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Generative AI Bootcamp for Developers

Generative AI Bootcamp for Developers course designed for experienced programmers ready to master generative AI techniques and tools.

This immersive bootcamp equips software engineers, platform engineers, QA, DevOps, and data teams with the knowledge and practical skills to effectively integrate Generative AI into modern software development workflows.

Participants will learn how large language models (LLMs) work, how to apply prompt engineering and AI-assisted development using tools such as GitHub Copilot and enterprise LLM platforms, and how to operationalize AI across the software development lifecycle (SDLC). The course emphasizes hands-on labs, real-world coding scenarios, and enterprise guardrails, enabling developers to accelerate delivery while maintaining security, compliance, and code quality.

By the end of the bootcamp, participants will be able to leverage AI to improve productivity, automate development tasks, enhance testing, and build AI-powered applications using modern architectures such as RAG and agent-based workflows.

GK# 840044 Vendor# Gen AI for Dev
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Who Should Attend?

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.

What You'll Learn
  • 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.

Course Outline

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.
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Labs Outline

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.

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Prerequisites

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).