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Training Program

AI for Developers

This 5-day accelerated program equips software developers and architects with practical knowledge to apply Generative AI, LLM APIs, RAG, prompt-driven development, and agentic AI patterns across the modern software delivery lifecycle.

Duration

5 Days

Overview

This 5-day hands-on program is designed for software developers and architects who want to use Generative AI as a practical development partner throughout the software delivery lifecycle. The course takes participants from AI fundamentals and prompt-driven development into real-world AI-assisted workflows for coding, debugging, testing, documentation, and system design. Participants will learn how to work effectively with tools such as ChatGPT and Codex, integrate AI APIs into applications, use Retrieval-Augmented Generation (RAG), explore agentic AI patterns, and understand fine-tuning approaches such as LoRA. By the end of the program, participants will be able to design and apply AI-assisted development pipelines that can scale into enterprise software engineering environments.

Introduction

Artificial Intelligence is changing how software is designed, built, tested, documented, and delivered. For developers and architects, AI is no longer just a research topic or a business feature. It is becoming a practical development partner that can improve productivity, support better engineering workflows, and influence system design. This program introduces the key concepts, tools, and development patterns needed to work effectively with Generative AI in software engineering. Participants will explore AI-assisted coding workflows, prompt engineering, LLM integration, embeddings, RAG, model customization, and enterprise AI architecture in a practical way that connects directly to real-world development environments.

Topics Covered

  • Introduction to AI in Software Development
  • Fundamentals of Generative AI
  • Machine Learning Foundations
  • Understanding LLM Internals (Simplified)
  • Mathematical Intuition (High-Level)
  • Prompt Engineering Fundamentals
  • Vibe Coding (AI-Assisted Development Workflow)
  • AI Coding Tools and Platforms
  • Building a Prompt-Driven Development Workflow
  • Working with LLM APIs
  • Embeddings and Vector Databases
  • Retrieval-Augmented Generation (RAG)
  • Building AI-Powered Applications
  • Fine-Tuning and Customization
  • Working with Open Source LLMs
  • Introduction to Agentic AI
  • Multi-Agent Systems for Development
  • Enterprise AI Architecture
  • AI Security and Risk Management
  • Advanced RAG and Knowledge Systems
  • AI in Software Architecture
  • Final Capstone Project
  • Final Presentation and Evaluation

Audience Profile

Software Developers Software Architects Technical Leads QA Engineers IT professionals moving into AI-assisted software development

Prerequisites

Programming experience (Java, JavaScript, Python, PHP, etc.) Basic understanding of APIs and web applications Familiarity with software development lifecycle

Learning Outcomes

  1. Apply structured prompt engineering techniques for software development tasks
  2. Use AI tools for coding, debugging, testing, refactoring, and documentation
  3. Design reusable prompt templates and role-based AI workflows for development teams
  4. Integrate AI APIs, RAG patterns, and agentic workflows into real-world applications
  5. Understand AI limitations, risks, and security considerations in software engineering
  6. Design and apply complete AI-assisted development pipelines in enterprise environments
Day 1 - Foundations of AI and Generative AI for Developers
1. Introduction to AI in Software Development
  • Evolution of AI in software engineering
  • Role of AI in modern development lifecycle
  • AI as a productivity tool (not a replacement)
  • Real-world use cases in coding, testing, and automation
  • AI-assisted software engineering concept
  • Traditional development vs AI-assisted development
  • When to Use AI in Development
2. Fundamentals of Generative AI
  • What is Generative AI
  • Types of Generative Models (Text, Image, Code, Audio)
  • Overview of models (GPT, LLaMA, Claude, Gemini)
  • Business and engineering applications
  • AI for Coding, Debugging, Testing, and Docs
  • Choosing the Right AI Use Case
3. Machine Learning Foundations
  • Supervised, Unsupervised, Reinforcement Learning
  • Neural Networks Basics
  • Introduction to Deep Learning
  • Training vs Inference
  • Why Developers Should Know the Basics
  • Training, Inference, and Model Behavior in Practice
4. Understanding LLM Internals (Simplified)
  • Tokenization
  • Embeddings and vector representation
  • Attention mechanism (conceptual)
  • Transformers vs RNN vs LSTM
  • Token Limits, Context, and Embeddings in Practice
  • LLM Behavior and Output Reliability
5. Mathematical Intuition (High-Level)
  • Vectors and similarity
  • Probability intuition in prediction
  • Loss functions (conceptual)
  • Why Transformers outperform RNN/LSTM
  • Practical intuition for similarity, retrieval, and output quality
  • Understanding these ideas without deep mathematical treatment
6. Lab 1: Exploring AI Tools
  • Use ChatGPT for coding tasks
  • Compare outputs with different prompts
  • Basic debugging with AI assistance
  • Traditional vs AI-Assisted Task Completion
  • Rewrite weak prompts into structured prompts
  • Generate code, refine it, and review the output
  • Accepting, Revising, or Rejecting AI Output
  • Reusable Prompt Templates for Development Tasks
Day 2 - Prompt Engineering and AI-Assisted Development (Vibe Coding)
7. Prompt Engineering Fundamentals
  • What is Prompt Engineering
  • Prompt patterns (Zero-shot, Few-shot, Chain-of-Thought)
  • Designing structured prompts
  • Avoiding hallucinations
  • Structured Prompts with Role, Context, and Constraints
  • Prompt refinement through iteration
  • Reusable Prompts for Coding, Testing, Debugging, and Docs
8. Vibe Coding (AI-Assisted Development Workflow)
  • Concept of Vibe Coding
  • AI in coding lifecycle (design to code to test to deploy)
  • Using AI for:
  • Code generation
  • Refactoring
  • Documentation
  • Test case generation
  • AI-Assisted Requirement Breakdown
  • From Idea to Implementation with AI
  • When AI Code Needs Review or Redesign
  • Speed, Correctness, and Maintainability
9. AI Coding Tools and Platforms
  • ChatGPT and advanced usage
  • Codex and code assistants
  • GitHub Copilot and alternatives
  • IDE integration strategies
  • Choosing the right tool for the job
  • Chat, IDE, and API Workflow Differences
  • Working Across Multiple AI Tools
10. Building a Prompt-Driven Development Workflow
  • Prompt workflow checkpoints
  • Role-based prompting (architect, developer, tester)
  • Standardizing prompts in teams
  • Projects, artifacts, and conversations
  • Reusable prompt templates and skills
  • Workflow Management for Repeatable Tasks
  • Organizing Prompts and Outputs for Reuse
11. Lab 2: AI-Powered Development
  • Generate CRUD application using prompts
  • Refactor existing code using AI
  • Generate unit tests using AI
  • Generate code from requirements
  • Refine and debug AI-generated code
  • Generate Tests and Supporting Documentation
  • Review outputs and improve prompts
  • Build an End-to-End AI Workflow
Day 3 - Building AI Applications with APIs, RAG and Embeddings
12. Working with LLM APIs
  • Introduction to AI APIs
  • Calling ChatGPT API
  • Structuring prompts programmatically
  • Handling responses and errors
  • Managing Prompt Payloads and Response Flow
  • Using System, User, and Task Context
  • Integrating AI into Development Workflows
  • Handling Partial or Unreliable Outputs
13. Embeddings and Vector Databases
  • What are embeddings
  • Similarity search concepts
  • Vector databases (FAISS, Pinecone, Weaviate)
  • Use cases in development
  • Embeddings for Developer Knowledge Retrieval
  • Documentation, Code Notes, and Searchable Context
  • Vector Search for Developer Assistants
14. Retrieval-Augmented Generation (RAG)
  • What is RAG
  • Why RAG is important for enterprise systems
  • RAG architecture
  • Document indexing and retrieval
  • Documentation and Project Context for RAG
  • Grounding Responses with Trusted Sources
  • Reducing Hallucinations in Engineering Workflows
  • Documentation-Aware Developer Assistants
15. Building AI-Powered Applications
  • Chatbot architecture
  • Knowledge assistant design
  • Integrating AI into web applications
  • Building coding assistants and knowledge assistants
  • AI in Internal Tools and Engineering Platforms
  • Design Considerations for Developer-Facing AI
16. Lab 3: Build a RAG-Based Coding Assistant
  • Connect to LLM API
  • Load documentation into vector DB
  • Build a developer assistant
  • Retrieve Context for Development Questions
  • Answer Questions with Grounded Documentation
  • Testing Context Quality and Responses
  • Build a Developer Support Workflow
Day 4 - Fine-Tuning, Open Source Models and Agentic AI
17. Fine-Tuning and Customization
  • Why fine-tuning is needed
  • LoRA and QLoRA
  • Parameter-efficient tuning (PEFT)
  • Instruction tuning vs prompting
  • Fine-Tuning vs Prompting and RAG
  • Customization Decision Criteria
  • Enterprise Customization Strategies
18. Working with Open Source LLMs
  • Overview of open-source models (LLaMA, Mistral)
  • Running models locally
  • Customizing models for organization standards
  • Model Selection by Project Need
  • Hosted vs Self-Managed Models
  • Enterprise Adoption Considerations
19. Introduction to Agentic AI
  • What is Agentic AI
  • Single-agent vs multi-agent systems
  • AI agents for coding tasks
  • Tool usage and orchestration
  • AI Agents in Engineering Workflows
  • Task Planning and Execution
  • Agentic Workflow Examples in Software Engineering
20. Multi-Agent Systems for Development
  • Developer agent
  • Tester agent
  • Reviewer agent
  • Workflow orchestration
  • Architect agent
  • Agent Coordination
  • When to Use Multi-Agent Workflows
21. Lab 4: Build a Multi-Agent Coding Assistant
  • Create agents for coding tasks
  • Chain agents together
  • Automate development workflow
  • Defining Agent Responsibilities
  • Chaining Agents for Design, Code, Test, and Review
  • Human Intervention Points
  • Evaluating Multi-Agent Strengths and Limits
Day 5 - Enterprise AI Architecture, Security and Capstone
22. Enterprise AI Architecture
  • AI system design principles
  • API orchestration and model routing
  • Microservices with AI
  • Scaling AI systems
  • Standalone, Embedded, and Platform AI Options
  • Model Orchestration in Applications
  • From Experimentation to Production Architecture
  • Reliability, Cost, and Maintainability Tradeoffs
23. AI Security and Risk Management
  • Prompt injection attacks
  • Data privacy and leakage
  • Secure AI API design
  • AI governance concepts
  • Safe AI Use in Software Delivery
  • Validating Generated Code
  • Sensitive Data in Prompts and Responses
  • Approval and Review Controls
24. Advanced RAG and Knowledge Systems
  • Hybrid search (dense + sparse)
  • Knowledge graphs integration
  • Enterprise AI assistants
  • Internal Documentation and Technical Knowledge
  • Better Retrieval for Better Answers
  • Maintaining Trustworthy Engineering Assistants
25. AI in Software Architecture
  • AI-driven system design
  • AI in DevOps and CI/CD
  • AI adoption strategy for organizations
  • AI in Product Architecture vs Engineering Workflow
  • AI in Support and Internal Engineering Tools
  • Team Enablement Beyond Individual Use
26. Final Capstone Project
  • Participants will build an AI-Powered Development Assistant:
  • Prompt-based code generator
  • RAG-enabled documentation assistant
  • Multi-agent workflow (developer + tester)
  • API-integrated system
  • Define a Real-World Development Scenario
  • Prompts for Design, Code, Test, and Documentation
  • Integrate APIs or RAG Where Relevant
  • Role-Based or Multi-Agent Workflow
  • Build a Practical AI Development Pipeline
27. Final Presentation and Evaluation
  • Project demonstration
  • Architecture explanation
  • Best practices discussion
  • Explaining Architectural Choices
  • AI Limits and Human Decision Points
  • What to Automate and What Not To