Context engineering is the observe of designing programs that decide what data an AI mannequin sees earlier than it generates a response to person enter. It goes past formatting prompts or crafting directions, as an alternative shaping the complete atmosphere the mannequin operates in: grounding knowledge, schemas, instruments, constraints, insurance policies, and the mechanisms that determine which items of data make it into the mannequin’s enter at any second. In utilized phrases, good context engineering means establishing a small set of high-signal tokens that enhance the probability of a high-quality final result.
Consider immediate engineering as a predecessor self-discipline to context engineering. Whereas immediate engineering focuses on wording, sequencing, and surface-level directions, context engineering extends the self-discipline into structure and orchestration. It treats the immediate as only one layer in a bigger system that selects, constructions, and delivers the proper data in the proper format in order that an LLM can plausibly accomplish its assigned process.
What does ‘context’ imply in AI?
In AI programs, context refers to all the things an a massive language mannequin (LLM) has entry to when producing a response — not simply the person’s newest question, however the full envelope of data, guidelines, reminiscence, and instruments that form how the mannequin interprets that question. The full quantity of data the system can course of directly known as the context window. The context consists of a variety of totally different layers that work collectively to information mannequin conduct:
