llm

Comprehensive guide to preparing data for RAG systems covering document parsing, chunking strategies, contextual enrichment, and embedding optimization

A practical, implementation-focused glossary for developers navigating the AI/LLM landscape. From tokens to agents, RAG to fine-tuning, with code examples and honest assessments.

A comprehensive technical guide to building production-grade prompt engineering systems, covering systematic design, security, observability, and cost optimization for enterprise LLM applications.

A comprehensive guide to advanced RAG techniques including hybrid search, reranking, GraphRAG, and self-corrective patterns with production AWS implementation examples.

Token-based pricing creates unique cost challenges for production LLM applications. Learn systematic optimization strategies including prompt caching, model routing, and token budgets to reduce costs by 60-80% without sacrificing quality.

Explore the architectural evolution from rule-based chatbots to autonomous AI agents. Learn ReAct, Plan-and-Execute, and multi-agent patterns with TypeScript implementations and practical migration strategies.

Real lessons from deploying LangChain applications to production. Learn about the anti-patterns that cause failures and the patterns that enable success, with working code examples and cost optimization strategies.

Learn how MCP standardizes AI tool integration, with TypeScript examples for building servers, managing security, and optimizing performance in production.

A guide to implementing AI-assisted code reviews based on real enterprise experience. Learn what AI catches that humans miss, where humans still excel, and how to build effective human-AI collaboration in code review processes.