AI Engineering Fundamentals
.MP4, AVC, 1920x1080, 30 fps | English, AAC, 2 Ch | 11h 35m | 3.59 GB
Instructor: Scott Moss
Key Takeaways
By participating along with us in the workshop, you'll learn:
- []Understand how AI Engineering differs from ML engineering, and why evals are your new test suite
[]Build a stateful chat agent on Cloudflare Workers using the Agents SDK
[]Write golden datasets, build automated scorers, and run eval suites that catch regressions
[]Master contenxt engineering, curating exactly the right tokens for your model at inference time
[]Implement sandboxed code execution, tool search for large tool libraries, and few-shot tool examples
[]Add RAG with Cloudflare Vectorize so your agent draws on real domain knowledge
[]Build a React chat interface that handles streaming tokens, tool call status, generative UI, and human-in-the-loop flows
[]Go beyond the basic agent loop with research-backed patterns like Observe-Predict-Update, multi-agent handoffs, and voting
[]Capture user corrections as eval data and build feedback loops that make your agent better with every interaction
In this two-day workshop, you'll learn AI Engineering as a discipline. Beyond just calling an LLM API, but how to build, evaluate, and continuously improve AI-powered systems. You start with a pre-built diagraming canvas and throughout the course turning it into an intelligent design tool with an agentic chat interface. We'll build the agent and establishing rigorous evaluation. Next we'll systematically improve the eval scores through context engineering, advanced tool use, RAG, better architectures, and production feedback loops. Every improvement is measured. By the end, you'll understand why 70% of AI Engineer job postings in 2026 center on RAG, evals, agents, and production deployment - and you'll have practiced all of them.
Any Prerequisites?
- []Comfortable with TypeScript and React
[]Basic understanding of LLM APIs
[]Familiarity with Node.js
[]A Cloudflare account (free tier works)
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