Most developers use AI coding tools like a search engine: type a question, hope for a good answer. But the teams getting real results are engineering the context: configuring system prompts, structuring project knowledge, and managing the AI’s working memory like a first-class architectural concern.

This workshop is built around a real coding project. Participants will receive a small application to build – and throughout the session, they’ll apply each technique as they learn it, experiencing firsthand how context engineering transforms their AI’s output quality.

We’ll cover:

– **The context window** – what tokens are, what counts as context, and why your AI gets worse mid-conversation (context rot)
– **Basic prompting techniques** – prompt engineering is no longer as important, but can still improve your results
– **System prompts as project configuration** – set up instruction files (CLAUDE.md, copilot-instructions.md) that make every AI interaction project-aware
– **Grounding techniques** – anchor AI output in real data instead of letting it guess, using file uploads, documentation references, and structured retrieval

Each concept is immediately put into practice on the project – so by the end, participants will have both a working application and a repeatable playbook for getting better results from any AI coding tool.

Targeted to software engineers, bring a laptop with an AI coding assistant installed.