Generative AI Boot Camp — Module Overview
This three-day immersive workshop equips experienced software developers with the knowledge and practical skills to effectively integrate Generative AI into modern software
M01: Generative AI Foundations for Developers
- LLMs, transformers, tokens, embeddings
- Context windows, hallucinations, grounding
- Prompting vs. traditional programming
- When to use AI vs. deterministic logic
- Overview of GANs, VAEs, and diffusion models (brief, conceptual)
- Learning outcomes: #1
M02: Prompt Engineering for Developers
- Prompt patterns: role-based, task decomposition, structured outputs
- Debugging prompts and iterative refinement
- Tool-agnostic patterns applicable across providers
- Learning outcomes: #2
Labs: M02 Lab PDFs: 📄 Lab PDF | 📄 Prompt Engineering Patterns Cheat Sheet
M03: AI-Assisted Coding
- The GenAI developer tool landscape: VS Code inline/chat agents, CLI tools (OpenCode, Claude Code, Codex), web-based assistants
- Inline completions vs. chat workflows — when to use which
- Code generation, refactoring, and explanation
- Writing clean, maintainable AI-assisted code
- Comparing tools: strengths, trade-offs, provider compatibility
- Learning outcomes: #3
Labs: M03 Lab PDFs: 📄 Lab PDF | 📄 AI-Assisted Coding Tool Selection
M04: AI in the Software Development Lifecycle
- AI across the SDLC: requirements → design → code → tests → docs
- AI for backlog generation, acceptance criteria, documentation
- Traceability and auditability in AI-assisted workflows
- Learning outcomes: #4
M05: Building AI-Powered Applications
- Calling LLM APIs across providers (patterns, not provider-specific)
- Application architecture patterns for AI features
- Prompt chaining and simple workflows
- Learning outcomes: #6 (nice-to-have)
M06: Retrieval-Augmented Generation (RAG)
- Embeddings and vector databases (conceptual)
- Document ingestion and chunking strategies
- Grounding AI with enterprise data
- Learning outcomes: #6 (nice-to-have)
M07: Testing, QA & Validation with AI
- AI-generated unit tests, integration tests, edge cases
- Mutation testing concepts
- Validating AI-generated code
- Learning outcomes: #7 (nice-to-have)
M08: DevOps, CI/CD & Automation
- AI in pipelines: linting, security scanning (SAST/DAST), code review
- ChatOps and AI-assisted automation patterns
- Learning outcomes: #7 (nice-to-have)
M09: AI Security, Governance & Risk
- Data privacy considerations for AI tools
- Prompt injection risks and mitigations
- IP protection when using AI-generated code
- Secure usage patterns and organizational policies
- Learning outcomes: #5
Labs: M09 Lab PDFs: 📄 Lab PDF | 📄 Security Governance Quick Reference
M10: Advanced AI Patterns (Agents & Workflows)
- Agentic workflows: concepts and patterns
- Multi-step reasoning and tool use
- Orchestration frameworks at a glance
- When agents are (and are not) the right solution
- Learning outcomes: #3, #6
Labs: M10 Lab PDFs: 📄 Lab PDF | 📄 Agents and Workflows Quick Reference
Course delivered by Agile Brains Consulting (ABC)