AI & Machine Learning

Explorations in artificial intelligence, large language models, and practical AI integration patterns for modern software development.

Focus Areas

LLM Integration

Patterns and best practices for integrating Large Language Models into applications.

Prompt Engineering

Techniques for effective prompt design and optimization.

AI-Powered Development

Using AI tools to enhance developer productivity and code quality.

Machine Learning Basics

Fundamental concepts and practical implementations.

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Example: AI Integration Architecture

flowchart LR
    subgraph "Client Layer"
        WEB[Web App]
        MOBILE[Mobile App]
    end

    subgraph "API Layer"
        API[REST API]
        WS[WebSocket]
    end

    subgraph "AI Services"
        PROMPT[Prompt Manager]
        CACHE[Response Cache]
        QUEUE[Job Queue]
    end

    subgraph "LLM Providers"
        GPT[OpenAI GPT]
        CLAUDE[Anthropic Claude]
        LOCAL[Local Model]
    end

    subgraph "Data"
        VDB[(Vector DB)]
        RDBMS[(PostgreSQL)]
    end

    WEB --> API
    MOBILE --> API
    WEB --> WS

    API --> PROMPT
    WS --> QUEUE

    PROMPT --> CACHE
    CACHE --> GPT
    CACHE --> CLAUDE
    CACHE --> LOCAL

    QUEUE --> PROMPT

    PROMPT --> VDB
    API --> RDBMS

    style PROMPT fill:#ffd,stroke:#333,stroke-width:2px
    style CACHE fill:#dfd,stroke:#333,stroke-width:2px

This architecture demonstrates: - Multiple LLM provider support for flexibility - Response caching for cost optimization - Vector database for semantic search - Asynchronous processing via job queues

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Recent Explorations

RAG (Retrieval-Augmented Generation)

Implementing context-aware AI responses using document retrieval.

Fine-tuning Strategies

Approaches for customizing models for specific domains.

AI Safety & Ethics

Considerations for responsible AI development and deployment.

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More AI content and practical examples coming soon.