Summary
Smart context management for LLM applications
Description
Context Engine is a prototype system for intelligently managing context windows in large language model applications.
As LLM applications grow more complex, managing what information gets included in prompts becomes critical. Context Engine automatically prioritizes and structures information based on relevance, recency, and importance.
The system uses a combination of embeddings, graph relationships, and custom heuristics to determine what context is most valuable for a given query. Early experiments show up to 40% better response quality compared to naive "dump everything" approaches.
Key questions we're exploring: How do we measure context relevance? When should we summarize versus include full text? How do we handle contradictory information from different sources?
The project is in active exploration phase as we test different approaches and gather feedback from real-world use cases.