Anthropic

Should you build a workflow or an agent? Learn the key differences, trade-offs, and when to use each approach for reliable AI-powered applications. Includes decision framework, hybrid patterns, and real-world examples.

Resources in MCP allow your server to expose data that can be directly included in prompts, rather than requiring tool calls to access information. This creates a more efficient way to provide context to AI models like Claude.

Speed up AI responses by up to 85% and cut costs dramatically — all by letting Claude remember what it just processed. A comprehensive guide to implementing prompt caching with TypeScript and Python.

A comprehensive quick-reference guide for implementing prompt caching in Claude. Includes code snippets, common patterns, debugging tips, and a production checklist.

Model Context Protocol (MCP) is a communication layer that provides Claude with context and tools without requiring you to write tedious integration code. Learn how MCP shifts the burden of tool definitions and execution to specialized servers, saving developers thousands of lines of code.

Stop letting users reinvent the wheel. Learn how to create battle-tested prompts in MCP servers that deliver consistent, high-quality results every time. Complete guide covering server-side implementation, client integration, and best practices.

Learn how to build a production-ready hybrid search system that combines the semantic understanding of vector embeddings with the precision of BM25 lexical search using Reciprocal Rank Fusion (RRF). Includes architecture patterns, mathematical explanations, and real-world examples.

When building MCP servers, you need a way to test your functionality without connecting to a full application. The Python MCP SDK includes a built-in browser-based inspector that lets you debug and test your server in real-time.

Take your prompt caching implementation to the next level with advanced strategies, edge case handling, and production-ready patterns for high-volume applications.

Stop fighting with massive prompts that Claude can't handle consistently. Learn how workflow chaining produces better results by breaking complex tasks into focused, sequential steps. Includes real-world examples and best practices.