77 minutesAI prompt engineering: A deep dive
Hear how production prompt engineers revise instructions, examples and behavioral constraints under real pressure instead of treating prompts as one-off text.
OpenAI. Walks through `strict: true`, the difference from old JSON mode, refusal handling, and how function calling and response-format schemas compose. Useful precisely because it describes the contract the API gives you, which is what the article's production patterns are built on top of.
Just under our view bar but canonical — the team that shipped strict mode describing the API contract the article's production patterns are built on.
You can explain what strict mode guarantees, how it differs from old JSON mode, and how refusal handling fits the structured-output contract.
Working knowledge of function calling and JSON Schema basics.
Last reviewed: May 18, 2026
Continue through the same learning path with the next curated companion videos.
77 minutesHear how production prompt engineers revise instructions, examples and behavioral constraints under real pressure instead of treating prompts as one-off text.
55 minutesDesign an eval ladder that catches regressions before prompt or model changes reach users.
9 minutesYou can navigate traces, projects and datasets in LangSmith and read off token cost, latency, error rate and per-span detail.
Hand-picked external courses that go deeper on this topic.
Emory University Goizueta Business School faculty
The deeper, university-level counterpart to our beginner HubSpot marketing pick — Emory's business-school treatment goes past 'how to prompt' into training generative models for brand-specific output, the purchase-funnel economics of AI-generated content, and a full module of genuine skepticism about when generative AI is and isn't worth using in marketing.
IBM AI Academy
The genAI-era answer to the executive-strategy question. Three short courses aimed squarely at business leaders — no technical background required — on where generative AI creates value, how to govern it responsibly, and how to turn a vague "we should use AI" into a concrete, defensible use case. Rated 4.6 across ~700 reviews. Best taken before your next AI budget decision.