Skip to main content

A structured learning path for mastering Generative AI, from core concepts to building production systems.

GenAI Learning Guide

This guide provides a comprehensive learning path for mastering Generative AI, organized by skill level and topic area. Follow the progression from fundamentals to advanced system design.

Learning Path:

  1. Fundamentals → Understand core concepts and terminology
  2. Hands-On Practice → Run LLMs locally and build simple applications
  3. Framework Selection → Choose the right tools for your use case
  4. System Design → Design production-ready GenAI systems
  5. Advanced Topics → Evaluation, security, optimization, and deployment

🎯 Learning Path Overview​

📚 Phase 1: Fundamentals​

Goal: Understand core GenAI concepts and terminology

Essential Reading​

  • The Fundamentals of Agentic Systems - Comprehensive glossary covering:
    • Foundation Models (LLMs)
    • Prompting techniques
    • RAG (Retrieval-Augmented Generation)
    • Vector Databases
    • Memory Systems
    • Agentic Systems
    • Tool Integration
    • Evaluation & Security

Key Concepts to Master​

  1. Foundation Models

    • What are LLMs and how do they work?
    • Model types: text, multimodal, code-specialized
    • Commercial vs. open-source models
    • Context windows and token limits

  2. Prompting

    • Zero-shot, few-shot, chain-of-thought
    • Prompt engineering best practices
    • Role-playing and structured prompts
    • Iterative refinement

  3. RAG (Retrieval-Augmented Generation)

    • When to use RAG vs. direct prompting
    • Vector databases and semantic search
    • Document processing and chunking
    • Retrieval strategies

  4. Agentic Systems

    • Single vs. multi-agent architectures
    • Tool integration and function calling
    • Memory systems (short-term vs. long-term)
    • Agent communication patterns

Practice Exercises​

  • Read the fundamentals guide completely
  • Understand the difference between RAG and fine-tuning
  • Learn when to use agents vs. simple LLM calls
  • Practice writing effective prompts for different use cases

🛠️ Phase 2: Hands-On Practice​

Goal: Get practical experience running LLMs and building simple applications

Running LLMs Locally​

  • Running LLMs Locally - Complete guide covering:
    • LM Studio: User-friendly local LLM interface
    • Ollama: Command-line LLM runner
    • Tool support and CLI integration
    • Hardware considerations (Apple Silicon, clustering)
    • Network setup and remote access

Getting Started​

  1. Install Ollama or LM Studio

    # Ollama on Mac
    brew install ollama
    ollama serve
    ollama run llama3

  2. Integrate with Existing Tools

    • Codex CLI integration
    • Claude Code router setup
    • Custom MCP servers

  3. Build Your First App

    • Simple Q&A application
    • Document summarization tool
    • Basic chatbot

Practice Exercises​

  • Set up Ollama or LM Studio locally
  • Run a model and test basic prompts
  • Integrate with an existing CLI tool
  • Build a simple RAG application with local LLM

🔧 Phase 3: Framework Selection​

Goal: Understand the AI framework landscape and choose the right tools

Framework Comparison​

  • The AI Framework Landscape - Interactive guide covering:
    • Frameworks: LangChain, LangGraph, Haystack, Semantic Kernel
    • Specialized Libraries: LlamaIndex, Guidance, Outlines, Instructor
    • Multi-Agent: AutoGen, CrewAI
    • Platforms: AWS Bedrock, Google ADK
    • Tools: LangSmith, Langfuse, evaluation frameworks

Decision Framework​

Choose LangChain if:

  • You need a versatile, modular framework
  • You want extensive integrations
  • You're prototyping quickly
  • You need a large community

Choose LangGraph if:

  • You need complex, stateful workflows
  • You're building multi-agent systems
  • You need loops and retries
  • You require graph-based orchestration

Choose LlamaIndex if:

  • You're focused on RAG applications
  • You need optimized data indexing
  • You want specialized retrieval

Choose Cloud Platforms if:

  • You need fully managed infrastructure
  • You want enterprise security
  • You're building at scale
  • You prefer serverless architecture

Practice Exercises​

  • Explore the interactive framework graph
  • Compare 2-3 frameworks for your use case
  • Build a simple app with your chosen framework
  • Understand the trade-offs between frameworks

🏗️ Phase 4: System Design​

Goal: Design production-ready GenAI systems with proper architecture

Design Framework​

  • Designing GenAI Systems - Complete decision framework covering:
    • Foundation Decisions: RAG vs. prompting, memory, fine-tuning, model selection
    • Architecture Decisions: Single vs. multi-agent, vector DB selection, evaluation strategy
    • Advanced Decisions: Specialized databases, processing patterns, runtime environments

Key Design Decisions​

  1. RAG vs. Direct Prompting

    • Use RAG when: You need up-to-date information, domain-specific knowledge
    • Use prompting when: General knowledge is sufficient, simple use cases

  2. Memory Strategy

    • Short-term: Conversation context, recent interactions
    • Long-term: User preferences, historical data, knowledge bases

  3. Agent Architecture

    • Single agent: Simple tasks, linear workflows
    • Multi-agent: Complex tasks, parallel processing, specialized roles

  4. Evaluation Strategy

    • Implement evaluation before deployment
    • Use automated testing and human evaluation
    • Monitor production performance

Practice Exercises​

  • Work through the design decision framework
  • Design a system architecture for a real use case
  • Document your design decisions and trade-offs
  • Review and refine your architecture

🚀 Phase 5: Advanced Topics​

Goal: Master evaluation, security, optimization, and production deployment

Evaluation & Testing​

Key Areas:

  • Automated Evaluation: Metrics for quality, relevance, accuracy
  • Human Evaluation: Subjective quality, user satisfaction
  • A/B Testing: Compare different models and prompts
  • Monitoring: Track performance in production

Tools:

  • LangSmith, Langfuse for observability
  • Ragas for RAG evaluation
  • DeepEval for comprehensive testing
  • Custom evaluation frameworks

Security & Guardrails​

Critical Considerations:

  • Input Validation: Sanitize user inputs
  • Output Filtering: Prevent harmful content
  • Data Privacy: Protect sensitive information
  • Access Control: Secure API endpoints
  • Rate Limiting: Prevent abuse

Resources:

  • AWS DataZone for data governance
  • Content moderation APIs
  • Prompt injection prevention
  • Secure model deployment

Cost Optimization​

Strategies:

  • Model Selection: Choose cost-effective models for tasks
  • Caching: Cache common queries and responses
  • Batching: Process multiple requests together
  • Token Management: Optimize prompt length and context
  • Infrastructure: Use appropriate compute resources

Performance Tuning​

Optimization Areas:

  • Latency: Reduce response times
  • Throughput: Handle more concurrent requests
  • Context Management: Efficient context window usage
  • Retrieval Optimization: Improve RAG retrieval speed
  • Model Optimization: Quantization, pruning, distillation

Production Deployment​

Considerations:

  • Scalability: Handle variable load
  • Reliability: Error handling and retries
  • Monitoring: Track metrics and errors
  • Versioning: Manage model and prompt versions
  • CI/CD: Automated testing and deployment

📖 Additional Resources​

AWS Services​

Books & Courses​

Design Patterns & Architecture​

Data Governance​

Practical Examples​

  • How I Use GenAI - Personal workflow and tools
  • MCP server implementations
  • Prompt library examples
  • Integration patterns

🎓 Learning Checklist​

Fundamentals ✅​

  • Understand foundation models and LLMs
  • Master prompting techniques
  • Learn RAG and vector databases
  • Understand agentic systems

Hands-On ✅​

  • Set up local LLM environment
  • Build a simple application
  • Integrate with existing tools
  • Practice with different models

Frameworks ✅​

  • Explore framework landscape
  • Choose appropriate framework
  • Build app with chosen framework
  • Understand framework trade-offs

Design ✅​

  • Work through design framework
  • Make key architecture decisions
  • Design a complete system
  • Document design rationale

Advanced ✅​

  • Implement evaluation strategy
  • Add security and guardrails
  • Optimize for cost and performance
  • Deploy to production

🔗 Related Content​

Next Steps: Start with the fundamentals, then progress through hands-on practice, framework selection, and system design. Use this guide as both a learning path and a reference for building production GenAI systems.