Context Engineering
Also: Context Engineering, Context design, Contexte (ingénierie du contexte), Kontext-Engineering, LLM context management, Context orchestration, Contextual grounding
The practice of assembling the right information, rules and tools into an AI model's working memory so it produces accurate, on-brand answers.
What It Is
Context Engineering is the discipline of deciding what information an AI model sees at the moment it answers. A large language model has no memory of your business by default. It only knows what you place in front of it: the question, plus any documents, data, rules and tools you supply alongside it. Context Engineering is the systematic work of choosing, formatting and sequencing that supporting material so the model gives reliable output. It goes beyond writing a clever prompt: it covers which company knowledge to retrieve, how much to include before hitting the model's limit, and in what order.
Why it matters
A CMO asking an AI assistant to draft campaign copy gets generic results if the model has no access to the brand guidelines, past performance data and current positioning. Feed it those, and the same model produces usable work. The quality of an AI system's answers depends more on the context it receives than on the raw power of the model itself. For leaders, this reframes the AI investment question. You are not only buying a smarter model, you are building the pipes that deliver your proprietary knowledge into it. This is where competitive advantage lives, because your data is the part competitors cannot copy.
How it works
In practice, a system retrieves relevant pieces of your knowledge base (product specs, policies, customer records), trims them to fit the model's context window, and injects them with clear instructions before the model responds. A well engineered context reduces hallucination, keeps answers current and enforces the tone and compliance rules your organization requires. A CFO evaluating an AI reporting tool should ask: where does it pull numbers from, how does it stay accurate as data changes, and what stops it from inventing figures. The answers reveal the quality of the context engineering underneath, which determines whether the tool is trustworthy at scale.
Frequently asked questions
What is context engineering?
Context engineering is the discipline of deciding what information an AI model sees at the moment it answers: the question itself, plus the documents, data, rules and tools supplied alongside it. A large language model knows nothing about your business by default, so this work consists of choosing, formatting and sequencing the supporting material. Done well, it determines whether the output is reliable or generic.
What is the difference between context engineering and prompt engineering?
Prompt engineering is about how you formulate the instruction; context engineering is about everything else the model receives alongside it. Context engineering covers which company knowledge to retrieve, how much to include before hitting the model's context limit, and in what order to present it. A clever prompt on an empty context still produces generic output.
Why should a non-technical executive care about context engineering?
Because the quality of an AI system's answers depends more on the context it receives than on the raw power of the model. That reframes the investment question: you are not only buying a smarter model, you are building the pipes that deliver your proprietary knowledge into it. Your data is the part competitors cannot copy, which is where the advantage sits.
How does context engineering reduce hallucination?
By grounding the model in retrieved material instead of letting it fill gaps from memory. A system pulls relevant pieces of your knowledge base (product specs, policies, customer records), trims them to fit the context window, and injects them with clear instructions before the model responds. The same mechanism keeps answers current as data changes and enforces tone and compliance rules.
What questions should I ask a vendor to judge the context engineering behind their AI tool?
Ask where the tool pulls its numbers or facts from, how it stays accurate as the underlying data changes, and what prevents it from inventing figures. These three answers expose the quality of the context engineering underneath and tell you whether the tool is trustworthy at scale. A CFO evaluating an AI reporting tool gets more from these questions than from any model benchmark.