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3.1 Level 1: The Fundamentals of LLM APIs

🤖 OLD vs. NEW Apps with LLM

📱 Evolution of Apps

graph LR
    subgraph "🕰️ OLD APPS"
        A1[📱 Frontend] --> A2[🔧 Backend] --> A3[💾 Database]
        A2 --> A4[📊 Analytics]
    end

    style A1 fill:#ffebee
    style A2 fill:#ffebee
    style A3 fill:#ffebee
    style A4 fill:#ffebee

graph LR
    subgraph "🚀 GENAI APPS"
        B1[📱 Frontend] --> B2[🔧 Backend] --> B3[💾 Database]
        B2 --> B4[📊 Analytics]
        B2 --> B5[🤖 LLM Endpoint
✨ GenAI Magic]
    end
    style B1 fill:#e8f5e8
    style B2 fill:#e8f5e8
    style B3 fill:#e8f5e8
    style B4 fill:#e8f5e8
    style B5 fill:#fff3e0

Key Points to Cover:

Overview of LLM API Providers

  • Major Platforms
  • OpenAI API (GPT-4, GPT-3.5, etc.)
  • Google AI Platform (Gemini)
  • Anthropic (Claude)
  • Azure OpenAI Service
  • AWS Bedrock
  • Open-source alternatives (Hugging Face, Ollama)
graph TD
    You[🤖 LLM Endpoint
✨ GenAI Magic] --> |Choose your fighter!| Decision{Which API?}

    Decision -->|"Need GPT-4"| OpenAI[OpenAI 🟢
Pro: Most popular
Con: $$$]
    Decision -->|"Long context"| Claude[Claude 🔵
Pro: 200k tokens!
Con: Newer]
    Decision -->|"Google ecosystem"| Gemini[Gemini 🔴
Pro: Multimodal
Con: Evolving]
    Decision -->|"Enterprise ready"| Azure[Azure OpenAI 🟦
Pro: Compliance
Con: Complex setup]
    Decision -->|"Open source fan"| OSS[Hugging Face 🤗
Pro: Free & flexible
Con: Self-hosted]

    style OpenAI fill:#90EE90
    style Claude fill:#ADD8E6
    style Gemini fill:#FFB6C1
    style Azure fill:#B0C4DE
    style OSS fill:#FFD700
  • Comparison Criteria
  • Pricing models
  • Rate limits and quotas
  • Model capabilities and specializations
  • Latency and performance
  • Data privacy policies

API Basics

  • Authentication and Setup
  • API keys and security
  • Environment configuration

  • SDK installation

  • Core API Concepts

  • Requests and responses
  • Message format (system, user, assistant)
  • Tokens and token counting
  • Temperature and sampling parameters

  • Max tokens and response length

  • Key Parameters Explained

  • Temperature (creativity vs. consistency)
  • Top-p (nucleus sampling)
  • Frequency and presence penalties
  • Stop sequences
  • Streaming vs. batch responses
graph LR
    A[Your Prompt] --> B{Temperature Setting}

    B -->|0.0 - 0.3| C[🤖 Robot Mode
Very predictable
Same output every time
Good for: Code, facts]

    B -->|0.4 - 0.7| D[👔 Balanced Professional
Reliable but varied
Good for: Most tasks]

    B -->|0.8 - 1.0| E[🎨 Creative Genius
Wild & unpredictable
Good for: Stories, ideas]

    B -->|1.5 - 2.0| F[🌪️ Chaos Mode
Random word salad
Good for: Entertainment]

    style C fill:#e3f2fd
    style D fill:#fff9c4
    style E fill:#f3e5f5
    style F fill:#ffccbc

Live Coding Demo

  • Simple Application Example (Python)
  • Setting up the environment
  • Making first API call
  • Parsing and displaying results

  • Error handling

  • Practical Use Cases

  • Text summarization
  • Code generation from description
  • Language translation
  • Sentiment analysis
  • Q&A system

Best Practices

  • Cost Management
  • Monitoring token usage
  • Caching strategies

  • Prompt optimization for efficiency

  • Error Handling

  • Rate limit handling
  • Retry logic with exponential backoff
  • Timeout management

  • Fallback strategies

  • Security Considerations

  • Protecting API keys
  • Input sanitization
  • Output validation
  • PII handling

Hands-On Exercise Ideas

  • Build a simple chatbot
  • Create a code documentation generator
  • Implement a text classification service
  • Design a custom code assistant for specific framework