Context assembly: building the prompt the model actually sees

Context assembly, a crucial process in the deployment of large language models (LLMs), involves the integration of various inputs such as system instructions, retrieved documents, conversation history, tool schemas, and stored memories into a cohesive token sequence that the model processes. This practice, known as context engineering, significantly influences the model's output quality, as it determines what information the model accesses before generating a response. The text highlights the importance of strategic ordering and token budgeting in context assembly, as LLMs show a U-shaped performance curve where initial and final tokens are weighted more heavily than those in the middle. It underscores the challenges of managing tool definitions and retrieval-augmented generation (RAG) in token-limited windows, which can impact response quality due to stale or misplaced inputs. Solutions like Redis Iris offer an efficient assembly layer, enhancing retrieval, memory, and caching processes to maintain up-to-date agent data and improve LLM performance, as evidenced by significant latency reductions and cost savings in high-repetition scenarios.

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