AI Evaluations Masterclass: How Product Managers and Tech Leaders at Top Companies Build Reliable AI Systems
Are you shipping AI features without knowing if they actually work? In this comprehensive episode of The AI and Tech Society, AI and tech leader Danar Mustafa delivers the definitive guide to AI evaluations—the systematic approach that separates production-ready AI from expensive failures.
What You'll Learn:
🔹 AI Evaluation Fundamentals – Understand what AI evals are, why LLM evaluation differs from traditional ML, and the five dimensions every team must measure: performance, robustness, fairness, factuality, and consistency.
🔹 The 9-Step Evaluation Process – A field-tested framework covering everything from defining success metrics to continuous monitoring, used by engineering teams at leading tech companies like Anthropic, OpenAI, Google, Meta, and Microsoft.
🔹 Complete Tools Comparison – Deep dive into the best AI evaluation frameworks:
- Promptfoo for prompt engineering and model comparison
- RAGAS for RAG pipeline evaluation
- DeepEval for pytest-style LLM testing
- LangSmith and LangFuse for tracing and observability
- TruLens for inline feedback
- Arize Phoenix for LLM debugging
- MLflow Evaluate for experiment tracking
- Deepchecks and EvidentlyAI for drift detection
- Robustness Gym for adversarial testing
🔹 CI/CD Integration – Copy-paste implementation plan for automating AI quality gates in your development pipeline, including specific thresholds for hallucination detection, accuracy regression, and safety violations.
🔹 Real-World Patterns – Battle-tested evaluation setups for customer support AI, HR chatbots, RAG assistants, and content moderation systems deployed at scale.
🔹 PM vs. Engineering Roles – Clear guidance on how product managers should lead evaluation strategy while engineers operationalize the technical infrastructure.
Perfect For:
- Product Managers building AI-powered features
- Machine Learning Engineers deploying LLMs to production
- Engineering Leaders establishing AI quality standards
- Tech Leaders at startups and enterprises adopting generative AI
- Anyone working with ChatGPT, Claude, Gemini, Llama, or other foundation models
Tools & Technologies Discussed: Promptfoo, RAGAS, DeepEval, LangSmith, LangFuse, TruLens, Arize Phoenix, MLflow, Deepchecks, EvidentlyAI, Robustness Gym, OpenAI Evals, LangChain, pytest, CI/CD pipelines, GitHub Actions
Keywords: AI evaluations, AI evals, LLM evaluation, machine learning testing, AI quality assurance, prompt engineering, RAG evaluation, hallucination detection, AI safety testing, MLOps, LLMOps, AI product management, generative AI deployment, foundation models, ChatGPT evaluation, Claude evaluation, AI metrics, model monitoring, AI observability
Whether you're at a Fortune 500 enterprise, a high-growth startup, or a tech giant like Amazon, Google, Microsoft, Meta, or Apple, this episode provides the blueprint for shipping AI that users trust.
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