{"id":24,"date":"2026-04-27T00:32:31","date_gmt":"2026-04-27T00:32:31","guid":{"rendered":"http:\/\/localhost:8080\/?program=ai-for-software-developers-and-software-architects-5-day"},"modified":"2026-04-27T01:12:18","modified_gmt":"2026-04-27T01:12:18","slug":"ai-for-developers-5-day","status":"publish","type":"program","link":"https:\/\/jegan.my\/?program=ai-for-developers-5-day","title":{"rendered":"AI for Developers &#8211; 5 Day"},"content":{"rendered":"<h2>Day 1 &#8211; Foundations of AI and Generative AI for Developers<\/h2>\n<h3>1. Introduction to AI in Software Development<\/h3>\n<ul>\n<li>Evolution of AI in software engineering<\/li>\n<li>Role of AI in modern development lifecycle<\/li>\n<li>AI as a productivity tool (not a replacement)<\/li>\n<li>Real-world use cases in coding, testing, and automation<\/li>\n<\/ul>\n<h3>2. Fundamentals of Generative AI<\/h3>\n<ul>\n<li>What is Generative AI<\/li>\n<li>Types of Generative Models (Text, Image, Code, Audio)<\/li>\n<li>Overview of models (GPT, LLaMA, Claude, Gemini)<\/li>\n<li>Business and engineering applications<\/li>\n<\/ul>\n<h3>3. Machine Learning Foundations<\/h3>\n<ul>\n<li>Supervised, Unsupervised, Reinforcement Learning<\/li>\n<li>Neural Networks Basics<\/li>\n<li>Introduction to Deep Learning<\/li>\n<li>Training vs Inference<\/li>\n<\/ul>\n<h3>4. Understanding LLM Internals (Simplified)<\/h3>\n<ul>\n<li>Tokenization<\/li>\n<li>Embeddings and vector representation<\/li>\n<li>Attention mechanism (conceptual)<\/li>\n<li>Transformers vs RNN vs LSTM<\/li>\n<\/ul>\n<h3>5. Mathematical Intuition (High-Level)<\/h3>\n<ul>\n<li>Vectors and similarity<\/li>\n<li>Probability intuition in prediction<\/li>\n<li>Loss functions (conceptual)<\/li>\n<li>Why Transformers outperform RNN\/LSTM<\/li>\n<\/ul>\n<h3>6. Lab 1: Exploring AI Tools<\/h3>\n<ul>\n<li>Use ChatGPT for coding tasks<\/li>\n<li>Compare outputs with different prompts<\/li>\n<li>Basic debugging with AI assistance<\/li>\n<\/ul>\n<h2>Day 2 &#8211; Prompt Engineering and AI-Assisted Development (Vibe Coding)<\/h2>\n<h3>7. Prompt Engineering Fundamentals<\/h3>\n<ul>\n<li>What is Prompt Engineering<\/li>\n<li>Prompt patterns (Zero-shot, Few-shot, Chain-of-Thought)<\/li>\n<li>Designing structured prompts<\/li>\n<li>Avoiding hallucinations<\/li>\n<\/ul>\n<h3>8. Vibe Coding (AI-Assisted Development Workflow)<\/h3>\n<ul>\n<li>Concept of Vibe Coding<\/li>\n<li>AI in coding lifecycle (design to code to test to deploy)<\/li>\n<li>Using AI for:<\/li>\n<li>Code generation<\/li>\n<li>Refactoring<\/li>\n<li>Documentation<\/li>\n<li>Test case generation<\/li>\n<\/ul>\n<h3>9. AI Coding Tools and Platforms<\/h3>\n<ul>\n<li>ChatGPT and advanced usage<\/li>\n<li>Codex and code assistants<\/li>\n<li>GitHub Copilot and alternatives<\/li>\n<li>IDE integration strategies<\/li>\n<\/ul>\n<h3>10. Building a Prompt-Driven Development Workflow<\/h3>\n<ul>\n<li>Reusable prompt templates<\/li>\n<li>Role-based prompting (architect, developer, tester)<\/li>\n<li>Standardizing prompts in teams<\/li>\n<\/ul>\n<h3>11. Lab 2: AI-Powered Development<\/h3>\n<ul>\n<li>Generate CRUD application using prompts<\/li>\n<li>Refactor existing code using AI<\/li>\n<li>Generate unit tests using AI<\/li>\n<\/ul>\n<h2>Day 3 &#8211; Building AI Applications with APIs, RAG and Embeddings<\/h2>\n<h3>12. Working with LLM APIs<\/h3>\n<ul>\n<li>Introduction to AI APIs<\/li>\n<li>Calling ChatGPT API<\/li>\n<li>Structuring prompts programmatically<\/li>\n<li>Handling responses and errors<\/li>\n<\/ul>\n<h3>13. Embeddings and Vector Databases<\/h3>\n<ul>\n<li>What are embeddings<\/li>\n<li>Similarity search concepts<\/li>\n<li>Vector databases (FAISS, Pinecone, Weaviate)<\/li>\n<li>Use cases in development<\/li>\n<\/ul>\n<h3>14. Retrieval-Augmented Generation (RAG)<\/h3>\n<ul>\n<li>What is RAG<\/li>\n<li>Why RAG is important for enterprise systems<\/li>\n<li>RAG architecture<\/li>\n<li>Document indexing and retrieval<\/li>\n<\/ul>\n<h3>15. Building AI-Powered Applications<\/h3>\n<ul>\n<li>Chatbot architecture<\/li>\n<li>Knowledge assistant design<\/li>\n<li>Integrating AI into web applications<\/li>\n<\/ul>\n<h3>16. Lab 3: Build a RAG-Based Coding Assistant<\/h3>\n<ul>\n<li>Connect to LLM API<\/li>\n<li>Load documentation into vector DB<\/li>\n<li>Build a developer assistant<\/li>\n<\/ul>\n<h2>Day 4 &#8211; Fine-Tuning, Open Source Models and Agentic AI<\/h2>\n<h3>17. Fine-Tuning and Customization<\/h3>\n<ul>\n<li>Why fine-tuning is needed<\/li>\n<li>LoRA and QLoRA<\/li>\n<li>Parameter-efficient tuning (PEFT)<\/li>\n<li>Instruction tuning vs prompting<\/li>\n<\/ul>\n<h3>18. Working with Open Source LLMs<\/h3>\n<ul>\n<li>Overview of open-source models (LLaMA, Mistral)<\/li>\n<li>Running models locally<\/li>\n<li>Customizing models for organization standards<\/li>\n<\/ul>\n<h3>19. Introduction to Agentic AI<\/h3>\n<ul>\n<li>What is Agentic AI<\/li>\n<li>Single-agent vs multi-agent systems<\/li>\n<li>AI agents for coding tasks<\/li>\n<li>Tool usage and orchestration<\/li>\n<\/ul>\n<h3>20. Multi-Agent Systems for Development<\/h3>\n<ul>\n<li>Developer agent<\/li>\n<li>Tester agent<\/li>\n<li>Reviewer agent<\/li>\n<li>Workflow orchestration<\/li>\n<\/ul>\n<h3>21. Lab 4: Build a Multi-Agent Coding Assistant<\/h3>\n<ul>\n<li>Create agents for coding tasks<\/li>\n<li>Chain agents together<\/li>\n<li>Automate development workflow<\/li>\n<\/ul>\n<h2>Day 5 &#8211; Enterprise AI Architecture, Security and Capstone<\/h2>\n<h3>22. Enterprise AI Architecture<\/h3>\n<ul>\n<li>AI system design principles<\/li>\n<li>API orchestration and model routing<\/li>\n<li>Microservices with AI<\/li>\n<li>Scaling AI systems<\/li>\n<\/ul>\n<h3>23. AI Security and Risk Management<\/h3>\n<ul>\n<li>Prompt injection attacks<\/li>\n<li>Data privacy and leakage<\/li>\n<li>Secure AI API design<\/li>\n<li>AI governance concepts<\/li>\n<\/ul>\n<h3>24. Advanced RAG and Knowledge Systems<\/h3>\n<ul>\n<li>Hybrid search (dense + sparse)<\/li>\n<li>Knowledge graphs integration<\/li>\n<li>Enterprise AI assistants<\/li>\n<\/ul>\n<h3>25. AI in Software Architecture<\/h3>\n<ul>\n<li>AI-driven system design<\/li>\n<li>AI in DevOps and CI\/CD<\/li>\n<li>AI adoption strategy for organizations<\/li>\n<\/ul>\n<h3>26. Final Capstone Project<\/h3>\n<ul>\n<li>Participants will build an AI-Powered Development Assistant:<\/li>\n<li>Prompt-based code generator<\/li>\n<li>RAG-enabled documentation assistant<\/li>\n<li>Multi-agent workflow (developer + tester)<\/li>\n<li>API-integrated system<\/li>\n<\/ul>\n<h3>27. Final Presentation and Evaluation<\/h3>\n<ul>\n<li>Project demonstration<\/li>\n<li>Architecture explanation<\/li>\n<li>Best practices discussion<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>This 5-day accelerated program is designed for software developers and architects who want to integrate Artificial Intelligence into their software development lifecycle. The course provides a structured journey from understanding Generative AI fundamentals to building AI-powered development environments, working with Large Language Models, and designing agentic AI systems. Participants will learn how to use tools like ChatGPT and Codex effectively, build prompt-driven development workflows, integrate AI APIs into applications, implement Retrieval-Augmented Generation, and explore model fine-tuning techniques such as LoRA. By the end of the program, participants will be able to design AI-assisted development pipelines and understand how to scale them into enterprise-level solutions.<\/p>\n","protected":false},"featured_media":0,"template":"","class_list":["post-24","program","type-program","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/jegan.my\/index.php?rest_route=\/wp\/v2\/program\/24","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/jegan.my\/index.php?rest_route=\/wp\/v2\/program"}],"about":[{"href":"https:\/\/jegan.my\/index.php?rest_route=\/wp\/v2\/types\/program"}],"wp:attachment":[{"href":"https:\/\/jegan.my\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=24"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}