🧠 LLM & Generative AI Learning Path
Master large language models and build production-ready generative AI applications.
📋 Overview
This learning path takes you from LLM fundamentals to building sophisticated generative AI applications. You'll learn to work with models like GPT-4, Claude, and open-source alternatives, mastering prompt engineering, RAG systems, fine-tuning, and production deployment.
What You'll Learn
- Transformer architecture and attention mechanisms
- Advanced prompt engineering and few-shot learning
- RAG (Retrieval-Augmented Generation) systems
- Fine-tuning and PEFT (LoRA, QLoRA)
- LLM deployment and cost optimization
- Building production LLM applications
Prerequisites
- Programming: Python proficiency required
- ML Basics: Understanding of neural networks helpful
- APIs: Familiarity with REST APIs and JSON
Time Commitment
3-4 months at 10-15 hours per week with hands-on projects.
Learning Objectives
- Understand transformer architecture from scratch
- Learn how attention mechanisms work
- Explore tokenization and embeddings
- Understand pre-training vs fine-tuning
- Compare different LLM architectures (GPT, BERT, T5)
💡 Pro Tip: Don't skip the fundamentals! Understanding how transformers work will make you much more effective at prompt engineering and debugging LLM applications.
🎯 Foundation Project
Build a Mini-GPT: Implement a small transformer from scratch
- Implement multi-head attention in PyTorch/TensorFlow
- Build a character-level GPT model
- Train on Shakespeare text or similar corpus
- Generate text and analyze model behavior
- Document your understanding in a blog post
✅ Checkpoint: You should be able to explain how transformers work and implement basic attention mechanisms.
Learning Objectives
- Master advanced prompt engineering techniques
- Learn few-shot and chain-of-thought prompting
- Work with OpenAI, Anthropic, and open-source APIs
- Implement function calling and tool use
- Build conversational AI applications
- Optimize for cost and latency
💡 Pro Tip: Test your prompts systematically. Create evaluation datasets and measure performance quantitatively. What works for one model may not work for another.
🎯 Practical Project
AI-Powered Research Assistant:
- Build an app that helps users research complex topics
- Implement web scraping to gather information
- Use LLM to summarize and synthesize findings
- Add function calling to fetch real-time data (weather, stocks, news)
- Create a chat interface with conversation memory
- Implement cost tracking and rate limiting
- Deploy with FastAPI backend and React/Streamlit frontend
✅ Checkpoint: You should be able to build conversational AI applications with proper prompt engineering and API integration.
Learning Objectives
- Master RAG (Retrieval-Augmented Generation)
- Work with embeddings and vector databases
- Fine-tune open-source models (Llama, Mistral)
- Use PEFT techniques (LoRA, QLoRA)
- Implement evaluation frameworks
- Optimize retrieval quality
💡 Pro Tip: RAG is often more cost-effective than fine-tuning for knowledge-intensive tasks. Fine-tune when you need to change behavior or style, not just add knowledge.
🎯 Advanced Project
Enterprise Document Intelligence System:
- Data Pipeline: Parse PDFs, Word docs, emails (multi-format)
- Chunking Strategy: Implement semantic chunking with overlap
- Embeddings: Generate embeddings with OpenAI or open-source models
- Vector Store: Set up Pinecone, Weaviate, or Qdrant
- Retrieval: Implement hybrid search (semantic + keyword)
- Re-ranking: Add cross-encoder re-ranking for quality
- Generation: Use retrieved context with GPT-4/Claude
- Evaluation: Create test set and measure accuracy/relevance
- Fine-tuning (Optional): Fine-tune Llama 2 on domain data
Bonus: Add multi-modal support (images, tables) and citation tracking
✅ Checkpoint: You should be able to build production RAG systems and fine-tune open-source LLMs for specific tasks.
Learning Objectives
- Deploy LLMs with proper infrastructure
- Implement caching and cost optimization
- Monitor LLM quality and performance
- Handle safety, toxicity, and hallucinations
- Scale to thousands of users
- Implement A/B testing for prompts
💡 Pro Tip: Implement proper observability from day one. Track token usage, latency, quality metrics, and user feedback. Data-driven optimization is key to production success.
🎯 Production Capstone
Production LLM Platform: Build a complete end-to-end system
- Application: Choose one (chatbot, code assistant, content generator, etc.)
- Multi-model: Support GPT-4, Claude, and open-source fallbacks
- Caching: Implement semantic caching to reduce costs
- Rate Limiting: Add user-level rate limiting and quotas
- Safety: Content moderation and PII detection
- Monitoring: Langfuse or custom observability dashboard
- A/B Testing: Framework to test different prompts/models
- Cost Tracking: Per-user and per-endpoint cost analytics
- Deployment: Kubernetes with horizontal pod autoscaling
- Documentation: API docs, runbooks, architecture diagrams
Deliverable: Production-ready LLM platform handling 1000+ requests/day
✅ Checkpoint: You should be able to deploy and scale LLM applications with proper monitoring, cost optimization, and safety guardrails.
🚀 Career Opportunities
With LLM expertise, you're positioned for some of the hottest roles in tech:
Target Roles
- LLM Engineer — Build and optimize LLM applications
- Prompt Engineer — Design and test prompts at scale
- AI Product Manager — Define LLM-powered product features
- ML Researcher — Advance the state of LLMs
- Freelance Consultant — Help companies implement LLM solutions
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