generative-ai
Evaluating LLM Outputs
How to build evaluation systems for LLM-powered features — covering human eval, automated checks, LLM-as-judge, eval datasets, and regression prevention.
Prompting Best Practices
Prompt engineering is the practice of designing inputs to an LLM to reliably get the outputs you want — it's often the fastest way to improve AI behavior before reaching for fine-tuning.
Structured Outputs
Structured outputs are techniques for getting LLMs to reliably produce machine-parseable data like JSON — essential for any pipeline that needs to process model responses programmatically.
The MCP Ecosystem
Model Context Protocol (MCP) is an open standard that lets AI models connect to external tools and data sources — a universal connector for building AI integrations without custom per-model code.
RAG — Retrieval-Augmented Generation
RAG gives an LLM access to specific knowledge at query time by retrieving relevant documents and passing them as context — without retraining the model.