The Growth Podcast AI Evals Explained Simply by Ankit Shukla
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Feb 19, 2026 
Ankit Shukla, an AI product management educator who trains thousands of PMs, breaks down AI evals from first principles. He covers why PMs need eval skills, how to evaluate non-deterministic models, and a job-site case study. Short takes on eval types, guardrails, metrics, prototyping pitfalls, and production monitoring keep the conversation practical and actionable.
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Job Site Example To Illustrate Evals
- Ankit uses an AI-first job site example that ingests job descriptions and outputs summaries, skills, interview questions, and quizzes.
- He runs those outputs through an evaluator LLM or code checks to verify length, accuracy, and relevance.
Why Prototypes Rarely Scale
- Prototypes often fail to scale due to data drift, cost, engineering limits, missing guardrails, and collaboration gaps.
- Evals specifically mitigate drift, cost waste, and missing guardrails when applied correctly.
Validate Cheaper Models With Evals
- Use evals to test whether cheaper models can meet product quality before choosing costly models.
- Evaluate cost-performance tradeoffs to avoid unnecessary production costs that kill scaling.