Model Context Protocol is a fundamental concept in AI systems that defines how conversational models manage and utilize information from previous interactions. It enables AI models to maintain coherence by remembering past details and building upon conversation history to generate relevant responses.
Context windows are a crucial limitation in AI models. They define the maximum number of tokens that can be processed simultaneously. When conversations become too long, they exceed the context window, requiring strategies like summarization or truncation to maintain functionality while preserving important information.
Several strategies help manage context effectively. Prompt history passing includes previous interactions directly in the current input. Summarization condenses long conversations into key points. Attention mechanisms allow models to focus on relevant parts of the context. External retrieval systems can fetch relevant information from knowledge bases to supplement the conversation context.
Attention mechanisms are a key innovation in modern AI models. They allow the model to dynamically focus on different parts of the input context when generating each part of the response. Instead of treating all context equally, attention weights determine which tokens are most relevant for the current processing step, enabling more coherent and contextually appropriate outputs.
Model Context Protocol has wide-ranging applications and benefits. It enables coherent multi-turn conversations where the AI remembers previous exchanges. This leads to personalized interactions, complex task completion, and knowledge retention across sessions. The result is a significantly improved user experience with more natural and contextually aware AI interactions.