Aakash GuptaThe Most Important New Skill for Product Managers in 2026: AI Evals Masterclass
CHAPTERS
- 0:00 – 2:14
Why AI features fail without evaluations (and why it’s a PM skill)
Ankit frames the core claim: AI features don’t fail primarily because the model is “bad,” but because teams ship without a reliable way to measure correctness, usefulness, and safety. The episode positions “writing evals” as a defining capability for product managers heading into 2026.
- 2:14 – 3:46
What’s different about this masterclass: real examples, not hypotheticals
Aakash and Ankit contrast most online eval content (introductory, theoretical) with what they’ll do here: a framework plus concrete examples and an end-to-end case study. The promise is that viewers walk away able to approach evals for many GenAI product types.
- 3:46 – 5:54
The 5 components of a GenAI product (and where nondeterminism enters)
Ankit breaks a GenAI product into five building blocks and explains why they require a different quality approach than deterministic software. The key issue is stochastic model behavior: the same input can yield different outputs, so teams must “tame the lion” with evals.
- 5:54 – 10:30
Case study: AI-first job website & what an eval looks like
A simple AI job site example illustrates how evals operate: generate summaries, interview questions, skills, learning guides, and quizzes from job descriptions, then assess quality and constraints. The chapter also clarifies that evals can be code-based, human, or LLM-judge prompts.
- 10:30 – 11:31
Why prototypes fail to scale: the 5 failure modes
Ankit explains why impressive demos break in production, citing research and practical patterns. He outlines five common reasons prototypes fail, many of which require systematic measurement and iteration to overcome.
- 11:31 – 12:06
Nondeterminism intuition: the ‘chai’ metaphor & why correctness isn’t enough
Using tea/chai variation across contexts, Ankit explains why even “correct” LLM answers may still fail user expectations. The takeaway: even as hallucinations decrease, products must be tuned to customer preferences and context, which evals operationalize.
- 12:06 – 13:43
Building evals end-to-end: the full workflow diagram
Ankit walks through an end-to-end eval lifecycle: define success criteria, build a baseline product, create a representative dataset, identify failures with SME help, convert them into metrics and evals, then iterate via offline and online loops.
- 13:43 – 22:04
How evals address drift, cost, and guardrails (and where they don’t)
Evals are mapped directly to the prototype failure modes. They’re positioned as continuous measurement for drift, as a way to compare models/cost tradeoffs, and as the backbone for guardrails—while acknowledging engineering constraints need additional approaches.
- 22:04 – 35:43
Evaluation methods & metrics: code checks, LLM judges, and legacy NLP metrics
This chapter drills into the “how” of measuring outputs: deterministic programmatic tests, subjective LLM-judge scoring, and when to use (or avoid) older NLP metrics like BLEU/ROUGE. The guiding principle is to use the cheapest reliable method for each metric.
- 35:43 – 37:10
Offline evals: the AI PRD and ‘hill-climbing’ to ship quality
Offline evals are positioned as the pre-launch gating system and effectively the PRD for AI engineers. The team iterates on prompts/models/tools until eval performance meets thresholds, then ships with confidence rather than hope.
- 37:10 – 39:26
Online evals & observability: sampling in production + drift detection
After launch, the same eval concepts extend into production via observability platforms and sampling-based checks. Online evals catch drift, regressions, and changing user expectations, creating a continuous improvement loop.
- 39:26 – 57:52
Case study deep dive: INDMoney Mind / Robinhood-style stock Q&A assistant
Ankit reverse-engineers a finance assistant feature and shows how a PM would define constraints, compliance guardrails, and evaluation dimensions in a regulated domain. The example highlights how expected behavior becomes explicit metrics and test criteria.
- 57:52 – 1:01:24
Real-world evaluation artifacts: datasets, thresholds, latency percentiles, and user feedback loops
The case study expands into concrete eval operations: dataset sourcing/maintenance, eval types (automated, LLM-judge, human), blocking criteria, online latency monitoring, and using hard/soft user feedback as additional signals.
- 1:01:24 – 1:03:59
Evals aren’t QA rebranded: business impact examples & final takeaways
Ankit distinguishes eval work from traditional QA by emphasizing transformation, SME alignment, and continuous system tuning. They close with examples (Grammarly, GitHub Copilot, Klarna, support chatbots) and the overarching lesson that evals are ongoing guardrails, not a one-time task.
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