Ai Dev Craft

Join us at AI Dev Craft where you will learn the tools, workflows and best practices to be your team's AI expert! Send your team and save - buy 10 or more tickets and get 10% off (workshops not included)! Price reflected at checkout.

AI-Assisted Development Foundations is a one-day, hands-on workshop for developers who already use AI coding assistants but keep getting inconsistent results. You've used the tools — nobody ever explained why they behave the way they do. Understanding those mechanics is the difference between frustration and flow. No AI or machine learning background needed; examples use Angular, but the concepts apply to any stack.
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We start with the fundamentals that explain your assistant's behavior — tokens, context windows, why hallucinations happen, what data leaves your editor — then move to how professionals work with agents today: task design, plan-before-code, spec-driven development, and verification that keeps pace with generation. The core of the day is building a complete AI context ecosystem for your own project: architecture documentation, style guides, context files, instructions, and MCP integrations, following open standards like Agent Skills. Bring whatever sits in your editor — Copilot, Claude Code, Cursor, Codex, Kiro, or similar.
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Come ready to build. Leave with real files you can use immediately.

AI systems are notoriously hard to evaluate: They're non-deterministic, quality is often subjective, and a pipeline with multiple steps like retrieval, prompts, and tool calls can fail silently.  After coaching hundreds of engineers into AI, the skill I see missing most often is structured evaluation. Teams that are newer to AI tend to rely on spot-checks and vibes, and don't know there's a problem until a customer complains or something embarrassing ends up on Twitter. This workshop aims to fix that. We'll cover evaluation from absolute beginner to production: picking metrics, building your first eval suite, component-level evals for everything from RAG to tool use, using LLMs as judges effectively, and evaluating multi-agent systems in production. You'll write real evals during the session and leave with systems you can reuse at work and a way to feel confident in what you're building.