CE

CE is the broader architectural discipline of dynamically assembling an LLM's inference context from multiple retrieved, historical, or real-time data sources—encompassing external document retrieval ^[67], agent memory ^[154], user profiles, tool outputs, and conversational history ^[154] (External Corpus, User Interaction Data). CE differs from PE in the structural locus of design: PE optimizes the content of a static input prompt, while CE designs the automated retrieval pipelines and orchestration systems that dynamically populate the model's context window at inference time. CE's External Corpus, User Interaction Data coordinate strictly distinguishes it from PE (Zero-Shot) by explicitly demanding complex, version-controlled retrieval and state-management infrastructure. Crucially, the live inference query serves solely as an execution trigger and data constraint, never as an on-the-fly parametric gradient signal (3tstOnly)—a strict architectural boundary that fundamentally separates CE from TTA. From a system engineering perspective, CE shifts the validation burden away from artifact-level model testing and toward complex state-machine verification. Because the model's operational envelope is dictated by dynamically retrieved external state, regression testing must rigorously validate the orchestration logic, context truncation algorithms, and data pipeline integrity rather than the static underlying model weights.