Matthew Berman. Walks through the LMSYS RouteLLM paper and code: a small classifier sits in front of a strong/weak model pair and decides which one to call, hitting roughly 95% of the strong model's quality at a fraction of the cost. The view count is under the usual 100k bar, but for the specific "show me real model routing, not just model comparisons" niche this is the cleanest explanation on YouTube and lines up directly with the article's quality/cost tradeoff section.
Model names, pricing and capabilities change quickly. Use this for the decision pattern, then verify current model behavior before adopting it.
Evaluate model-routing tradeoffs between quality, cost and reliability before adding orchestration complexity.
Comfort reading Python and calling model APIs; the primary pick's tier map helps.
Last reviewed: May 18, 2026
Continue through the same learning path with the next curated companion videos.
107 minutesLearn the product-builder eval loop: inspect traces, label failures, define criteria, test changes and compare against human judgment.
211 minutesUnderstand the modern LLM stack well enough to reason about tokens, training, tools and failures.
18 minutesLearn why schema-first LLM calls need typed objects, validators, retries and explicit handling for malformed or hallucinated fields.
Hand-picked external courses that go deeper on this topic.
Anthropic AcademyAnthropic Academy
MCP is the protocol that's quietly replacing one-off tool integrations across the AI tooling ecosystem. Learn it from the source. By the end you'll have built and deployed your own MCP server, connected an LLM client to it, and understood why this standard is the closest thing the field has to USB-C.
DeepLearning.AIJoão Moura (Founder, CrewAI)
Doubles as our sales and customer-support vertical pick and a genuinely practical agent-building course: you build an agentic sales pipeline (lead scoring, personalized outreach) and a customer-support data-insights pipeline as two of the five hands-on projects, taught by CrewAI's own founder. Requires basic Python, so it sits with our other builder-track courses rather than the no-code picks.
Hugging FaceHugging Face
The clearest open-source treatment of agentic systems available. Anchored in the three frameworks engineers actually evaluate (smolagents, LlamaIndex, LangGraph) rather than one vendor's stack. Concludes with a benchmark assignment and public leaderboard — accountability your team can verify.
