In this presentation, we explore how AI tools have changed the software development process in XM, extending far beyond the capabilities of traditional Integrated Development Environments (IDEs). We’ll delve into the practical applications of AI in various stages of development, from code generation and auto-completion to bug detection and testing. By integrating AI, developers can enhance productivity, improve code quality, and streamline workflows, allowing them to focus on more creative and complex tasks.

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.