Large Language Models: Complete Guide
Everything you need to know about LLMs: from fine-tuning to production deployment. Master GPT-4, Claude, Llama, and custom models.
Large Language Models (LLMs) have revolutionized how we interact with AI systems, enabling natural language understanding, generation, and reasoning at unprecedented scales. From GPT-4 and Claude to open-source models like Llama and Mistral, LLMs power chatbots, code assistants, content generation tools, and enterprise knowledge systems. This comprehensive guide covers everything from foundational concepts to advanced production deployment.
Understanding LLMs requires knowledge across multiple domains: transformer architectures, attention mechanisms, fine-tuning techniques, prompt engineering, and production infrastructure. Whether you're building a customer support chatbot, implementing RAG for document Q&A, or fine-tuning models for domain-specific tasks, this guide provides the technical depth and practical guidance needed for success.
The LLM landscape evolves rapidly, with new models, techniques, and best practices emerging regularly. This guide consolidates proven approaches that work in production environments, based on our experience deploying LLM systems processing millions of requests monthly. We cover cost optimization, latency reduction, accuracy improvement, and scalability strategies that deliver real business value.
Core LLM Topics Covered
Model Fine-tuning
LoRA, QLoRA, and full fine-tuning techniques to adapt pre-trained models for specific domains, achieving 90-96% accuracy on custom tasks.
RAG Systems
Build retrieval-augmented generation systems with vector databases, embedding models, and retrieval strategies for accurate, context-aware responses.
Prompt Engineering
Master few-shot learning, chain-of-thought prompting, and advanced techniques to maximize LLM performance without model retraining.
Production Deployment
Optimize inference latency, reduce costs through quantization, implement caching strategies, and scale to handle millions of requests.
Each section below includes detailed tutorials, code examples, architecture diagrams, and best practices. We've organized content by use case and complexity level, making it easy to find resources matching your current expertise and project requirements. Whether you're just starting with LLMs or optimizing production systems, you'll find actionable guidance to advance your implementation.
LLM Fine-tuning
RAG & Knowledge Systems
Prompt Engineering
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