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Master the rapidly evolving field of AI development. Learn to work with LLMs, build intelligent applications, and understand the latest AI techniques.
From prompt engineering to building production AI systems.
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AI development is one of the only fields where a tutorial can go stale before you finish watching it. A walkthrough of the best prompting technique recorded a few months ago is often already referencing a model that's been replaced twice over. That churn is exactly why treating AI development as programming plus a few new API calls doesn't hold up: the tools rotate constantly, but the underlying mechanics of how a language model turns tokens into predictions, why context windows matter, and what retrieval actually retrieves don't change nearly as fast.
This track is built around that distinction. It opens with LLM fundamentals, how these models actually process and generate text, before it puts you anywhere near a prompt template, because prompting techniques make a lot more sense once you understand what you're steering. From there it moves into prompt engineering as its own discipline, into wiring real API calls from providers like OpenAI and Anthropic into applications, into retrieval-augmented generation for grounding answers in your own data, and finally into the parts most courses skip entirely: fine-tuning tradeoffs and what running AI in production actually requires.
The honest outcome to expect: you won't finish having trained a model from scratch, and that's intentional. You'll finish able to build an application that calls an LLM, retrieves the right context, handles a bad response gracefully, and doesn't fall over when a provider changes its rate limits, which is the actual day-to-day job of an AI developer at most companies right now.
The curriculum is sequenced around durable mechanics, tokens, context, retrieval, evaluation, instead of one vendor's current prompt syntax, so it doesn't need a rewrite every time a model ships.
Later modules deal with running AI in production: cost, latency, and safety, not just getting one clever response inside a playground.
Spaced repetition keeps AI-specific vocabulary, embeddings, temperature, RAG, fine-tuning, coming back until it's actually retained, since this field introduces more new terminology than most.
You build against real API calls to real models throughout, so the applications you finish with are the same shape as what you'd ship at work, not a simulated exercise.
Master AI Development step by step with our structured curriculum.
How large language models work and their capabilities.
Master the art of crafting effective prompts for optimal results.
Integrate OpenAI, Anthropic, and other AI services into applications.
Retrieval-Augmented Generation for knowledge-grounded AI.
Customize AI models for specific use cases.
Deploy, monitor, and scale AI applications safely.
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