75 minutesThe Ultimate MCP Crash Course - Build From Scratch
Build a first MCP server and understand how tools, schemas, prompts, resources and transports fit together.
Hamel Husain. Hamel Husain, Eugene Yan, Brian Bischof, Harrison Chase, and Shreya Shankar working through tracing, log analysis, LLM-as-judge, and the workflow around looking at real production data. Sit with it the same way you would a long podcast — it is the single best deep treatment of the article's "look at your traces" thesis on YouTube.
Two and a half hours with tiny view numbers, kept deliberately: there is no comparable long-form treatment of trace-driven evaluation, so sit with it like a long podcast.
Connect tracing, evaluation, feedback and production review into an operating loop for LLM systems.
Production LLM experience — it assumes you have real traces and logs worth analysing.
Last reviewed: May 18, 2026
Continue through the same learning path with the next curated companion videos.
75 minutesBuild a first MCP server and understand how tools, schemas, prompts, resources and transports fit together.
19 minutesImprove tool design so agents select the right action with the right parameters.
19 minutesIdentify the production RAG controls missing from naive document-chat demos.
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.