use: Continuous evaluation in production environments, A/B testing of prompts

Key features

• Experiment management: Manage prompt change history tied to evaluation results
• A/B testing: Compare performance of multiple prompts
• Prompt version management: Version-control prompts like code
• Dashboard: Visualization of evaluation results and trend analysis

Best suited for: Continuous quality monitoring in production environments, team-based prompt management

---

5. Prometheus (Open-Source Evaluation LLM)

Prometheus is an open-source LLM fine-tuned specifically for evaluation tasks.

• Developer: KAIST (Korea Advanced Institute of Science and Technology)
• Base model: Llama 2 / Llama 3
• Main use: LLM-as-a-Judge evaluation without depending on GPT-4

Features

• Enables high-quality evaluation without using GPT-4 as an evaluator
• Can dramatically reduce evaluation costs
• Evaluation criteria (rubrics) can be freely customized

Best suited for: Cases where you want to avoid dependency on the GPT-4 API, cases where minimizing evaluation costs is important

---

6. OpenAI Evals

OpenAI Evals is an evaluation framework published by OpenAI.

• License: MIT License
• Features: Easy creation of custom evals; evaluation definitions written in YAML files
• Main use: Custom evaluation of OpenAI models, sharing evaluation sets

Best suited for: Evaluation of apps using GPT-4/GPT-4o, providing evaluation sets to the community

---

Framework Comparison Table

Framework	Specialized Domain	License	Learning Cost	Main Use
LM Evaluation Harness	General LLM evaluation, benchmarks	MIT	Low–Medium	Research, model comparison
Ragas	RAG system evaluation	Apache 2.0	Low	RAG pipeline quality measurement
DeepEval	LLM app unit testing	Apache 2.0	Medium	CI integration, production testing
Braintrust	Experiment management, A/B testing	SaaS	Low (UI-focused)	Continuous production evaluation
Prometheus	Cost-reduction evaluation LLM	Apache 2.0	Medium–High	Evaluation without GPT-4
OpenAI Evals	Custom eval creation	MIT	Low–Medium	OpenAI model evaluation

Evaluation Pipeline Structure

In actual development, evaluation is structured as a continuous pipeline.

graph TD
    A["Development"] --> B["Unit Testing\n(DeepEval, etc.)"]
    B --> C{Evaluation passed?}
    C -->|Yes| D["Staging"]
    C -->|No| A
    D --> E["Integration Testing\n(Braintrust, etc.)"]
    E --> F{Quality standard passed?}
    F -->|Yes| G["Production"]
    F -->|No| A
    G --> H["Continuous Monitoring\n(periodic evaluation batches)"]
    H --> I{Quality degradation detected?}
    I -->|Yes| J["Alert\n→ Investigate · Fix"]
    I -->|No| H

Evaluation in the Development Phase

• Purpose: Confirming that changes haven’t broken existing functionality (regression testing)
• Tool example: DeepEval (pytest integration)
• Execution timing: On commit or PR creation

Evaluation in the Testing Phase

• Purpose: Confirming that production-equivalent quality standards are met
• Tool examples: Ragas (for RAG systems), LM Evaluation Harness (benchmark comparison)
• Execution timing: Before deployment

Evaluation in the Production Monitoring Phase

• Purpose: Early detection of quality degradation and anomalies
• Tool example: Braintrust (dashboard, trend analysis)
• Execution timing: Periodic batches (daily, weekly)

How to Choose a Framework

graph TD
    A["What do you want to evaluate?"] --> B{Is it a RAG system?}
    B -->|Yes| C["Choose Ragas"]
    B -->|No| D{Is it research / model comparison?}
    D -->|Yes| E["Choose LM Evaluation Harness"]
    D -->|No| F{Do you want CI integration?}
    F -->|Yes| G["Choose DeepEval"]
    F -->|No| H{Is it continuous production monitoring?}
    H -->|Yes| I["Choose Braintrust"]
    H -->|No| J["Combine multiple based on use case"]

Summary

• Using evaluation frameworks enables automation, standardization, and continuity of evaluation
• For RAG systems, use Ragas; for CI integration, use DeepEval; for production monitoring, use Braintrust
• For research and model comparison, LM Evaluation Harness is most widely used
• For reducing evaluation costs, Prometheus is effective and eliminates GPT-4 dependency
• In production, structure an evaluation pipeline across three phases: “development → testing → production monitoring”

Frequently Asked Questions

Q: I’m building a RAG system. Which framework should I use?

A: I recommend Ragas as the first choice. It systematically evaluates the entire RAG pipeline with four metrics: Faithfulness, Answer Relevancy, Context Precision, and Context Recall. It’s also relatively easy to get started — install with pip install ragas and begin evaluation in a few dozen lines of code.

Q: Can DeepEval and Ragas be used together?

A: Yes. DeepEval covers general LLM app unit testing, while Ragas specializes in RAG-specific evaluation. For LLM apps that include a RAG system, combining both enables more comprehensive evaluation.

Q: Doesn’t using GPT-4 for evaluation make it expensive?

A: When using GPT-4 for LLM-as-a-Judge evaluation, evaluating large numbers of samples can get costly. Cost reduction strategies include using Prometheus (an open-source LLM specialized for evaluation), selecting a subset of evaluation samples (narrowing from full evaluation to only representative samples), and using cheaper models (like GPT-4o-mini) as evaluators.

Q: If evaluation results are numerically high, does that always mean it’s a good model?

A: Judging by numbers alone is dangerous. Evaluation metrics only measure specific aspects. For example, a high Faithfulness score doesn’t mean the answer is useful. Also, if the evaluation dataset itself has biases or skew, scores may not reflect actual user satisfaction. Combining multiple metrics and conducting regular human evaluation in parallel is important.

---

Next step: What Is Responsible AI?