What Are Evals in AI & How to Become an AI Eval Writer
By
Liz Fujiwara
•
Oct 16, 2025
AI evaluations, or AI evals, are structured assessments designed to measure the performance, accuracy, fairness, and reliability of artificial intelligence systems. As AI technologies become increasingly integrated into everyday applications, ensuring that these systems operate effectively and ethically is more important than ever. AI evals not only identify potential biases and errors but also guide continuous improvements in AI models, helping developers create more trustworthy systems. In this article, we’ll provide an overview of AI evaluations, explain why they are critical for the responsible deployment of AI, and offer guidance on how you can pursue a career as an AI eval writer.
Key Takeaways
AI evaluations are essential for assessing the performance of AI systems, with a focus on reliability, accuracy, fairness, and identifying potential biases.
Unlike traditional testing, AI evaluations provide a scalable and adaptable framework that supports real-time performance adjustments and continuous testing.
Effective evaluations depend on clear criteria, systematic methods, and comprehensive data, emphasizing the importance of context and consistency in AI assessments.
Understanding AI Evals
AI evaluations are structured frameworks designed to assess the performance of AI systems against defined success criteria. They ensure AI models are reliable, accurate, and fair, providing insights that drive continuous improvement. These evaluations go beyond checking whether an AI works, they examine how well it performs under diverse conditions.
A critical aspect of AI evaluations is identifying and mitigating biases within AI systems. Effective evaluations ensure fair outcomes, which is especially important in domains like hiring, lending, and law enforcement. The quality of an AI evaluation depends heavily on the data used; high-quality, reliable, and representative datasets are essential for meaningful results. Additionally, clearly defined performance metrics are necessary, though determining what constitutes ‘good’ performance can be subjective. This underscores the importance of designing meticulous AI evaluations that provide actionable insights and systematic assessments to ensure current reliability and guide future improvements.
Traditional Testing vs. AI Evals
While traditional software testing and AI evaluations share the goal of assessing system performance, they operate differently. Traditional testing relies on pass/fail scenarios to verify whether specific conditions are met. AI evaluations, in contrast, measure quality and effectiveness across multiple dimensions, offering a more nuanced understanding of performance.
AI-powered evaluations provide faster feedback and execute tests at higher speeds, which is particularly valuable in rapid deployment environments requiring continuous assessment. They can operate in real time, adapting to changes in user interactions, a flexibility traditional methods, which depend on pre-set scenarios, often lack. AI evaluations also improve predictability by analyzing past performance to identify potential defects.
Despite these advantages, human judgment remains essential in areas like usability assessments, where AI may struggle with the subtleties of human perception. Overall, AI evaluations provide a scalable, adaptable, and comprehensive approach, making them especially suited to the complexity and dynamism of modern AI systems.
Key Components of Effective AI Evaluations
Effective AI evaluations rely on several key components:
Clear definition of the evaluator’s role: Establishing responsibilities provides structure and clarity to the assessment process.
Systematic and thorough evaluation: Ensures that all relevant aspects of AI performance are consistently assessed.
Defining AI objectives: Clearly outlining what the AI is supposed to accomplish guides the entire evaluation process.
The role of context in AI evaluation
Evaluators require comprehensive data to analyze AI performance effectively, including relevant information about AI interactions and decisions.
Clear definitions of success and failure translate user expectations into measurable criteria that guide assessments.
A consistent evaluation system standardizes results, making them comparable across different assessments. By defining success, evaluators ensure alignment with AI system goals and maintain consistent labeling
These components collectively form the backbone of effective AI evaluations, enabling the creation of high-quality evals that provide actionable insights by measuring specific subcomponents and intermediate values within a complex evaluation infrastructure.
Types of AI Evaluations
AI evaluations take several forms, each serving distinct purposes in the development process. Human evaluation involves direct feedback from users or expert evaluators, helping refine AI systems based on real-world interactions.
LLM-as-a-Judge evaluations are particularly useful for assessing open-ended text outputs efficiently. This approach offers:
A cost-effective alternative to traditional human evaluations
Customizability to enhance assessment quality
Operation in both offline evaluations (e.g., pairwise comparisons) and ongoing assessments for continuous monitoring
Easy adjustments to LLM evaluators as evaluation needs evolve, allowing quick adaptation without retraining
Code-based evaluations use objective criteria such as format compliance or runtime errors, ideal for AI systems involved in software development or data processing. Each evaluation type plays a vital role in improving AI accuracy and responsiveness, making them integral to system development.
Common Evaluation Criteria for AI Outputs
When evaluating AI outputs, several common criteria are used to ensure they meet user expectations and system objectives. Accuracy is a primary criterion, measured by tracking the percentage of factual errors against reliable sources, ensuring that the information provided by the AI is factually correct.
Relevance assesses how well the AI’s response addresses a specific query, typically scored on a 1–5 scale. Coherence is another essential criterion, rated on a scale to evaluate whether users can logically follow the ideas presented in the AI’s output.
Completeness measures the extent to which a response addresses all aspects of a question, often expressed as a percentage. Helpfulness is determined by rating how effectively a response enables users to achieve their intended goals.
Safety is evaluated by checking for harmful content, typically tracked with a binary flag indicating the presence or absence of such content. Tone and style compliance involves assessing how well the AI’s output adheres to defined communication guidelines, measured by a style compliance score and specific formatting.
Contextual awareness evaluates the AI’s ability to remember previous interactions, often measured by tracking the percentage of interactions remembered. Together, these criteria establish a framework for evaluating AI outputs, ultimately improving performance and user satisfaction.
Implementing AI Evaluations
Implementing AI evaluations begins with creating golden examples, which serve as representative benchmarks for assessing AI performance. These examples define what success looks like for various evaluation criteria, providing a clear reference point for evaluators.
Developing tailored evaluation prompts is crucial for effective assessments, guiding the process to examine each aspect of the AI’s performance. Evaluation tuples can be generated through prompt optimization, cross-product filtering, or direct generation by an LLM, depending on the evaluation’s specific needs.
Automated evaluation processes can be established by using evaluation prompts with a judge LLM for each dimension and running synthetic data queries through the actual system. This approach enables efficient, scalable evaluations and ensures that AI systems are continuously monitored and improved.
Tools and Techniques for Running Evals
Several tools and techniques support effective AI evaluations. Automated evaluators provide various automatic evaluation methods, including model-graded evaluations for subjective tasks, and allow customization of prompts, output parsing, and exception management.
To run evals locally, the evals package can be installed via pip. Autoevals supports a wide range of evaluation types, such as factuality checks, moderation, and summarization, making it a versatile tool for AI assessments. Comprehensive documentation guides users through usage and methodologies, including new evals.
Error analysis is critical for identifying failure modes and informing evaluations, helping evaluators pinpoint unique issues related to specific applications and datasets, thereby tailoring assessments to real-world conditions.
Avoiding Common Pitfalls in AI Evaluations
Avoiding common pitfalls is essential for high-quality AI evaluations. Key practices include:
Using clear, specific, and measurable criteria to prevent vague assessments.
Providing detailed rubrics with examples for each rating level to clarify expectations.
Ensuring consistency throughout the evaluation process.
Over-reliance on LLM judges can result in inadequate multi-dimensional evaluations, so incorporating varied assessment methods is essential. Improving inconsistent human evaluations involves regularly validating LLM evaluations against human judgments. Evaluators should also address edge cases, such as ambiguous queries, to reveal hidden AI model flaws.
Learning from past mistakes, like the Microsoft Tay incident, highlights the critical need for robust evaluation processes. When designing evaluations, consider the following:
Leverage error analysis to guide the development of criteria that reflect actual system behavior.
Consider trade-offs between false positives and false negatives to inform evaluation strategies.
Ensure a balanced approach in evaluation.
Case Study: Fonzi's Approach to AI Evaluations
Fonzi, a curated AI engineering talent marketplace, employs structured evaluations to enhance the hiring process. These evaluations integrate fraud detection and bias auditing, ensuring the quality and reliability of hiring. The candidate experience is preserved and elevated by Fonzi, keeping talent engaged and well-matched.
The approach taken by Fonzi is scalable, supporting organizations from startups to large enterprises in building AI teams. Fonzi makes hiring fast, consistent, and scalable, with most hires happening within three weeks. This case study illustrates the effective implementation of structured AI evaluations in real-world scenarios, driving efficiency and quality.
Beyond Traditional Metrics: Monitoring What Really Matters
Monitoring beyond traditional metrics captures additional key performance dimensions in AI products. Evaluating a model’s ability to express uncertainty ensures it refuses appropriately without avoiding answerable questions.
Error analysis highlights critical path failure cases for users, informing necessary adjustments and tracking gradual improvements in error rates. Techniques like user input and negative feedback help identify problematic traces.
Semantic search helps users find conceptually similar problems when reviewing traces. Filtering by metadata and keyword searching enhance the review process. Clustering techniques group similar traces, aiding reviewers in navigating complex evaluation results, including reference-based checks.
These advanced techniques ensure evaluations go beyond generic metrics, focusing on what truly matters for AI performance.
Becoming an AI Eval Writer
The ability to write effective evaluations is becoming a crucial skill for professionals involved in AI product development. Writing evaluations requires a deep understanding of AI systems and the specific criteria used to assess their performance, and it is increasingly recognized as essential for product managers and other AI professionals.
To become an AI evaluation writer, one must:
Learn to create high-quality evaluations that guide the continuous improvement of AI systems.
Develop tailored evaluation prompts.
Use LLM judges effectively.
Understand the nuances of different assessment methods.
By mastering these skills, individuals can contribute significantly to the development and refinement of AI products.
Summary
In summary, AI evaluations are essential for ensuring the reliability, accuracy, and fairness of AI systems. They provide a structured framework for assessing AI performance against predefined criteria, driving continuous improvement and mitigating biases. By understanding the key components and types of AI evaluations, implementing effective evaluation processes, and using advanced tools and techniques, professionals can create high-quality evals that enhance AI performance.
The transition from traditional testing to AI evaluations highlights the unique benefits and challenges of assessing AI systems. Fonzi’s approach demonstrates the real-world applications and impact of structured evaluations. As AI continues to evolve, proficiency in writing evaluations will be a valuable skill for anyone involved in AI product development. By applying these insights, professionals can ensure AI systems are not only effective but also ethical and trustworthy, making this knowledge increasingly relevant.
