By Rina Diane Caballar,Cole Stryker
Publication Date: 2026-02-05 12:00:00
High-quality data can lead to powerful generative AI models. And while data reviews, data integration, and data preparation are typical aspects of the generative AI integration process, adding relevant context can further improve data quality and lead to more context-aware results.
One way to incorporate context is to optimize a pre-trained model on smaller data sets specific to your domain or real-world tasks and use cases. This helps save the significant time, effort, and cost associated with training models from scratch.
Both Retrieval Augmented Generation (RAG) and Model Context Protocol (MCP) now integrate context in real time. A RAG system retrieves data from an external knowledge base, supplements the prompt with extended context from the retrieved data, and generates a response. MCP works similarly, but instead of adding context before generation, as RAG does, MCP merges context during generation. It acts as a standardized layer for AI applications to connect…
