Evaluate LLM Models

Without rigorous evaluation, the risks associated with AI might outweigh its benefits, leading to product failures, reputational damage, and financial loss.

1. What is Evaluating LLMs?

LLM evaluation is the systematic process of assessing an AI model or AI-powered application’s performance, quality, and alignment with predefined goals and user expectations. It is a form of quality control for AI outputs, which are inherently probabilistic and can be unpredictable.

This process moves beyond subjective “vibe checks” or simple eyeballing of results. Instead, it involves a rigorous, iterative lifecycle of testing and analysis using a blend of metrics, benchmarks, and structured methodologies. The evaluation can be:

• Offline: Conducted in a controlled environment before deployment, using established benchmarks or custom-curated example suites to measure performance against a known ‘gold standard’ or a set of quality criteria.
• Online: Performed after a product is launched, using real-world user interactions and feedback to measure performance through techniques like A/B testing and telemetry analysis.

Evaluation is not an isolated activity but a core component of the AI Product Development Lifecycle (AIPDL), particularly in the “Testing and Analysis” phase, which informs the critical “Go/No-Go Decision” for a product launch. It assesses everything from the model’s foundational capabilities to the final application’s impact on user satisfaction and business goals.

2. Why Evaluating LLMs is Critical

Failing to properly evaluate LLMs can lead to catastrophic product failures, reputational damage, financial loss, and significant safety risks.

Evaluation is therefore a critical discipline for several key reasons:

• To Mitigate Risks and Ensure Safety:
  • Hallucinations: LLMs can “hallucinate”—generating plausible but factually incorrect or nonsensical information. A notable real-world case involved a law firm being fined by a court for submitting fictitious legal research generated by ChatGPT, highlighting the severe consequences of unchecked AI outputs.
  • Bias and Harmful Content: Models trained on vast internet datasets can reflect and amplify societal biases related to gender, race, or politics. They can also be prompted to generate toxic, unsafe, or harmful content. Evaluation is essential for detecting and mitigating these risks.
  • Security Vulnerabilities: LLM applications are vulnerable to attacks like prompt injection, where malicious instructions are hidden in inputs to compromise the system. Evaluation helps test a system’s robustness against such adversarial attacks.
• To Ensure Product Quality and Performance:
  • Preventing Overfitting: A primary goal of traditional machine learning evaluation is to ensure a model generalises well to new, unseen data rather than just memorising its training data (a problem known as overfitting). This is achieved by assessing performance on holdout test sets.
  • Driving Systematic Improvement: A core principle of prompt engineering is to “Test Changes Systematically”. This ensures that any modification to a prompt, model, or application architecture is a genuine improvement and not just a random fluctuation. Without systematic evaluation, teams may end up with more errors than insights.
• To Guide Strategy and Development:
  • Evaluation-Driven Development: Many teams now adopt an evaluation-driven development approach, where evaluation criteria are defined before building begins. This ensures the product is designed from the outset to solve a real problem and deliver measurable value. The first code written for GitHub Copilot, for instance, was its evaluation harness.
  • Informing Key Decisions: Evaluation is fundamental to the “Go/No-Go Decision” of whether an AI product is ready for market. It is the biggest hurdle to bringing AI applications to reality.
• To Build User Trust and Manage Expectations:
  • Calibrating Trust: AI is probabilistic, not deterministic. Evaluation helps manage user expectations by making the system’s capabilities and limitations transparent. Features like confidence scores, derived from evaluation, help users calibrate their trust and avoid overreliance.
  • Enhancing Transparency: By explaining AI decision-making (explainability), evaluation makes the “black box” of complex models more understandable, which is crucial for building user confidence and often a regulatory requirement.

3. How to Evaluate an LLM Model

Evaluating an LLM is a complex process that requires a combination of different methods, metrics, and best practices tailored to the specific application.

Evaluation Methods

1. Human Evaluation: Having human evaluators assess the quality of model outputs remains a necessary option, especially for subjective qualities like creativity or tone. However, this method is slow, expensive, and can be inconsistent.
2. Evaluation Against a Gold Standard: This involves comparing the model’s output to a set of “ground truth” or reference answers. While effective for tasks with a single correct answer, it is less useful for open-ended generative tasks where many valid responses are possible.
3. AI as a Judge (LLM-as-a-Judge): This increasingly popular method uses a powerful LLM (like GPT-4) to evaluate the output of another model based on a predefined rubric or set of criteria. An AI judge can explain its decisions and evaluate qualities like relevance, coherence, and factual consistency. However, AI judges can be prone to biases (e.g., favouring their own responses or longer answers), inconsistency, and can be costly to run.
4. Comparative Evaluation: This method ranks models by comparing their outputs against each other, often through pairwise comparisons where an evaluator picks the “winner”. This approach powers leaderboards like LMSYS’s Chatbot Arena and can be more reliable for subjective tasks than assigning absolute scores.
5. Holdout Sets (from traditional ML): This involves splitting a dataset into three parts:
   • A training set to build the model.
   • A validation set to tune hyperparameters (like the learning rate α in gradient descent or the penalty parameter C in SVMs).
   • A test set of unseen data to provide an unbiased assessment of the final model’s performance.

Evaluation Metrics

A robust evaluation pipeline uses a blend of different metrics:

• Traditional Classification Metrics: For tasks that can be framed as classification, standard metrics are used:
  • Accuracy: The percentage of correct predictions.
  • Precision and Recall: Precision measures the accuracy of positive predictions (how many flagged spam emails are actually spam), while recall measures the model’s ability to find all positive cases (how many of all spam emails were found).
  • Confusion Matrix: A table that summarises true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN) to provide a detailed view of performance.
• Language Modelling Metrics: These assess the core predictive accuracy of the underlying language model:
  • Perplexity (PPL): Measures how uncertain a model is when predicting the next token. Lower perplexity indicates a more confident and accurate model.
  • Cross-Entropy: A related metric that measures the difference between the model’s predicted probability distribution and the true distribution of the data.
• Application-Level and Quality Metrics:
  • Functional Correctness: Evaluates if the model’s output achieves its intended function, such as whether generated code compiles and passes unit tests.
  • Factual Consistency / Groundedness: Measures whether the output is factually supported by a given context, a key metric for RAG systems.
  • Semantic Similarity: Uses embeddings and metrics like cosine similarity to measure how close the meaning of the generated response is to a reference answer.
  • The Product Metric Blend: A holistic framework that groups metrics into three buckets:
    • Product Health: User-facing metrics like engagement, user satisfaction, retention, and adoption.
    • System Health: Technical reliability metrics like uptime, error rate, and latency (including Time to First Token (TTFT) and Time per Output Token (TPOT)).
    • AI Proxy Metrics: Model-level performance measures like accuracy, precision, and recall.

Best Practices

• Evaluation-Driven Development: Define evaluation criteria before starting development to ensure the project is aligned with clear, measurable goals.
• Create Custom Evaluation Pipelines: Public benchmarks are often contaminated (i.e., the data is present in the models’ training sets) or may not be relevant to your specific use case. It is crucial to build your own evaluation pipeline with tailored data and metrics.
• Develop Clear Rubrics and Guidelines: For both human and AI evaluators, create a detailed scoring rubric with clear criteria and examples to ensure consistency and reliability.
• Use Data Slicing: Evaluate performance not just on aggregate data but on specific subsets or “slices” to uncover hidden biases or weaknesses in how the model performs for different user groups or data types.

4. Tools & Frameworks

Several tools and frameworks are available to support systematic LLM evaluation:

• Prompt Flow: An open-source tool from Microsoft designed for developing and evaluating prompts and agent profiles. It allows for systematic, batch processing of different prompt variations and can be used to create complex evaluation flows that automatically score outputs against a rubric.
• Evaluation Harnesses: Frameworks like EleutherAI’s lm-evaluation-harness and OpenAI’s evals provide standardised tools for running models against hundreds of public benchmarks.
• Observability and Tracing Platforms: Tools like AgentOps, LangSmith, and Nexus are designed to monitor and trace the behaviour of agents and multi-agent systems, providing insights into their performance, costs, and internal reasoning steps.
• Bias and Fairness Toolkits: Libraries such as Fairlearn and AI Fairness 360 are designed to audit datasets and models for bias. Tools like Evidently AI help monitor for data drift and performance degradation in production.
• General MLOps Tools: Platforms like MLflow offer capabilities for tracking experiments, managing models, and evaluating performance, which are also applicable to LLM workflows.

5. Common Challenges

Evaluating LLMs is a difficult process fraught with several challenges:

• The Open-Ended Nature of Outputs: Unlike traditional ML tasks with a single correct answer (e.g., classification), generative tasks have a vast space of possible valid outputs, making it difficult to evaluate against a single “ground truth”.
• Benchmark Contamination and Saturation: Many public benchmarks are “contaminated,” meaning their data was included in the models’ training sets, leading to inflated and unreliable performance scores. Additionally, as models improve, benchmarks quickly become “saturated,” with top models achieving near-perfect scores, making them less useful for differentiating performance.
• The Black Box Problem: Many foundation models are treated as “black boxes,” with limited transparency into their architecture, training data, or internal workings, making it difficult to understand their strengths and weaknesses fully.
• Cost and Latency: Rigorous evaluation, especially using AI-as-a-judge with powerful models or running large-scale A/B tests, can be computationally expensive and add significant latency to the development cycle.
• Limitations of AI Judges: While powerful, AI judges have their own biases (e.g., position bias, favouring the first option; verbosity bias, favouring longer answers; and self-bias, favouring their own outputs) and can be inconsistent. Their judgements are also subjective and depend heavily on the prompt and model used.

6. Case Study: The Perils of Unevaluated AI in a High-Stakes Environment

A striking real-world example of the failure to evaluate an LLM’s output occurred in 2023 when a law firm used ChatGPT for legal research.

• The Scenario: Lawyers at the firm were preparing a legal brief and used ChatGPT to find relevant case law to support their arguments.
• The Failure: The LLM hallucinated several entirely fictitious legal cases, complete with plausible-looking citations and judicial opinions. The legal team did not independently verify the existence or accuracy of these AI-generated cases and included them in their official court submission.
• The Consequence: The opposing counsel discovered that the cases did not exist, and the law firm faced sanctions from the judge for submitting false information to the court.
• The Lesson: This incident serves as a stark reminder that the outputs of LLMs are probabilistic and not guaranteed to be factual. Evaluation, in this case simple fact-checking and source verification, is not an optional step but a fundamental requirement, especially in high-stakes domains like law, medicine, or finance where factual accuracy is non-negotiable. The failure was not in using AI, but in trusting its output without a rigorous evaluation process.

References

• AI Engineering by Chip Huyen
• ChatGPT: US lawyer admits using AI for case research
• Prompt flow in Azure AI Foundry portal