Generative AI Bootcamp for Developers

Module 1: Generative AI Foundations for Developers

• LLMs, transformers, tokens, embeddings.
• Prompting vs traditional programming.
• Context windows, hallucinations, grounding.
• When to use AI vs deterministic logic.

Module 2: Prompt Engineering for Developers

• Prompt patterns: role-based prompting, task decomposition, structured outputs.
• Debugging prompts.
• Iterative refinement.

Module 3: AI-Assisted Coding (Copilot + LLMs)

• AI assisted code: inline completions, chat workflows.
• Code generation and refactoring.
• Writing clean, maintainable AI-assisted code.

Module 4: AI in the Software Development Lifecycle

• Requirements → design → code → tests → docs.
• AI for: backlog generation, acceptance criteria, documentation.
• Traceability & audibility.

Module 5: Building AI-Powered Applications

• Calling LLM APIs (OpenAI, Gemini, Azure).
• Application architecture patterns.
• Prompt chaining & workflows.

Module 6: RAG (Retrieval-Augmented Generation)

• Embeddings & vector databases.
• Document ingestion.
• Grounding AI with enterprise data.

Module 7: Testing, QA & Validation with AI

• AI-generated unit tests.
• Integration testing.
• Edge case generation.
• Mutation testing.

Module 8: DevOps, CI/CD & Automation

• AI in pipelines: linting, security scanning (SAST/DAST), code review.
• ChatOps & automation.

Module 9: AI Security, Governance & Risk

• Data privacy.
• Prompt injection risks.
• IP protection.
• Secure usage patterns.

Module 10: Advanced AI Patterns (Agents & Workflows)

• Agentic workflows.
• Multi-step reasoning.
• Orchestration frameworks.

Module 11: Adoption, Metrics & Scaling

• Developer productivity metrics.
• AI ROI.
• Scaling across teams.
• Training models.

Capstone Project (Final)
Participants will: Build an AI-enabled developer workflow including:

• Prompt templates.
• Code generation.
• Testing automation.
• CI/CD integration.
• Optional RAG layer.