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Retrieval-Augmented Generation (RAG) is a technique that boosts the performance of large language models (LLMs) by giving them real-time access to external knowledge sources. Rather than relying solely on pre-trained knowledge, RAG allows LLMs to retrieve relevant information from databases or document stores during the generation process, resulting in more accurate, up-to-date, and context-aware responses.

Artificial intelligence (AI) is a branch of computer science that is dedicated to creating systems that exhibit intelligent behaviors that are typically associated with human functions, such as understanding language, learning, reasoning, and solving problems.

To automate the process of reading, understanding, and filtering news articles using a Language Model (LLM), we need to combine data ingestion, natural language understanding, and rule-based or AI-driven filtering into a cohesive processing pipeline that can operate continuously or on demand.

Instructor is a Python library that helps you get structured, predictable data from language models like GPT-4 and Claude. It's like giving the LLM a form to fill out instead of letting it respond however it wants.

The Model Context Protocol (MCP) is an open standard designed to streamline how applications deliver context to large language models (LLMs). Think of MCP as the USB-C of AI integration—just as USB-C provides a universal interface for connecting devices to chargers, displays, and accessories, MCP offers a consistent and structured way to connect LLMs with tools, data sources, APIs, and systems.