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Learn how to reliably extract structured data from LLMs. Covers native JSON modes, Pydantic integration, function calling, and handling edge cases.
Ai-engineering
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Learn how to evaluate LLM applications effectively. Covers evaluation frameworks, metrics, test set creation, and continuous monitoring strategies.- Published on
Learn how prompt caching can reduce LLM API costs by up to 90% and improve latency. Covers implementation strategies for Anthropic, OpenAI, and custom caching solutions.- Published on
Learn how to build production-ready AI agents using Google's Agent Development Kit (ADK). Covers agent architecture, tool integration, multi-agent systems, and deployment with Vertex AI.- Published on
Explore the Model Context Protocol (MCP), an open standard for connecting AI models to external tools and data sources. Learn how to build MCP servers and integrate them with Claude and other AI systems.- Published on
Learn how to structure Python ML projects for production. Covers project layout, dependency management, configuration, testing, and CI/CD practices.- Published on
Learn how to build robust, scalable APIs for machine learning models using FastAPI. Covers async patterns, streaming, background tasks, and production deployment.- Published on
Take your RAG systems to the next level with advanced techniques like query expansion, hybrid search, reranking, and sophisticated chunking strategies.- Published on
A comprehensive guide to understanding, building, and deploying AI agents. Learn about agent architectures, tool use, memory systems, and production considerations.