Context assembly — the process of stitching together system instructions, retrieved documents, conversation history, tool schemas, and memory into a single token sequence before an LLM call — has a major impact on response quality. Key factors include token ordering (models attend better to content at the start and end of the window), token budget management (tool schemas alone can consume tens of thousands of tokens), and data freshness (stale or conflicting inputs cause context poisoning, distraction, confusion, and clash). Research shows that burying relevant content in the middle of a long prompt can perform worse than not retrieving it at all, and effective context lengths are often far shorter than advertised. Teams managing these trade-offs increasingly treat assembly as a dedicated architectural layer rather than ad-hoc string concatenation, using frameworks like LangGraph and LlamaIndex for orchestration and low-latency stores for retrieval and memory. Redis Iris is presented as a unified platform for the data half of this problem, combining vector search, semantic caching, and agent memory at sub-millisecond latency.
