Generative AI for Software Developers
Generative Artificial Intelligence (Gen-AI)
COURSE OVERVIEW
This intensive, five-day program is designed to bridge the gap between traditional software engineering and the rapidly evolving world of Generative AI. Developers will move beyond simply using AI chatbots to understanding the underlying mechanics of Large Language Models (LLMs), mastering prompt engineering, and building sophisticated, production-ready AI applications. By the end of this course, participants will be equipped to integrate AI into their daily workflows and architect systems that leverage RAG (Retrieval-Augmented Generation), vector databases, and autonomous agents.
COURSE OBJECTIVES
By the end of this course, participants will be able to:
- Deconstruct the Transformer architecture and understand how LLMs process tokens and embeddings.
- Master advanced prompting techniques like Chain-of-Thought and Few-Shot learning to improve code reliability.
- Architect applications that integrate with leading AI APIs (OpenAI, Anthropic, etc.) using secure authentication methods.
- Implement RAG pipelines to allow AI models to interact with private, domain-specific data.
- Evaluate and mitigate risks related to AI security, data privacy, and model hallucinations.
- Build a functional AI-powered tool or agent as a final capstone project.
Duration: 5 Days / 40 Hours
Delivery Method: Classroom-based, Virtual Instructor Led Training
COURSE OUTLINE
Day 1: Foundations of Generative AI for Developers
Focus: Understanding the "How" and "Why" behind the models.
- Evolutionary Context: Introduction to AI, ML, Deep Learning, and the jump to Generative AI.
- Deep Dive into LLMs: The Transformer architecture (Attention mechanisms, Encoders, and Decoders).
- The Developer’s Vocabulary: Master concepts like tokens, embeddings, and context windows.
- Ecosystem Overview: Navigating closed vs. open-source models (GPT-4 vs. Llama).
- Setup: Configuring IDEs (VS Code/Cursor) and AI coding assistants (GitHub Copilot).
- Hands-on: Benchmarking different models for specific coding tasks.
Day 2: Prompt Engineering & AI-Assisted Development
Focus: Turning AI into a high-performance pair programmer.
- Advanced Prompting: Zero-shot, one-shot, and few-shot learning strategies.
- Reasoning Frameworks: Using Chain-of-Thought (CoT) to solve complex logic puzzles.
- Development Lifecycle:
- Generation: Writing boilerplate and logic from scratch.
- Refactoring: Modernizing legacy code and optimizing performance.
- Documentation: Auto-generating docstrings and READMEs.
- Debugging: Using AI to interpret stack traces and suggest fixes.
- Hands-on Labs: Refactoring a monolithic script into modular, documented microservices.
Day 3: API Integration & Application Development
Focus: Programming with LLMs via APIs.
- The AI Stack: Introduction to RESTful AI APIs and SDKs.
- Security First: Managing API keys, rate limits, and authentication.
- Function Calling: Teaching models to interact with your own functions and external databases.
- State Management: Handling conversation history and context management in chatbots.
- Hands-on: Building a CLI tool or a web-based AI assistant that executes real code.
Day 4: Advanced Generative AI Development
Focus: Building smarter systems with memory and tools.
- Retrieval-Augmented Generation (RAG): Connecting models to external data sources.
- Vector Infrastructure: Understanding vector databases (Pinecone, Milvus, or Weaviate).
- Fine-tuning vs. RAG: When to retrain a model versus when to provide better context.
- AI Agents: Designing autonomous workflows that can plan and execute multi-step tasks.
- Multi-modal: Brief overview of integrating vision and audio models into apps.
- Hands-on Project: Implementing a RAG system that "chats" with a repository of local PDF documentation.
Day 5: Security, Governance, and Capstone
Focus: Shipping responsible and reliable AI.
- The Dark Side: Identifying Prompt Injection, data leakage, and insecure output handling.
- Reliability: Managing and reducing hallucinations through validation layers.
- Compliance: Navigating GDPR, privacy concerns, and responsible AI practices.
- The Future: Agents, "Small" Language Models (SLMs), and on-device AI.
- Capstone Implementation: Final development time for the integrated project.
- Presentations: Demonstrating the Capstone project and peer evaluation.
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