AI Fundamentals

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AI Fundamentals

Master the building blocks of modern artificial intelligence - from LLMs to practical implementation strategies

What You’ll Learn

🔬 Large Language Models (LLMs)

Understand the core technology powering modern AI

  • Model Architecture Deep Dive - Transformer architecture, attention mechanisms, and scaling laws
  • Training & Fine-tuning - Pre-training strategies, supervised fine-tuning, and RLHF implementation
  • Model Selection Guide - Choosing between GPT-4, Claude, LLaMA, and open-source alternatives
  • Performance Optimization - Model compression, quantization, and efficient inference strategies
  • Cost Management - Token optimization, caching strategies, and budget-conscious deployment

💬 Generative AI Applications

Build real-world AI-powered applications

  • Content Generation - Text, code, and creative content automation at scale
  • Conversational AI - Building chatbots, virtual assistants, and interactive experiences
  • Code Generation - AI-assisted development, automated testing, and code review
  • Creative Applications - AI in design, writing, marketing, and content strategy
  • Multimodal AI - Combining text, images, audio, and video in AI applications

🎯 Advanced Prompting Techniques

Master the art and science of prompt engineering

  • Prompt Design Patterns - Zero-shot, few-shot, chain-of-thought, and tree-of-thought prompting
  • Prompt Optimization - Systematic approaches to improving prompt performance
  • Template Systems - Building reusable prompt templates and libraries
  • Dynamic Prompting - Context-aware prompts that adapt to user inputs and situations
  • Prompt Security - Preventing prompt injection, jailbreaking, and adversarial attacks

🧩 Context Engineering

Maximize AI performance through intelligent context management

  • Context Window Management - Optimizing for different model context limits and token usage
  • Information Retrieval - Smart context selection and relevance ranking
  • Context Compression - Techniques for fitting more relevant information in limited space
  • Multi-turn Conversations - Maintaining context across long conversations and sessions
  • Context Switching - Adapting context for different tasks, domains, and user personas

📊 AI Model Evaluation

Measure and improve AI system performance

  • Evaluation Frameworks - Building comprehensive evaluation pipelines
  • Benchmark Design - Creating domain-specific benchmarks and test suites
  • Human Evaluation - Designing effective human-in-the-loop evaluation processes
  • Automated Metrics - BLEU, ROUGE, perplexity, and custom evaluation metrics
  • A/B Testing for AI - Comparing model performance in production environments

🔧 Practical Implementation

Turn AI concepts into production-ready systems

  • API Integration - Working with OpenAI, Anthropic, Cohere, and other AI APIs
  • Local Model Deployment - Running open-source models on your infrastructure
  • Batch Processing - Efficient processing of large datasets with AI models
  • Real-time Applications - Building low-latency AI-powered features
  • Error Handling - Robust error handling, fallbacks, and graceful degradation

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Learning Path

🚀 Beginner Track (0-3 months)

  1. Understanding LLMs - Start with the basics of how large language models work
  2. First AI Application - Build a simple chatbot or text generation tool
  3. Prompt Basics - Learn fundamental prompting techniques
  4. API Integration - Connect to AI services and handle responses

📈 Intermediate Track (3-6 months)

  1. Advanced Prompting - Master complex prompting patterns and techniques
  2. Context Engineering - Optimize context for better AI performance
  3. Model Comparison - Evaluate different models for various use cases
  4. Production Deployment - Deploy AI applications to production environments

🎯 Advanced Track (6+ months)

  1. Custom Model Fine-tuning - Train models for specific domains and tasks
  2. Evaluation Systems - Build comprehensive evaluation and monitoring
  3. Performance Optimization - Advanced optimization techniques and strategies
  4. Research & Innovation - Contribute to the AI field with novel approaches

Tools & Resources

Essential Tools

  • OpenAI Playground - Experiment with GPT models interactively
  • Anthropic Console - Test Claude models and prompts
  • Hugging Face Hub - Explore and deploy open-source models
  • LangChain - Framework for building LLM applications
  • Weights & Biases - Experiment tracking and model management
  • “Attention Is All You Need” - The foundational transformer paper
  • “Language Models are Few-Shot Learners” - GPT-3 capabilities and implications
  • “Training language models to follow instructions” - InstructGPT and RLHF
  • “Constitutional AI” - Anthropic’s approach to AI alignment

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