Context engineering boosts LLM accuracy by structuring input to reduce hallucinations and improve privacy.
In November 2025, context engineering has emerged as a prominent AI development focusing on optimizing how background information is structured and fed into large language models (LLMs). Unlike prompt engineering that tailors user inputs, context engineering organizes extensive datasets, metadata, and relational information to create a coherent “worldview” for LLMs prior to query processing. Techniques include embedding hierarchies, knowledge graphs, and dynamic retrieval systems, all aimed at minimizing hallucinations—where AI outputs plausible but incorrect responses. This approach also ties into AI privacy efforts, influenced by prior data leak controversies, with companies like Google and research labs like DeepSeek advancing “nested learning” and unlearning protocols. This trend reflects global innovation hubs like the Bay Area and Beijing pushing AI towards more reliable, privacy-conscious applications amid ongoing debates about its sufficiency in fully resolving hallucination issues.
Source: ETC Journal