AI Performance Evaluation Standards

𝔼𝕍𝔸𝕃 field note (2 of 3): Finding the benchmarks that matter for your own use cases is one of the biggest contributors to AI success. Let's dive in. AI adoption hinges on two foundational pillars: quality and trust. Like the dual nature of a superhero, quality and trust play distinct but interconnected roles in ensuring the success of AI systems. This duality underscores the importance of rigorous evaluation. Benchmarks, whether automated or human-centric, are the tools that allow us to measure and enhance quality while systematically building trust. By identifying the benchmarks that matter for your specific use case, you can ensure your AI system not only performs at its peak but also inspires confidence in its users. 🦸♂️ Quality is the superpower—think Superman—able to deliver remarkable feats like reasoning and understanding across modalities to deliver innovative capabilities. Evaluating quality involves tools like controllability frameworks to ensure predictable behavior, performance metrics to set clear expectations, and methods like automated benchmarks and human evaluations to measure capabilities. Techniques such as red-teaming further stress-test the system to identify blind spots. 👓 But trust is the alter ego—Clark Kent—the steady, dependable force that puts the superpower into the right place at the right time, and ensures these powers are used wisely and responsibly. Building trust requires measures that ensure systems are helpful (meeting user needs), harmless (avoiding unintended harm), and fair (mitigating bias). Transparency through explainability and robust verification processes further solidifies user confidence by revealing where a system excels—and where it isn’t ready yet. For AI systems, one cannot thrive without the other. A system with exceptional quality but no trust risks indifference or rejection - a collective "shrug" from your users. Conversely, all the trust in the world without quality reduces the potential to deliver real value. To ensure success, prioritize benchmarks that align with your use case, continuously measure both quality and trust, and adapt your evaluation as your system evolves. You can get started today: map use case requirements to benchmark types, identify critical metrics (accuracy, latency, bias), set minimum performance thresholds (aka: exit criteria), and choose complementary benchmarks (for better coverage of failure modes, and to avoid over-fitting to a single number). By doing so, you can build AI systems that not only perform but also earn the trust of their users—unlocking long-term value.

𝐀𝐈 𝐰𝐢𝐭𝐡𝐨𝐮𝐭 𝐩𝐫𝐨𝐦𝐩𝐭 𝐦𝐞𝐭𝐫𝐢𝐜𝐬 𝐢𝐬 𝐥𝐢𝐤𝐞 𝐬𝐚𝐥𝐞𝐬 𝐰𝐢𝐭𝐡𝐨𝐮𝐭 𝐜𝐨𝐧𝐯𝐞𝐫𝐬𝐢𝐨𝐧 𝐫𝐚𝐭𝐞𝐬. 𝘛𝘩𝘦 𝘍𝘶𝘵𝘶𝘳𝘦 𝘰𝘧 𝘈𝘐 𝘈𝘨𝘦𝘯𝘵𝘴: 𝘔𝘦𝘢𝘴𝘶𝘳𝘪𝘯𝘨 𝘗𝘳𝘰𝘮𝘱𝘵 𝘚𝘶𝘤𝘤𝘦𝘴𝘴 𝘸𝘪𝘵𝘩 𝘗𝘳𝘦𝘤𝘪𝘴𝘪𝘰𝘯 Most AI agents fail not from bad models but from weak prompts. Advanced 𝐏𝐫𝐨𝐦𝐩𝐭 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 isn’t just about crafting inputs. It’s about 𝐦𝐞𝐚𝐬𝐮𝐫𝐢𝐧𝐠 impact. How do we assess prompt success? 𝐁𝐞𝐲𝐨𝐧𝐝 𝐠𝐮𝐭 𝐟𝐞𝐞𝐥𝐢𝐧𝐠. 𝐁𝐞𝐲𝐨𝐧𝐝 𝐠𝐮𝐞𝐬𝐬𝐰𝐨𝐫𝐤. 𝐇𝐨𝐰 𝐭𝐨 𝐂𝐫𝐞𝐚𝐭𝐞 𝐏𝐫𝐨𝐦𝐩𝐭 𝐀𝐬𝐬𝐞𝐬𝐬𝐦𝐞𝐧𝐭 𝐌𝐞𝐭𝐫𝐢𝐜𝐬: 1) 𝐑𝐞𝐥𝐞𝐯𝐚𝐧𝐜𝐞 𝐒𝐜𝐨𝐫𝐞: Are outputs aligned with intent? 2) 𝐏𝐫𝐞𝐜𝐢𝐬𝐢𝐨𝐧 & 𝐑𝐞𝐜𝐚𝐥𝐥:  Does the AI retrieve the right information? 3) 𝐑𝐞𝐬𝐩𝐨𝐧𝐬𝐞 𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲:  Are outputs concise and useful? 4) 𝐔𝐬𝐞𝐫 𝐒𝐚𝐭𝐢𝐬𝐟𝐚𝐜𝐭𝐢𝐨𝐧: Do users trust and use the response? 5) 𝐂𝐨𝐧𝐯𝐞𝐫𝐬𝐢𝐨𝐧 𝐈𝐦𝐩𝐚𝐜𝐭: Does it drive action in sales or engagement? 6) 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐀𝐜𝐜𝐮𝐫𝐚𝐜𝐲: Does it improve efficiency in manufacturing workflows? 7) 𝐓𝐡𝐫𝐞𝐚𝐭 𝐃𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧 𝐑𝐚𝐭𝐞: Does it enhance security without false alarms? 8) 𝐀𝐮𝐭𝐨𝐧𝐨𝐦𝐲 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞: Does the AI make reliable and context-aware decisions? 𝑪𝒂𝒔𝒆 𝑺𝒕𝒖𝒅𝒊𝒆𝒔: ↳ 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐒𝐮𝐩𝐩𝐨𝐫𝐭: AI reduced resolution time by 40% through clearer prompts. ↳ 𝐋𝐞𝐠𝐚𝐥 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡: AI cut irrelevant results by 60% by optimizing specificity. ↳ 𝐒𝐚𝐥𝐞𝐬 𝐎𝐮𝐭𝐫𝐞𝐚𝐜𝐡:  AI boosted reply rates by 35% with refined personalization. ↳ 𝐄-𝐜𝐨𝐦𝐦𝐞𝐫𝐜𝐞 𝐒𝐞𝐚𝐫𝐜𝐡: AI improved product matches by 50% with structured prompts. ↳ 𝐌𝐞𝐝𝐢𝐜𝐚𝐥 𝐀𝐈: AI reduced diagnostic errors by 30% by improving context clarity. ↳ 𝐌𝐚𝐧𝐮𝐟𝐚𝐜𝐭𝐮𝐫𝐢𝐧𝐠 𝐀𝐈: AI improved defect detection by 45% by enhancing prompt precision. ↳ 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐀𝐈: AI reduced false alerts by 50% in fraud detection systems. ↳ 𝐀𝐮𝐭𝐨𝐧𝐨𝐦𝐨𝐮𝐬 𝐀𝐈: AI enhanced robotics decision-making by 55%, reducing human intervention. 𝐌𝐞𝐭𝐫𝐢𝐜𝐬 𝐦𝐚𝐭𝐭𝐞𝐫. Precision beats intuition. AI Agents thrive when we measure what works. What’s your framework for 𝐏𝐫𝐨𝐦𝐩𝐭 𝐀𝐬𝐬𝐞𝐬𝐬𝐦𝐞𝐧𝐭 𝐟𝐨𝐫 𝐲𝐨𝐮𝐫 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬? ♻️ Repost to your LinkedIn followers if AI should be more accessible and follow Timothy Goebel for expert insights on AI & innovation. #AIagents #PromptEngineering #AIMetrics #ArtificialIntelligence #TechInnovation

Happy Friday! This week in #learnwithmz, let’s talk about 𝐀𝐈 𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧𝐬 and why PMs need to lean in. As AI features become core to product roadmaps, evaluating AI systems is no longer just a research problem. It's a product responsibility. Whether you're building copilots, agents, search, or agentic systems, you need to know how to measure what “good” looks like. 𝐓𝐨𝐨𝐥𝐬 & 𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞𝐬 𝐟𝐨𝐫 𝐀𝐈 𝐄𝐯𝐚𝐥𝐬 Ragas: End-to-end evals for RAG pipelines 🔗 https://lnkd.in/g-upbP3p Gaia Eval Harness (Anthropic): Tests groundedness and reasoning in Claude-like models 🔗 https://lnkd.in/ggcasAdQ OpenAI Evals: Structured prompt test harness for model behaviors 🔗 https://lnkd.in/gXNcwvSU Arize AI Phoenix: Evaluation + observability for LLMs in production 🔗 https://lnkd.in/gAb9aguA Giskard: Automated testing for ML model quality and ethics 🔗 https://lnkd.in/gzQ_heQW Bonus read: Aakash Gupta’s breakdown on AI evals is an excellent read https://lnkd.in/gJkCDxFT I have posted before on key evaluation metrics: https://lnkd.in/gx5CBNsG 𝐊𝐞𝐲 𝐀𝐫𝐞𝐚𝐬 𝐭𝐨 𝐖𝐚𝐭𝐜𝐡 (𝐚𝐬 𝐚 𝐏𝐌) Guardrails aren’t optional, they’re product requirements - Groundedness: Is the model hallucinating or based in fact? - Helpfulness: Does it solve the actual user need? - Bias & Harm: How inclusive, fair, and safe are the outputs? - Consistency: Is the model deterministic where it needs to be? - Evaluation Triggers: Can we detect failure modes early? 𝐄𝐱𝐚𝐦𝐩𝐥𝐞 Evaluating an NL2SQL Copilot Goal: User types a question like “Show me the top 5 customers by revenue last quarter” The system should generate correct, optimized SQL against a given schema. 𝐊𝐞𝐲 𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧 𝐃𝐢𝐦𝐞𝐧𝐬𝐢𝐨𝐧𝐬 - Correctness (Semantic Accuracy) Does the SQL produce the expected result? Is it aligned with schema constraints (e.g., table and column names)? Automate this with unit tests or snapshot comparisons - Executability Does the generated SQL run without error? You can use test DBs or mock query runners - Faithfulness (Groundedness) Does the SQL only use tables and columns present in the schema? Hallucinated column/table = major fail - Performance/Affordability Is the SQL optimized for cost and latency (no SELECT *)? Use static query analysis or query plan inspection - Helpfulness (UX/Intent Match) Does the SQL actually answer the user's intent? This can require human-in-the-loop eval 𝐖𝐡𝐲 𝐭𝐡𝐢𝐬 𝐦𝐚𝐭𝐭𝐞𝐫𝐬 You can’t ship AI responsibly without evals and you can’t evaluate well without cross-functional design. PMs, DS, and Eng need shared language, goals, and metrics. Which eval tools are in your stack or on your radar? Let’s crowdsource some best practices #AI #ProductManagement #LLM #AIEvals #ResponsibleAI #RAG #AIObservability #LearnWithMZ

Honestly, most AI developers are still stuck in the last century. It blows my mind how few people are aware of Error Analysis. This is *literally* the fastest and most effective way to evaluate AI applications, and most teams are still stuck chasing ghosts. Please, stop tracking generic metrics and follow these steps: 1. Collect failure samples Start reviewing the responses generated by your application. Write notes about each response, especially those that were mistakes. You don't need to format your notes in any specific way. Focus on describing what went wrong with the response. 2. Categorize your notes After you have reviewed a good set of responses, take an LLM and ask it to find common patterns in your notes. Ask it to classify each note based on these patterns. You'll end up with categories covering every type of mistake your application made. 3. Diagnose the most frequent mistakes Begin by focusing on the most common type of mistake. You don't want to waste time working with rare mistakes. Drill into the conversations, inputs, and logs leading to those incorrect samples. Try to understand what might be causing the problems. 4. Design targeted fixes At this point, you want to determine how to eliminate the mistakes you diagnosed in the previous step as quickly and cheaply as possible. For example, you could tweak your prompts, add extra validation rules, find more training data, or modify the model. 5. Automate the evaluation process You need to implement a simple process to rerun an evaluation set through your application and evaluate whether your fixes were effective. My recommendation is to use an LLM-as-a-Judge to run samples through the application, score them with a PASS/FAIL tag, and compute the results. 6. Keep an eye on your metrics Each category you identified during error analysis is a metric you want to track over time. You will get nowhere by obsessing over "relevance", "correctness", "completeness", "coherence", and any other out-of-the-box metrics. Forget about these and focus on the real issues you found.

In the rapidly evolving world of conversational AI, Large Language Model (LLM) based chatbots have become indispensable across industries, powering everything from customer support to virtual assistants. However, evaluating their effectiveness is no simple task, as human language is inherently complex, ambiguous, and context-dependent. In a recent blog post, Microsoft's Data Science team outlined key performance metrics designed to assess chatbot performance comprehensively. Chatbot evaluation can be broadly categorized into two key areas: search performance and LLM-specific metrics. On the search front, one critical factor is retrieval stability, which ensures that slight variations in user input do not drastically change the chatbot's search results. Another vital aspect is search relevance, which can be measured through multiple approaches, such as comparing chatbot responses against a ground truth dataset or conducting A/B tests to evaluate how well the retrieved information aligns with user intent. Beyond search performance, chatbot evaluation must also account for LLM-specific metrics, which focus on how well the model generates responses. These include: - Task Completion: Measures the chatbot's ability to accurately interpret and fulfill user requests. A high-performing chatbot should successfully execute tasks, such as setting reminders or providing step-by-step instructions. - Intelligence: Assesses coherence, contextual awareness, and the depth of responses. A chatbot should go beyond surface-level answers and demonstrate reasoning and adaptability. - Relevance: Evaluate whether the chatbot’s responses are appropriate, clear, and aligned with user expectations in terms of tone, clarity, and courtesy. - Hallucination: Ensures that the chatbot’s responses are factually accurate and grounded in reliable data, minimizing misinformation and misleading statements. Effectively evaluating LLM-based chatbots requires a holistic, multi-dimensional approach that integrates search performance and LLM-generated response quality. By considering these diverse metrics, developers can refine chatbot behavior, enhance user interactions, and build AI-driven conversational systems that are not only intelligent but also reliable and trustworthy. #DataScience #MachineLearning #LLM #Evaluation #Metrics #SnacksWeeklyonDataScience – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gAC8eXmy

Stanford and Microsoft researchers just launched a new standard for testing medical AI across real-world clinical tasks, not just test questions. While most medical AI models boast near-perfect scores on licensing exams, these benchmarks miss the day-to-day reality of clinical work. 𝗠𝗲𝗱𝗛𝗘𝗟𝗠 𝗶𝘀 𝗮 𝗳𝗶𝗿𝘀𝘁 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 𝗳𝗼𝗿 𝗟𝗟𝗠𝘀 𝘁𝗵𝗮𝘁 𝘀𝗽𝗮𝗻𝘀 𝟭𝟮𝟭 𝗿𝗲𝗮𝗹 𝗰𝗹𝗶𝗻𝗶𝗰𝗮𝗹 𝘁𝗮𝘀𝗸𝘀, grounded in clinician input and real data.  1. Defined 121 medical tasks across 22 subcategories in a taxonomy co-developed with 29 clinicians, with 96.7% agreement on its organization.  2. Built 35 benchmarks from public, gated, and private data sources, including 13 new ones using real EHRs, to evaluate all subcategories.  3. Benchmarked 9 leading LLMs (like GPT-4o, Claude 3.5, DeepSeek R1) across these tasks using both standard metrics and an “LLM-jury” that matched or outperformed clinicians in scoring consistency.  4. Found that most models performed best in Clinical Note Generation (scores: 0.74–0.85) and Patient Communication (0.76–0.89), but struggled in Administration & Workflow (0.53–0.63).  5. Identified cost-effective models: Claude 3.5 Sonnet achieved near-top performance (63% win-rate) at 40% lower cost than top-performing models like DeepSeek R1 (66% win-rate). This is one of the most comprehensive task coverage I have seen for an medical LLM: all 22 subcategories across 121 tasks with 35 distinct benchmarks (17 existing, 5 reformulated, 13 new) I thought curating additional benchmarks especially in under-represented subcategories could be beneficial as well. Interestingly Administration & Workflow remains the weakest category (0.53–0.63 mean scores). Perhaps developing specialized prompts or integrating structured data tools such as scheduling APIs could help. Here's the awesome work: https://lnkd.in/gmFSaw-v Congrats to Suhana Bedi, Hejie Cui, Miguel Ángel Fuentes Hernández, Alyssa Unell, Nigam Shah, and co! I post my takes on the latest developments in health AI – 𝗰𝗼𝗻𝗻𝗲𝗰𝘁 𝘄𝗶𝘁𝗵 𝗺𝗲 𝘁𝗼 𝘀𝘁𝗮𝘆 𝘂𝗽𝗱𝗮𝘁𝗲𝗱! Also, check out my health AI blog here: https://lnkd.in/g3nrQFxW

Recently, I've been building a GenAI product and wanted to understand how to properly evaluate AI agents. This led me to take the "Evaluating AI Agents" course from deeplearning.ai. For anyone in my position, here are the key concepts to know: AI Agent Architecture • Router: The "brain" deciding which function to call • Skills: Logic blocks that perform specific tasks • Memory/State: Storage for context and execution logs Evaluation Techniques • Code-Based Evaluations: Traditional testing comparing outputs to expected results • LLM as a Judge: Using another AI to evaluate outputs based on criteria • Human Annotations: The gold standard, but resource-intensive What to Evaluate • Router: Did it select the correct function with the right parameters? • Skills: Are individual components performing correctly? • Path Convergence: Does the agent reach a satisfactory conclusion? Evaluation-Driven Development This powerful approach transformed my building process: • Curate test cases representing typical inputs • Run experiments with different agent variations • Apply evaluators to compare results • Create a continuous improvement flywheel As I continue building, this evaluation framework has been invaluable for improving reliability. What evaluation methods have you found effective for your AI systems? #ArtificialIntelligence #GenAI #MachineLearning #AIAgents #Evaluation

Generative AI is transforming industries, but as adoption grows, so does the need for trust and reliability. Evaluation frameworks ensure that generative AI models perform as intended—not just in controlled environments, but in the real world. Key Insights from GCP Blog : Scalable Evaluation - new batch evaluation API allows you to assess large datasets efficiently, making it easier to validate model performance at scale. Customizable Autoraters - Benchmark automated raters against human judgments to build confidence in your evaluation process and highlight areas for improvement. Agentic Workflow Assessment - For AI agents, evaluate not just the final output, but also the reasoning process, tool usage, and decision trajectory. Continuous Monitoring - Implement ongoing evaluation to detect performance drift and ensure models remain reliable as data and user needs evolve. - Key Security Considerations: - Data Privacy: Ensure models do not leak sensitive information and comply with data protection regulations - Bias and Fairness: Regularly test for unintended bias and implement mitigation strategies[3]. - Access Controls:Restrict model access and implement audit trails to track usage and changes. - Adversarial Testing:Simulate attacks to identify vulnerabilities and strengthen model robustness **My Perspective: ** I see robust evaluation and security as the twin pillars of trustworthy AI. #Agent Evaluation is Evolving : Modern AI agent evaluation goes beyond simple output checks. It now includes programmatic assertions, embedding-based similarity scoring, and grading the reasoning path—ensuring agents not only answer correctly but also think logically and adapt to edge cases. Automated evaluation frameworks, augmented by human-in-the-loop reviewers, bring both scale and nuance to the process. - Security is a Lifecycle Concern: Leading frameworks like OWASP Top 10 for LLMs, Google’s Secure AI Framework (SAIF), and NIST’s AI Risk Management Framework emphasize security by design—from initial development through deployment and ongoing monitoring. Customizing AI architecture, hardening models against adversarial attacks, and prioritizing input sanitization are now standard best practices. - Continuous Improvement: The best teams integrate evaluation and security into every stage of the AI lifecycle, using continuous monitoring, anomaly detection, and regular threat modeling to stay ahead of risks and maintain high performance. - Benchmarking and Transparency: Standardized benchmarks and clear evaluation criteria not only drive innovation but also foster transparency and reproducibility—key factors for building trust with users and stakeholders. Check GCP blog post here: [How to Evaluate Your Gen AI at Every Stage](https://lnkd.in/gDkfzBs8) How are you ensuring your AI solutions are both reliable and secure?