Context orchestration: what it is & how it works

Context orchestration is emerging as a critical component in AI systems, bridging the gap between the model's needs and the data it utilizes, particularly as traditional retrieval-augmented generation (RAG) methods become inadequate. It addresses runtime challenges by ensuring that large language models (LLMs) receive the most relevant data at the right time, thus reducing issues like hallucinations from stale data and inefficiencies in token usage. This involves dynamically assembling, retrieving, and delivering appropriate context using strategies like writing, selecting, compressing, and isolating data. The infrastructure supporting this process, known as the context engine, integrates various components such as vector search and semantic caching to maintain data freshness and relevance. Redis Iris, as a context engine, offers a comprehensive solution by combining these elements to enable efficient context orchestration, thus enhancing agent reliability and performance within AI stacks.

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