AI assisted development workshop cover image
Workshop

AI-Assisted Development Foundations

About the event

88% of organizations now use AI in at least one business function, yet most remain stuck in the pilot phase — and over half have already experienced negative consequences from AI inaccuracy. Without structured adoption, teams face inconsistent output, hidden security risks, and costly refactoring cycles. The tool is not the problem; the approach is. Understanding the mechanics behind AI code generation is the difference between frustration and flow!

In this workshop, you will uncover the principles that power every AI coding assistant. You will learn why context gets lost mid-conversation, why hallucinations happen, and how to structure your interactions to get reliable and secure results. These fundamentals apply regardless of which tool sits in your editor.

Beyond single prompts, you will explore the different ways to interact with AI: from chat completions and inline suggestions to agent workflows and spec-driven development. Understanding these modes — and when to reach for each — is the difference between fighting your tools and flowing with them.

You will build a complete AI context ecosystem for your project: architecture documentation, style guides, context files, instruction configurations, and MCP integrations that connect your AI to external tools and data sources. Following open standards like Agent Skills, this infrastructure stays portable across tools - it doesn't just help you today, it empowers those who come after. The foundations you create improve any assistant's understanding of your codebase - and position you to adapt as tools evolve.

This is a hands-on workshop. You will work through exercises in your own project, building the infrastructure that makes AI-assisted development reliable. No theoretical lectures - you leave with real files you can use immediately.

We break each concept into manageable pieces with exercises that reinforce what you learn.

Prerequisites

  • Basic development experience in Angular (examples use Angular, but AI concepts apply to any stack)

  • Familiarity with your preferred code editor

  • Access to any AI coding assistant (Copilot, Claude Code, Cursor, Windsurf, Kiro, or similar)

  • No prior AI or machine learning knowledge required

Takeaways

  1. AI Foundations

    • Tokens & Context Windows - Understand how AI processes and recalls your code

    • Hallucination Prevention - Identify causes and apply prevention strategies

    • Security Awareness - Know what data leaves your editor and how to avoid leaking secrets

    • Prompt Design - Design effective prompts that produce consistent, reliable results across tools

  2. Workflows & Context

    • AI Interaction Modes - Understand the difference between chat, agent, and spec-driven workflows — and when to use each

    • Documentation Generation - Generate project documentation automatically from your codebase

    • Style Guides - Capture your team's coding patterns in AI-readable style guides

    • Context Files - Build context files that give AI deep awareness of your project structure and standards

  3. Tooling & Quality

    • Instructions & Skills - Configure instruction files and portable skills that shape AI behavior to match your workflow

    • MCP Integration - Connect external tools and data sources to your AI assistant using MCP (Model Context Protocol)

    • Quality Gates - Ensure AI-generated code meets the same bar as human-written code before it lands in your codebase

Agenda

Day 1

AI/LLM Fundamentals ~1.5 hours

  • How tokens work and why they matter for every interaction

  • Context windows: what AI remembers, what it forgets, and why

  • The mechanics behind hallucinations and how to prevent them

  • Security basics: what data leaves your editor and what to watch out for

  • Mental models for working effectively with any AI tool

Prompt Engineering & AI Workflows ~1.5 hours

  • Structuring prompts for consistent, reliable output

  • Techniques to keep AI on track: edge anchoring, task splitting, critique runs

  • AI interaction modes: chat completions, agent workflows, and spec-driven development

  • Applying AI to real tasks: refactoring, debugging, code review, and research

  • Hands-on: transform vague requests into structured, constraint-based prompts

Context Ecosystem Setup ~1.5 hours

  • Generating architecture documentation from your codebase

  • Extracting style guides from existing code patterns

  • Building the 5-type context file system for project-wide AI awareness

  • Configuring general and focused instructions that shape AI behavior

  • Optimizing instruction files for token budgets and pattern specificity

  • Hands-on: build context files for your own project

Workflows & Validation ~1.5 hours

  • Configuring MCP to connect external tools and data sources

  • One-shot implementation: validating your context ecosystem end-to-end

  • Leveraging AI for test generation and validation

  • Quality checks for AI-generated code

Preview of the workshop

Slide 1 of 6
AI-Assisted Development Foundations Slide 1

Your trainers for this workshop