How to Evaluate AI Solution Robustness

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?

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.

𝘠𝘰𝘶𝘳 𝘎𝘗𝘛 𝘮𝘪𝘨𝘩𝘵 𝘸𝘰𝘳𝘬 𝘪𝘯 𝘵𝘩𝘦𝘰𝘳𝘺, 𝘣𝘶𝘵 𝘸𝘪𝘭𝘭 𝘪𝘵 𝘴𝘶𝘳𝘷𝘪𝘷𝘦 𝘳𝘦𝘢𝘭𝘪𝘵𝘺? 7 𝘴𝘵𝘦𝘱𝘴 𝘵𝘰 𝘤𝘳𝘦𝘢𝘵𝘦 𝘳𝘰𝘤𝘬-𝘴𝘰𝘭𝘪𝘥 𝘣𝘦𝘯𝘤𝘩𝘮𝘢𝘳𝘬𝘴. 𝐖𝐡𝐞𝐧 𝐝𝐞𝐬𝐢𝐠𝐧𝐢𝐧𝐠 𝐛𝐞𝐧𝐜𝐡𝐦𝐚𝐫𝐤𝐬 𝐟𝐨𝐫 𝐆𝐏𝐓 𝐬𝐲𝐬𝐭𝐞𝐦𝐬, 𝐫𝐞𝐚𝐥-𝐰𝐨𝐫𝐥𝐝 𝐜𝐨𝐦𝐩𝐥𝐞𝐱𝐢𝐭𝐲 𝐦𝐚𝐭𝐭𝐞𝐫𝐬. 𝐀 𝐠𝐨𝐨𝐝 𝐀𝐈 𝐢𝐬𝐧’𝐭 𝐣𝐮𝐬𝐭 𝐬𝐦𝐚𝐫𝐭. 𝐈𝐭’𝐬 𝐫𝐞𝐥𝐢𝐚𝐛𝐥𝐞 𝐮𝐧𝐝𝐞𝐫 𝐩𝐫𝐞𝐬𝐬𝐮𝐫𝐞. Here’s how to make your benchmarks stronger: 1) 𝐂𝐫𝐞𝐚𝐭𝐞 𝐯𝐚𝐫𝐢𝐞𝐝 𝐭𝐞𝐬𝐭 𝐜𝐚𝐬𝐞𝐬 ↳ Mix factual, reasoning, and creative tasks. ↳ Use diverse literacy, domain knowledge, and cultural profiles. ↳ Include short, vague, and adversarial inputs. 2) 𝐁𝐮𝐢𝐥𝐝 𝐚 𝐝𝐞𝐭𝐚𝐢𝐥𝐞𝐝 𝐨𝐮𝐭𝐩𝐮𝐭 𝐫𝐮𝐛𝐫𝐢𝐜 ↳ Check for correctness and logical flow. ↳ Ensure tone matches user needs and context. ↳ Assess for bias, safety, and relevance. 3) 𝐒𝐢𝐦𝐮𝐥𝐚𝐭𝐞 𝐫𝐞𝐚𝐥 𝐬𝐜𝐞𝐧𝐚𝐫𝐢𝐨𝐬 ↳ Handle frustration, ambiguity, and mistakes. ↳ Adapt to jargon, slang, and emotional cues. ↳ Test both beginners and advanced users. 4) 𝐌𝐞𝐚𝐬𝐮𝐫𝐞 𝐜𝐨𝐧𝐯𝐞𝐫𝐬𝐚𝐭𝐢𝐨𝐧 𝐪𝐮𝐚𝐥𝐢𝐭𝐲 ↳ Look for topic continuity and smooth transitions. ↳ Prioritize direct, timely, and empathetic responses. ↳ Test how well past interactions are remembered. 5) 𝐃𝐞𝐬𝐢𝐠𝐧 "𝐖𝐡𝐚𝐭 𝐈𝐟" 𝐜𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬 ↳ What if the user gives incorrect information? ↳ What if the question is vague? ↳ What if cultural sensitivity is required? 6) 𝐂𝐡𝐞𝐜𝐤 𝐚𝐝𝐯𝐞𝐫𝐬𝐚𝐫𝐢𝐚𝐥 𝐫𝐞𝐬𝐢𝐥𝐢𝐞𝐧𝐜𝐞 ↳ Test against tricky or biased questions. ↳ Spot privacy-violating or inappropriate inputs. ↳ Assess ability to reject unethical requests. 7) 𝐀𝐝𝐚𝐩𝐭 𝐟𝐨𝐫 𝐮𝐧𝐢𝐪𝐮𝐞 𝐚𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 ↳ Tailor benchmarks to customer service, learning, or financial tasks. ↳ Ensure robustness across unexpected “what if” cases. ↳ Refine tests to mirror real user behaviors. Benchmarking isn’t about perfection. It’s about preparation. What benchmarks will you test today? This is something I learned from taking a course and in interviews companies are asking. ♻️ Repost to your LinkedIn followers and follow Timothy Goebel for more actionable insights on AI and innovation.

A new study shows that even the best financial LLMs hallucinate 41% of the time when faced with unexpected inputs. FailSafeQA, a new benchmark from Writer, tests LLM robustness in finance by simulating real-world mishaps, including misspelled queries, incomplete questions, irrelevant documents, and OCR-induced errors. Evaluating 24 top models revealed that: * OpenAI’s o3-mini, the most robust, hallucinated in 41% of perturbed cases * Palmyra-Fin-128k-Instruct, the model best at refusing irrelevant queries, still struggled 17% of the time FailSafeQA uniquely measures: (1) Robustness - performance across query perturbations (e.g., misspelled, incomplete) (2) Context Grounding - the ability to avoid hallucinations when context is missing or irrelevant (3) Compliance - balancing robustness and grounding to minimize false responses Developers building financial applications should implement explicit error handling that gracefully addresses context issues, rather than solely relying on model robustness. Developing systems to proactively detect and respond to problematic queries can significantly reduce costly hallucinations and enhance trust in LLM-powered financial apps. Benchmark details https://lnkd.in/gq-mijcD

As we scale GenAI from demos to real-world deployment, one thing becomes clear: 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 𝗱𝗮𝘁𝗮𝘀𝗲𝘁𝘀 𝗰𝗮𝗻 𝗺𝗮𝗸𝗲 𝗼𝗿 𝗯𝗿𝗲𝗮𝗸 𝗮 𝗚𝗲𝗻𝗔𝗜 𝘀𝘆𝘀𝘁𝗲𝗺. A model can be trained on massive amounts of data, but that doesn’t guarantee it understands context, nuance, or intent at inference time. You can teach a student all the textbook theory in the world. But unless you ask the right questions, in the right setting, under realistic pressure, you’ll never know what they truly grasp. This snapshot outlines the 6 dataset types that AI teams use to rigorously evaluate systems at every stage of maturity: The Evaluation Spectrum 1. 𝐐𝐮𝐚𝐥𝐢𝐟𝐢𝐞𝐝 𝐚𝐧𝐬𝐰𝐞𝐫𝐬 Meaning: Expert-reviewed responses Use: Measure answer quality (groundedness, coherence, etc.) Goal: High-quality, human-like responses 2. 𝐒𝐲𝐧𝐭𝐡𝐞𝐭𝐢𝐜 Meaning: AI-generated questions and answers Use: Test scale and performance Goal: Maximize response accuracy, retrieval quality, and tool use precision 3. 𝐀𝐝𝐯𝐞𝐫𝐬𝐚𝐫𝐢𝐚𝐥 Meaning: Malicious or risky prompts (e.g., jailbreaks) Use: Ensure safety and resilience Goal: Avoid unsafe outputs 4. 𝐎𝐎𝐃 (𝐎𝐮𝐭 𝐨𝐟 𝐃𝐨𝐦𝐚𝐢𝐧) Meaning: Unusual or irrelevant topics Use: See how well the model handles unfamiliar territory Goal: Avoid giving irrelevant or misleading answers 5. 𝐓𝐡𝐮𝐦𝐛𝐬 𝐝𝐨𝐰𝐧 Meaning: Real examples where users rated answers poorly Use: Identify failure modes Goal: Internal review, error analysis 6. 𝐏𝐑𝐎𝐃 Meaning: Cleaned, real user queries from deployed systems Use: Evaluate live performance Goal: Ensure production response quality This layered approach is essential for building: • Trustworthy AI • Measurable safety • Meaningful user experience Most organizations still rely on "accuracy-only" testing. But GenAI in production demands multi-dimensional evaluation — spanning risk, relevance, and realism. If you’re deploying GenAI at scale, ask: Are you testing the right things with the right datasets? Let’s sharpen the tools we use to measure intelligence. Because better testing = better AI. 👇 Would love to hear how you’re designing your eval pipelines. #genai #evaluation #llmops #promptengineering #aiarchitecture #openai

🚨 Public Service Announcement: If you're building LLM-based applications for internal business use, especially for high-risk functions this is for you. Define Context Clearly ------------------------ 📋 Document the purpose, expected behavior, and users of the LLM system. 🚩 Note any undesirable or unacceptable behaviors upfront. Conduct a Risk Assessment ---------------------------- 🔍 Identify potential risks tied to the LLM (e.g., misinformation, bias, toxic outputs, etc), and be as specific as possible 📊 Categorize risks by impact on stakeholders or organizational goals. Implement a Test Suite ------------------------ 🧪 Ensure evaluations include relevant test cases for the expected use. ⚖️ Use benchmarks but complement them with tests tailored to your business needs. Monitor Risk Coverage ----------------------- 📈 Verify that test inputs reflect real-world usage and potential high-risk scenarios. 🚧 Address gaps in test coverage promptly. Test for Robustness --------------------- 🛡 Evaluate performance on varied inputs, ensuring consistent and accurate outputs. 🗣 Incorporate feedback from real users and subject matter experts. Document Everything ---------------------- 📑 Track risk assessments, test methods, thresholds, and results. ✅ Justify metrics and thresholds to enable accountability and traceability. #psa #llm #testingandevaluation #responsibleAI #AIGovernance Patrick Sullivan, Khoa Lam, Bryan Ilg, Jeffery Recker, Borhane Blili-Hamelin, PhD, Dr. Benjamin Lange, Dinah Rabe, Ali Hasan