Anthropic

Stop using one-size-fits-all prompts. Learn how routing workflows categorize user requests and send them to specialized pipelines for better, more consistent results. Includes real-world examples and implementation best practices.

Learn how to build an MCP client that bridges your application logic with server capabilities. This tutorial covers the two-layer client architecture, implementing core methods, and testing the complete flow.

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

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.

Learn how to build a custom prompt evaluation workflow for AWS-specific code generation. This guide covers creating baseline prompts, automatically generating test datasets using Claude, and setting up a systematic evaluation framework for Python, JSON, and regex outputs.

Learn how to use RAG with Claude to work with large documents. Understand chunking strategies, benefits, challenges, and when to use RAG vs. prompt stuffing.

Learn how to implement grading systems for prompt evaluation workflows. This comprehensive guide covers code graders, model graders, and human graders, with detailed examples of building LLM-as-judge systems that provide objective quality scores and actionable feedback.

Learn how to build the core evaluation pipeline for prompt testing. This guide covers the three essential functions—run_prompt, run_test_case, and run_eval—that process test cases through Claude and collect structured results for analysis.

Walk through a complete RAG pipeline from start to finish. Learn how chunking, embeddings, vector databases, and cosine similarity work together to retrieve relevant information and generate accurate AI responses.

Learn how semantic search uses text embeddings to find the most relevant chunks in a RAG pipeline. Understand embeddings, implement VoyageAI for generating embeddings, and discover how to match user queries with the right content.