Building an Effective LLM Evaluation Framework from Scratch

You might have probably noticed how LLMs give different answers to the same prompt. This non-deterministic behavior—the very trait that makes generative AI creative and adaptive—completely upends our conventional testing approaches.

Traditional evaluation just doesn't work with LLMs. There's rarely a single "right" answer to measure against when your application generates emails, summaries, or conversations. Plus, LLMs handle an enormous range of inputs—from technical questions to creative tasks—making thorough testing a genuine challenge.

This article explores a practical approach to building an effective LLM evaluation framework that blends quantitative metrics with qualitative criteria, customized for specific needs.

What is an LLM Evaluation Framework?

An LLM Evaluation Framework is a structured methodology for systematically assessing the performance, behavior, and output quality of LLMs. It consists of defined metrics, test cases, evaluation protocols, and feedback mechanisms designed to measure how well an LLM meets specific business objectives and quality standards across dimensions like accuracy, relevance, safety, and consistency.

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Why Traditional Evaluation Approaches Fall Short for LLMs

Evaluating LLMs presents fundamentally different challenges than traditional machine learning. While conventional ML models work well with metrics like accuracy and precision, LLMs generate outputs that are inherently non-deterministic. The same input produces different responses each time, creating a probabilistic output space that defies simple right-wrong classification.

Another crucial difference is the absence of a single correct answer for many LLM tasks. Unlike traditional systems evaluated against predefined ground truth (like spam detection), LLMs often perform open-ended tasks where many valid responses exist. This shifts evaluation methods from exact matching to assessing fuzzy similarities and qualities like style, tone, and safety.

The diverse input range compounds these difficulties. LLM applications span countless domains, from customer service chatbots to code generation. Creating comprehensive evaluation datasets that cover this vast spectrum of potential inputs is a significant challenge, making fixed-dataset evaluations inadequate for assessing true performance.

To overcome these limitations and effectively evaluate LLMs, teams need a structured framework that addresses these unique challenges. The following steps outline a comprehensive approach to building an evaluation system tailored specifically for LLM applications.

LLM Evaluation Framework Step #1: Define Evaluation Goals and Success Metrics

Effective LLM evaluation begins with aligning your assessment framework to specific business objectives. Whether you're building a customer service AI that needs high empathy or a content generation system that demands factual accuracy, your evaluation goals should directly reflect these business needs. This alignment creates the foundation for meaningful measurements rather than vanity metrics.

Once you've identified your business goals, focus on selecting metrics that translate them into specific, measurable criteria. Common evaluation categories include relevance (how well responses match queries), hallucinations (factual accuracy), question-answering accuracy, and toxicity levels. Each application requires different weightings of these metrics based on use case priorities.

Context-specific evaluation is crucial for specialized domains. A medical AI assistant requires different evaluation criteria than a creative writing tool. Domain-specific datasets and metrics should be employed, often with input from subject matter experts who understand the nuances of acceptable outputs in that field.

Many standard metrics won't adequately address all business scenarios, necessitating custom evaluation approaches. For customer-facing chatbots, adherence to brand tone might be critical, while for internal knowledge systems, factual accuracy may take precedence. For example, Galileo's Guardrail Metrics provide a framework for developing these custom evaluation metrics that extend beyond conventional standards.

Documentation and stakeholder alignment are vital components of the evaluation process. Create a simple evaluation criteria document that defines each metric, its calculation method, target thresholds, and business impact. Tools like EvalLM can help refine these criteria through iterative evaluation with user-defined parameters, ensuring your evaluation system grows alongside your evolving business requirements.

LLM Evaluation Framework Step #2: Create Representative Test Cases

Creating comprehensive test cases is critical for thoroughly evaluating LLMs. Your test suite must balance breadth and depth while covering both common scenarios and challenging edge cases. Here are three key approaches for developing representative test cases that will align with your evaluation goals.

First, build a "golden" test set that truly represents your use case. Begin with 10-15 challenging examples that push your model's capabilities, not just straightforward prompts. These should include edge cases like unexpected inputs, queries that might induce biases, or questions requiring deep subject understanding.

For instance, if your application involves Retrieval-Augmented Generation (RAG), ensure your test cases evaluate LLMs for RAG by including scenarios that test the model's ability to retrieve and incorporate external information accurately.

Synthetic data generation provides an efficient way to expand your test coverage. You can use high-quality LLMs themselves to generate test cases, though be cautious about potential hallucinations or repetitive patterns. Here is an implementation of a synthetic test case generator:

1class EvaluationDataset:
2    def __init__(self, test_cases: List[LLMTestCase]):
3        self.test_cases = test_cases
4
5    def generate_synthetic_test_cases(self, contexts: List[List[str]]):
6        for context in contexts:
7            input, expected_output = generate_input_output_pair(context)
8            test_case = LLMTestCase(
9                input=input, 
10                expected_output=expected_output,
11                context=context
12            )
13            self.test_cases.append(test_case)
14
15    def evaluate(self, metric):
16        for test_case in self.test_cases:
17            metric.measure(test_case)
18            print(test_case, metric.score)

Don't overlook bias testing in your evaluation framework. LLMs often reflect societal stereotypes that can manifest in their outputs. Create specific test cases that probe for gender, racial, or other biases to ensure your model performs fairly across diverse contexts.

Consider implementing multi-generation methods to test output consistency. Research shows that generating multiple responses to the same prompt and measuring their consistency can help identify hallucinations. Techniques like SelfCheckGPT employ Natural Language Inference models to compare multiple generations and detect contradictions.

Finally, continuously iterate on your test cases based on real user feedback and real-world tasks evaluation. While you might start with a few dozen examples, robust evaluation requires expanding to hundreds of diverse scenarios. This expansion should occur gradually as you move from model selection to production deployment.

LLM Evaluation Framework Step #3: Establish Evaluation Frequency and Triggers

Integrating LLM evaluations into your CI/CD pipeline creates a systematic approach to quality assessment. Every code change, prompt adjustment, or model update should trigger targeted evaluations, ensuring that performance doesn't degrade with new iterations. This automation mirrors traditional software CI practices but requires specific adaptations for the probabilistic nature of LLMs.

Event-based triggers provide an efficient evaluation framework that balances thoroughness with computational resources. Key triggering events should include model version changes, prompt engineering updates, training data modifications, and user feedback thresholds.

Scheduled evaluations complement event-based testing by providing regular health checks of your LLM system. Start with a comprehensive weekly evaluation using diverse test cases, then adjust frequency based on development velocity and risk tolerance. As your test suite grows, consider implementing tiered testing—running lightweight evaluations hourly while reserving resource-intensive tests for nightly or weekly runs.

Implementation of automated evaluation workflows requires thoughtful architecture. Open-source tools like DeepEval can help integrate evaluations into CI/CD pipelines, with features like caching, parallel execution, and threshold-based pass/fail criteria. Consider incorporating both reference-based evaluations for factual accuracy and reference-free assessments for response quality and consistency.

LLM Evaluation Framework Step #4: Implement Advanced Evaluation Metrics

Implementing robust key performance metrics is essential for thoroughly evaluating LLM performance across various dimensions. These metrics provide quantitative and qualitative measures that help you understand model behavior, identify weaknesses, and guide improvements in your LLM applications.

Hallucination Detection and Factuality Assessment

Understanding LLM hallucinations—outputs that contradict inputs, conflict with known facts, or show internal inconsistencies—is critical for reliable LLM applications. A structured hallucination detection framework can be formalized mathematically by defining a probabilistic framework where the goal is to estimate whether an output contains fabricated information.

The most effective hallucination detection pipeline typically involves three key steps:

• Producing a confidence score
• Calibrating based on input/output attributes
• Applying thresholds to make detection decisions.

Research studies show that combining multiple scoring methods often yields better results than relying on a single approach, especially across diverse datasets and use cases.

Coherence, Fluency, and Relevance Metrics

Beyond factuality, AI fluency and the linguistic quality of LLM outputs significantly impact user experience. Text similarity metrics like BLEU and ROUGE offer quantitative ways to compare generated text against references, though they have limitations when ground truth isn't available.

ROUGE (Recall-Oriented Understudy for Gisting Evaluation) focuses primarily on recall, measuring how much of the reference content appears in the generated text. The calculation involves identifying n-gram overlaps between generated and reference texts.

Response completeness and conciseness metrics assess whether outputs fully address queries while remaining succinct. This balance is crucial for maintaining user engagement and satisfaction. For applications without reference texts, coherence can be evaluated through perplexity measurements or discourse structure analysis.

Safety and Bias Evaluation Approaches

Implementing AI safety metrics to ensure LLM outputs are free from harmful content, unintended biases, and toxic language is increasingly important for responsible AI deployment. Tools like Galileo's toxicity monitoring tools provide robust detection of harmful content, helping you maintain a safe environment for all users. This integration is particularly important for customer-facing applications where safety is paramount.

For bias detection, counterfactual evaluation techniques can be employed—generating variations of prompts that only differ in sensitive attributes (gender, race, etc.) and comparing responses. Significant variations may indicate biased behavior that requires mitigation through model adjustments or filtering mechanisms.

Implementing comprehensive evaluation across these dimensions allows you to build responsible, high-performing LLM applications that meet both technical requirements and ethical standards. When combined with proper monitoring systems, these metrics enable continuous improvement as your applications evolve.

LLM Evaluation Framework Step #5: Implement Feedback Loops with Production Data

The most powerful LLM evaluation frameworks incorporate continuous learning from real-world usage. By implementing feedback loops with production data, you can identify emerging failure modes and enhance your evaluation criteria over time. This creates a virtuous cycle where your evaluation improves as your system encounters more diverse real-world scenarios.

Start by establishing comprehensive monitoring that captures the right signals. Focus on tracking key metrics like perplexity, factual accuracy, and error rates across your production environment.

Real-time monitoring catches issues as they happen, while batch analysis helps identify longer-term patterns. A leading entertainment company leveraged Galileo's real-time monitoring to detect hallucinations and anomalies in their conversational AI, significantly improving user experience by catching problematic outputs before they affected customers.

The integration of Galileo’s dashboard and APIs into their workflows provides real-time insights, allowing the team to monitor and optimize active implementations from a single, streamlined console. This deeper integration into their annotator workflows empowers their operations team to proactively address issues flagged by Galileo's metrics.

Flagging Problematic Outputs Automatically

Implement automated systems that flag potentially problematic outputs for closer review. Define clear thresholds for metrics that matter in your application context. For instance, trigger alerts when toxicity levels exceed a certain threshold or when response similarity to known problematic patterns is detected. These flags should feed directly into your evaluation dataset expansion process.

1def flag_problematic_output(response, metrics):
2    flags = []
3    if metrics['toxicity_score'] > TOXICITY_THRESHOLD:
4        flags.append('high_toxicity')
5    if metrics['factual_accuracy'] < ACCURACY_THRESHOLD:
6        flags.append('potential_hallucination')
7    if flags:
8        # Log to evaluation improvement queue
9        log_to_evaluation_expansion(response, metrics, flags)
10    return flags
11

To make feedback loops sustainable, automate as much of the process as possible. Develop systems that automatically collect problematic outputs, prioritize them based on impact, and schedule them for human review before incorporation into evaluation sets:

• Stay Current with Research: Monitor publications from key conferences (NeurIPS, ACL, EMNLP) and research labs. Implement a validation process for new evaluation techniques before integration.
• Balance Stability and Innovation: | Maintain core metrics for consistent tracking while experimenting with new approaches.
• Combine Automation and Human Review: Establish a hybrid approach where automated systems handle routine assessments, human evaluators focus on edge cases or when automated scores show high variance, and calculate correlation between automated metrics and human ratings to optimize resource allocation
• Implement Tiered Evaluation: Apply CI/CD principles where each system modification undergoes consistent testing via combined automated and human evaluation.
• Conduct Regular Review Cycles: Schedule quarterly assessments to formalize the evaluation of new metrics and determine which to add to your permanent framework.

Remember that the goal isn't just to catch more errors but to develop more nuanced evaluation criteria. Each production failure represents an opportunity to refine how you measure success.

Accelerate Your LLM Evaluation Journey with Galileo

Building an effective LLM evaluation framework requires holistic approaches that evaluate not just the model itself, but the entire system, including prompt templates and integration points that impact user experience.

Galileo provides comprehensive capabilities to streamline your LLM evaluation workflow, addressing the complexity challenges while maintaining rigorous standards. Galileo supports the systematic process needed for meaningful assessment, from establishing benchmarks to enabling continuous improvement:

• Automated benchmark creation and management for consistent performance tracking
• Real-time observability tools that monitor behavior, performance, and costs in production environments
• Customizable evaluation criteria alignment to meet your specific use case requirements
• Integration support to reduce deployment complexity and minimize errors with third-party systems
• Quantitative and qualitative analysis tools to identify improvement opportunities across your entire LLM application

Ready to transform your LLM evaluation process? Request a demo to see how Galileo has become an enterprise-trusted GenAI evaluation and observability platform.