📌 TL;DR
Developing and deploying successful AI products, especially those powered by Large Language Models (LLMs), hinges not on complex architectures or fancy tools, but on a relentless focus on measurement and iteration. Unlike traditional software, LLMs produce non-deterministic outputs, making conventional evaluation methods insufficient and introducing challenges like factual inaccuracy, bias, and a lack of coherence. Many teams err by focusing on building features before establishing how to measure their efficacy, leading to wasted effort and a lack of trust in their systems.
Successful AI development requires human-centered evaluation, often involving domain experts who provide pass/fail judgments augmented by detailed critiques. This process, dubbed "Critique Shadowing," helps articulate evaluation criteria that evolve with understanding, addressing the "criteria drift" phenomenon. While human evaluation is the gold standard for nuance, it's slow and costly. Thus, LLM-as-a-Judge is a crucial technique, where LLMs evaluate other LLM outputs, often employing methods like Question-Answer Generation (QAG) and Chain-of-Thought (CoT) prompting to enhance reliability and accuracy.
A simple, customized data viewer is identified as the most impactful AI investment, removing friction for domain experts to analyze AI behavior and accelerate iteration. Furthermore, shifting AI roadmaps from counting features to counting experiments fosters a culture of learning and adaptation, prioritizing feasibility and clear decision points over rigid delivery dates. This requires robust evaluation infrastructure as its foundation. Ultimately, success comes from continuously observing data, systematically analyzing errors (bottom-up is preferred), and empowering domain experts directly, even those without AI knowledge, to iterate on prompts and system behavior. Synthetic data also plays a vital role in bootstrapping evaluation when real data is scarce.
📚 Sources
AI Model Evaluation Explained by Miquido
LLM Evaluation Metrics by Dezlearn
The One Skill Every AI PM Needs (That Nobody Taught You) by Aakash Gupta and Hamel Husain
A Field Guide to Rapidly Improving AI Products by Hamel Husain
Creating a LLM-as-a-Judge That Drives Business Results by Hamel Husain
Your AI Product Needs Evals by Hamel Husain
Frequently Asked Questions (And Answers) About AI Evals by Hamel Husain
A Step-By-Step Guide to Evaluating an LLM Text Summarization Task by Jeffrey Ip
How I Built Deterministic LLM Evaluation Metrics for DeepEval by Jeffrey Ip
LLM Evaluation Metrics: The Ultimate LLM Evaluation Guide by Jeffrey Ip
LLM-as-a-Judge Simply Explained: A Complete Guide to Run LLM Evals at Scale by Jeffrey Ip
RAG Evaluation Metrics: Assessing Answer Relevancy, Faithfulness, Contextual Relevancy, And More by Jeffrey Ip
A Palantir Manual for Evaluating Generative AI by Arnav Jagasia and Colton Rusch
Human-Centered AI Evaluation: Best Practices for Accuracy & Inclusivity by Conor Bronsdon
Maximizing AI Potential: Comprehensive Evaluation Steps by Conor Bronsdon
LLM Evaluations: from Prototype to Production by Mariya Mansurova
LLM evaluation: Metrics, frameworks, and best practices by Dave Davies
Red Teaming for Large Language Models: A Comprehensive Guide by Deval Shah
Who Validates the Validators? Aligning LLM-Assisted Evaluation of LLM Outputs with Human Preferences by Shreya Shankar, J.D. Zamfirescu-Pereira, Björn Hartmann, Aditya G. Parameswaran, and Ian Arawjo
🧩 Key Terms
AI evaluation: The systematic process of assessing the performance, accuracy, and reliability of AI models, especially generative AI, to ensure they meet objectives without producing harmful content. It's crucial for continuous improvement and demonstrating system reliability in real-world applications.
Generative AI: AI systems that produce content like text, images, or music, characterized by probabilistic outputs that can vary significantly even with the same prompts, making their evaluation distinct from traditional machine learning models.
LLM-as-a-Judge: A method where a Large Language Model (LLM) is used to evaluate the outputs of other LLMs or AI systems. This approach helps scale evaluations, especially for subjective criteria that are hard to quantify with traditional metrics like creativity or nuance.
Error analysis: The process of systematically reviewing AI outputs to identify, categorize, and understand failure modes, consistently revealing the highest-ROI improvements. A "bottom-up" approach, starting with actual data and allowing metrics to naturally emerge, is often more effective than a "top-down" approach that begins with predefined metrics.
Data viewer: A customized interface that allows anyone on an AI team, including non-engineers and domain experts, to easily examine and annotate what their AI is actually doing, showing all context in one place and facilitating quick feedback capture. This crucial investment significantly accelerates iteration.
Criteria drift: A phenomenon where evaluation criteria evolve as evaluators observe more model outputs. People define their criteria more clearly through the process of grading outputs, implying that it is impossible to completely pre-determine all evaluation standards. Trustworthy systems embrace this reality and treat evaluation criteria as living documents.
Synthetic data: Artificially generated data used for testing and evaluation, especially useful when real-world data is scarce or to comprehensively cover specific scenarios and edge cases. LLMs are surprisingly good at generating diverse and realistic synthetic user prompts and underlying test data.
Binary judgments: A recommended approach for evaluation where evaluators make a clear pass/fail decision rather than using complex numerical scales (e.g., 1-5). This clarity provides actionable insights and forces the articulation of precise expectations, with any nuances captured in accompanying qualitative critiques.
Retrieval-Augmented Generation (RAG): An AI architecture where an LLM's output is informed by external data retrieved at runtime from a knowledge source (e.g., a document database, vector store, or API) based on the user's input. RAG evaluation focuses on assessing the quality of both its "retriever" and "generator" components.
Red teaming: A proactive and systematic process involving diverse teams who challenge an AI system to identify vulnerabilities, biases, and potential misuse before deployment. It helps develop robust mitigation strategies and enhances the safety and reliability of AI systems.
💡 Key Insights
Focus on measurement, not just tools: Successful AI teams obsess over measurement and iteration rather than just debating which tools or frameworks to use. A common pitfall is that many AI teams invest heavily in complex systems but fail to measure if their changes are actually helping or hurting.
Error analysis yields highest ROI: Error analysis is the single most valuable and consistently highest-ROI activity in AI development, revealing actionable insights by examining actual data and identifying common failure modes. For example, a "bottom-up" approach at Nurture Boss uncovered that just three issues accounted for over 60% of all problems.
Simple data viewers are critical: The most important investment for any AI team is building a customized interface (data viewer) that allows anyone to easily examine what their AI is actually doing and capture feedback. Teams with thoughtfully designed data viewers iterate tenfold faster than those without them, with minimal investment.
Empower domain experts: The individuals best positioned to improve an AI system are often those who understand the domain deeply, regardless of their AI expertise. Giving domain experts tools to directly write and iterate on prompts (e.g., via integrated prompt environments) removes unnecessary friction and technical jargon.
Synthetic data is highly effective: Synthetic data, particularly user inputs generated by LLMs, is remarkably effective for evaluation and can bootstrap the evaluation process when real data is scarce. Key principles include diversifying datasets, generating realistic user inputs (not AI outputs), incorporating real system constraints, and verifying scenario coverage.
Trust in evaluations requires clarity: Maintaining trust in evaluation systems is paramount and best achieved by favoring binary (pass/fail) decisions paired with detailed critiques over arbitrary numerical scales (e.g., 1-5). This clarity makes results immediately actionable and captures nuance through qualitative feedback.
AI roadmaps should count experiments, not features: Traditional feature-based roadmaps fail with AI; instead, successful teams structure roadmaps around a cadence of experimentation, learning, and iteration, defining success by progressive levels of capability rather than fixed outcomes. This approach manages inherent uncertainty and provides clear decision points for leadership.
Evaluation infrastructure is the foundation: Robust and trusted evaluation infrastructure is the key to making an experiment-based roadmap work, enabling rapid iteration, hypothesis testing, and building on successes. Without it, teams are simply guessing whether their experiments are effective.
Human judgments are gold standard, but scalable automation is key: While human evaluation captures crucial subjective qualities like creativity, relevance, and fluency, it is time-consuming and expensive. LLM-as-a-Judge offers a scalable alternative that, when properly designed (e.g., with binary criteria, Chain-of-Thought, few-shot examples), can align well with human judgments.
Address system-level challenges, not just model-level: Evaluating an LLM-based system involves considering prompts, retrieval modules (in RAG), and post-processing, not just the raw model capabilities. For example, irrelevant retrieval context or a generator ignoring context can completely break an otherwise good RAG pipeline, highlighting the need for component-level evaluation.
🚀 Use Cases
AI assistant improvement:
Context: Nurture Boss deployed an AI assistant for the apartment industry.
Motivation: To improve the AI's performance and precisely identify its failure modes.
How it Works: The team built a simple data viewer to examine conversations between their AI and users, capturing open-ended notes on failure modes. They then employed a "bottom-up" error analysis approach, starting with actual data to let metrics naturally emerge, which helped in building a taxonomy of common issues like conversation flow, handoff failures, and rescheduling problems.
Challenges: Common metrics often miss domain-specific issues.
Avoidance: A "bottom-up" approach, which allowed issues to emerge from raw data, was far more effective than starting with predefined metrics.
Implementation: Requires a simple, customized data viewer that displays all necessary context, like full chat history and scheduling details, in one place.
Real estate CRM AI assistant evaluation:
Context: Rechat, a SaaS for real estate professionals, built an AI assistant named "Lucy" to streamline tasks such as finding listings.
Motivation: To ensure the AI assistant accurately and reliably finds listings based on varied user queries, integrating seamlessly with the CRM.
How it Works: The LLM converts user requests (e.g., "find listings with 3 bedrooms under $2M in San Jose") into CRM queries, with unit tests and assertions verifying expected results, such as the number of listings returned for a given scenario. Generic tests were also implemented to prevent issues like exposing internal UUIDs.
Challenges: Ensuring proper handling of edge cases and preventing the unintentional surfacing of sensitive system information.
Avoidance: Implement scoped unit tests for specific features and scenarios, and use assertions (e.g., regex) to prevent data leakage.
Implementation: Involves writing assertions that run quickly and cheaply, leveraging existing analytics systems (like Metabase) to visualize test results and error prevalence over time.
Content moderation project planning:
Context: A content moderation project faced inherent uncertainties regarding feasibility and suitable machine learning techniques.
Motivation: To systematically explore possible approaches, manage expectations with leadership, and ensure resources were not wasted on open-ended exploration.
How it Works: The roadmap was structured around a cadence of experimentation and learning, rather than promising specific features or capabilities. Progress was measured through a "capability funnel," breaking down AI performance into progressive levels of utility.
Challenges: The inherent uncertainty of AI development requires flexible planning.
Avoidance: Leadership was reassured through time-boxed exploration phases with clear decision points for pivoting if goals weren't met within the timeframe.
Implementation: Building robust evaluation infrastructure early on is fundamental, enabling rapid iteration and learning.
SQL agent development:
Context: Developing an LLM-powered agent to answer customer questions by generating and executing SQL queries against an e-commerce database.
Motivation: To provide immediate, clear answers and improve customer's ability to interpret data, reducing reliance on complex reports.
How it Works: The LLM agent is equipped with a tool to execute SQL queries. Evaluation begins by gathering a small, diverse dataset of questions (including "happy path" scenarios, edge cases like personal questions, and adversarial inputs like jailbreak attempts).
Challenges: Ensuring generated SQL queries are valid and executable, and gracefully handling challenging user inputs.
Avoidance: Manual testing with a small, representative dataset helps understand initial quality and define expected behavior for edge cases before scaling. Functional testing verifies query validity.
Implementation: Utilize LLM-as-a-Judge for evaluating factual correctness against ground truth answers. Employ observability platforms (e.g., Evidently) for real-time monitoring and tracing of LLM operations, capturing customer questions, LLM responses, and intermediate steps.
Text summarization evaluation:
Context: Evaluating LLM-generated text summaries for qualities like conciseness, comprehensiveness, and factual alignment.
Motivation: Traditional metrics (e.g., ROUGE, BertScore) often focus on surface-level features like word overlap, failing to capture semantic nuance or effectively assess disjointed information from RAG sources. LLM-Evals, while better, can suffer from arbitrariness and bias in overlooking factual inconsistencies or essential details.
How it Works: Employs the Question-Answer Generation (QAG) framework, where closed-ended questions are generated from either the original text or the summary. This technique removes stochasticity by constraining verdicts to binary "yes" or "no" answers, leading to more deterministic and reliable evaluations.
Challenges: Ensuring reliability and accuracy in LLM-powered evaluations.
Avoidance: QAG helps overcome arbitrariness and bias by providing structured, closed-ended questions, thereby allowing for fine-grained control and reliable scoring.
Implementation: Can be implemented using DeepEval's DAG (Deep Acyclic Graph) metric, which structures evaluations as decision trees powered by LLM judges to enforce specific formatting, section ordering, or content quality requirements, ensuring highly deterministic outcomes.
Chatbot quality monitoring:
Context: A company with a customer support chatbot needs continuous evaluation and improvement in a staging environment.
Motivation: To monitor the bot's performance, identify quality regressions (e.g., confusing answers, outdated information), and ensure ongoing user satisfaction.
How it Works: Tools like W&B Weave are set up to log detailed conversation flows (user message, bot reply, chain-of-thought reasoning) from simulated conversations. Regular runs (e.g., nightly builds) are analyzed to detect performance drops or improvements in metrics like politeness scores.
Challenges: Diagnosing root causes of issues and effectively communicating findings to product managers.
Avoidance: Log every detail about the product's operation, including customer questions, LLM-generated answers, and all intermediate agent steps. Utilize visual analytics platforms for comparing runs, breaking down accuracy by category, and inspecting traces to pinpoint issues.
Implementation: Integrate logging tools (e.g., W&B Weave, Tracely) into the development cycle to automate tracking and provide real-time insights into LLM behavior.
Prompt engineering optimization:
Context: A developer is trying to optimize the prompt for a summarization task, exploring multiple candidate prompt templates.
Motivation: To efficiently test various prompt variations, compare their outputs, and select the best one based on multiple quality criteria.
How it Works: Multiple prompt variants are run on a set of test articles, with results (summaries, ROUGE scores, human preference scores) logged to a platform like W&B Weave. This allows for side-by-side comparison and aggregated analysis of different prompt performance.
Challenges: Manually trying many prompts is inefficient, and aggregating results across variations can be complex.
Avoidance: Use structured evaluation tools that manage the complexity of testing numerous prompts and aggregating their results, facilitating informed decisions.
Implementation: Employ prompt playgrounds (e.g., Arize, Langsmith, Braintrust) or integrated prompt environments that expose prompt editing directly within the application's user interface, enabling non-technical domain experts to contribute.
Bias and ethical consideration in LLMs:
Context: Addressing potential biases (e.g., racial, gender, political) and the generation of harmful or offensive content by LLMs, especially in applications impacting critical decisions like loan approvals or recruitment.
Motivation: To ensure AI systems align with ethical standards, do not perpetuate societal inequalities, or spread disinformation.
How it Works: Specialized tools and custom evaluation metrics are used to detect toxicity and bias in LLM outputs. Red-teaming strategies involve posing "contrastive prompts" (e.g., asking about different demographic groups) to identify and quantify disparities in responses.
Challenges: Identifying subtle, nuanced biases, ensuring diverse perspectives in testing, and preventing the unintentional exploitation of discovered vulnerabilities.
Avoidance: Employ Chain-of-Thought prompting to guide LLMs in evaluating bias. Prioritize user privacy (e.g., protecting Personally Identifiable Information - PII) and foster diversity within red teaming teams to ensure comprehensive risk assessment.
Implementation: Integrate ethical guardrails and bias audit components directly into the AI evaluation framework. Regular calibration and alignment checks between automated evaluations and human judgment are essential.
🛠️ Now / Next / Later
Now
Identify your principal domain expert and establish a simple data viewer: Pinpoint the key individual whose judgment is crucial for your AI product's success. Simultaneously, build a simple, customized data viewer (achievable in hours with AI-assisted development tools) to enable easy observation of AI interactions and feedback capture. This removes all friction from looking at data, a critical first step.
Conduct bottom-up error analysis with binary judgments and critiques: Engage your domain expert in manually reviewing a small, diverse dataset (initially 30-50 examples, expanding until no new failure modes emerge) of AI outputs. Insist on clear pass/fail judgments for desired outcomes, each accompanied by detailed critiques explaining the reasoning and capturing nuance. This activity consistently yields the highest-ROI improvements by forcing precise articulation of criteria.
Generate targeted synthetic data: Leverage LLMs to create realistic, diverse user inputs (not AI outputs) that cover key features, scenarios, and user personas relevant to your AI product, even if real user data is scarce. Ensure this synthetic data is grounded in real system constraints to effectively test edge cases and bootstrap your evaluation process.
Next
Build your first LLM-as-a-Judge and calibrate with domain expert: Once you have a sufficient set of human-labeled pass/fail judgments and critiques, use these as few-shot examples to train an LLM-as-a-Judge. Iteratively refine the judge's prompt until high agreement (e.g., >90%) is achieved with your domain expert's evaluations. Emphasize clear, binary criteria in the judge's prompt to ensure consistency.
Shift to an experiment-based roadmap and track key metrics: Transition your AI roadmap to emphasize counting experiments and iterations rather than fixed features or delivery dates. Prioritize a focused set of crucial metrics (e.g., 1-2 custom task-specific, 2-3 generic system-specific) that directly align with your specific use case and architecture, thereby avoiding overwhelming "metric sprawl".
Automate continuous evaluation and tracing: Integrate your evaluation system into your CI/CD pipelines to run unit tests and LLM-as-a-Judge evaluations automatically on every code change or at a set cadence. Implement robust tracing and logging capabilities (e.g., with tools like LangSmith or W&B Weave) to capture all intermediate steps and outputs, essential for debugging and continuous quality monitoring in production.
Later
Explore advanced LLM judge techniques and specialized evals: Invest in more targeted LLM judges or code-based assertions for specific, critical error types identified through ongoing error analysis (e.g., citation accuracy, complex formatting rules). Consider advanced techniques like DAG (Deep Acyclic Graph) metrics for complex, multi-step evaluations that require highly deterministic outcomes.
Implement hybrid evaluation and Human-in-the-Loop feedback: Develop a balanced approach that combines the scalability of automated metrics with ongoing human judgment, especially for subjective qualities such as creativity, tone, or emotional nuance. Integrate mechanisms for collecting explicit user feedback (e.g., satisfaction ratings, direct feedback buttons) directly from the deployed system to continuously inform improvements.
Establish robust red teaming and bias audits: Proactively integrate red teaming into your AI development lifecycle to systematically identify and mitigate security vulnerabilities, biases, and ethical risks before they impact users. Conduct regular bias audits using diverse test sets and annotators to ensure fairness across demographic groups, reflecting the increasing societal importance of AI safety and trustworthiness.
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