Upcoming Sessions

Session with asterisk (*) are guaranteed to run

Aug 3 - 5, 2026 10:00am - 6:00pm ET

Course Format and Delivery

Delivery Method: LiveOnline
Schedule: 3 sessions of 4.5 hour
Cost: $1,595 USD

All sessions are delivered live by an expert instructor in a fully interactive online environment.

*20% off for group bookings when booking 3 or more attendees from the same organization on the same course dates in the same transaction.

About this course

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.  

In this course you will 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

Topics Covered

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

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

Module 2: Prompt Engineering for Developers

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

Lab: Generate:

• API spec
• function stubs
• documentation

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

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

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

Module 4: AI in the Software Development Lifecycle

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

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

Module 5: Building AI-Powered Applications

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

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

Module 6: RAG (Retrieval-Augmented Generation)

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

Lab: Build RAG-based knowledge assistant

Module 7: Testing, QA & Validation with AI

• AI-generated unit tests
• integration testing
• edge case generation
• mutation testing

Lab: Generate + run test suites. Capture results

Module 8: DevOps, CI/CD & Automation

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

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

Module 9: AI Security, Governance & Risk

• Data privacy
• prompt injection risks
• IP protection
• secure usage patterns

Lab: Red-team prompts. define guardrails

Module 10: Advanced AI Patterns (Agents & Workflows)

• Agentic workflows
• multi-step reasoning
• orchestration frameworks

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

Module 11: Adoption, Metrics & Scaling

• Developer productivity metrics
• AI ROI
• scaling across teams
• training models

Lab: Define: team rollout plans, KPIs

Capstone Project (Final)

Participants will: Build an AI-enabled developer workflow including:

• prompt templates
• code generation
• testing automation
• CI/CD integration
• optional RAG layer