GenAI for Developers

In this GenAI for Developers course, you'll learn:

Introduction to Generative AI

• What is generative AI?
• Overview of LLMs
• Capabilities and limitations
• Real-world use cases

Prompt Engineering Fundamentals

• Prompt structure and best practices
• Zero-shot vs few-shot prompting
• Controlling tone, format, and constraints
• Structured outputs

Working with LLM APIs

• API interaction patterns
• Token usage and context windows
• Streaming responses
• Error handling strategies

Modern LLM Application Patterns

• Core Patterns
  • Retrieval-Augmented Generation (RAG)
  • Embeddings and vector databases
  • Chunking strategies (basic vs semantic)
• Orchestration Concepts
  • Chains vs routing workflows
  • Intent-based decision systems
  • Tool/function calling
• Improving RAG QualityConversation memory and session handling
• Managing context and avoiding drift
• Multi-step workflows
• Streaming UX patterns
  • Hybrid search (keyword + vector)
  • Re-ranking results (conceptual)
  • Why naive RAG fails in production
• When NOT to use LLMs

Building a Production-Ready AI Assistant

• Conversation memory and session handling
• Managing context and avoiding drift
• Multi-step workflows
• Streaming UX patterns

Evaluation and Debugging

• Why LLM outputs are hard to test
• Prompt evaluation strategies
• Golden datasets and test cases
• Introduction to “LLM-as-a-judge”
• Detecting hallucinations and inconsistencies

Cost, Performance, and Architecture

• Model selection tradeoffs (small vs large models)
• Cost optimization strategies
• Latency vs quality tradeoffs
• Caching and batching

Responsible AI and Safety

• Bias and fairness
• Data privacy and protection
• Guardrails and moderation