Generative AI Bootcamp for Developers

 The ideal audience for this intermediate and beyond level course consists of experienced software developers, programmers, and engineers who are eager to learn and adopt cutting-edge generative AI techniques in their projects. The course is tailored for experienced professionals with a background in programming and a basic understanding of artificial intelligence and machine learning concepts.

Attendee roles might include:

• Software Developers/Programmers: Those wanting to integrate AI into tasks like code generation, documentation, and testing.
• UI/UX Designers: Professionals interested in creating dynamic, adaptive interfaces using AI.
• Technical Product Managers: Managers looking to enhance AI-driven products.
• Technical Team Leads: Leaders seeking innovative ways to incorporate generative AI into team projects.

 

●      Explain how LLMs, transformers, and generative AI systems work, including their limitations and risks

●      Apply prompt engineering and AI-assisted development techniques to accelerate coding, testing, and documentation

●      Use tools such as GitHub Copilot, ChatGPT, and Gemini for real-world development workflows

●      Integrate AI into the SDLC (requirements → design → code → test → deploy)

●      Build and evaluate AI-powered applications using APIs, RAG, and embeddings

●      Implement AI guardrails for security, privacy, and responsible usage

●      Automate testing, CI/CD pipelines, and developer workflows using AI

●      Measure productivity gains and define an AI adoption roadmap for engineering teams

 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 & auditability

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

Hands-on Lab: Identify 5 engineering use cases. Classify: automation vs augmentation vs risk

Lab: Generate:

○      API spec

○      function stubs

○      documentation

Hands on Lab: Build: REST API, service layer, documentation

Hands-on Lab: Convert user story → full development package

Hands-on Lab: Build: AI-powered assistant, simple chatbot API

Lab: Build RAG-based knowledge assistant

Lab: Generate + run test suites. Capture results

Lab: Build: AI-assisted CI pipeline. automated PR review

Lab: Red-team prompts. define guardrails

Lab: Build: agent workflow: input → analysis → output

Lab: Define: team rollout plans, KPIs

 This course is highly technical in nature. In order to gain the most from attending you should possess the following incoming skills:

• Experience with software development languages and platforms (C++, Java, C#, or HTML/Javascript)
• Basic understanding of artificial intelligence and machine learning concepts (supervised and unsupervised learning, neural networks, optimization techniques)