Context engineering is the practice of deciding what information an AI model has when it works: the instructions, facts, examples, tools and history in front of it. Prompt engineering is about wording one request. Context engineering is about everything around it. For a business, it means deciding what its AI knows.
Key takeaways
- Context engineering designs everything an AI sees at the moment it works, not just the prompt.
- Anthropic's engineering team calls context "a finite resource with diminishing marginal returns", so more is not always better.
- According to Atlassian's Teamwork Lab, only 12% of workers say their AI understands their organisation like a tenured senior colleague.
- For a business, the most important context is its own: product facts, customers, voice, methods and rules.
- Prompting is one person's skill; context is a shared asset the company can build once and maintain.
What is context engineering?
Context engineering is the work of curating the information an AI model can see when it produces an answer or does a task. That includes system instructions, retrieved documents, examples, tool results and conversation history. The goal is to give the model exactly what it needs to do the job well, and little else.
The term spread in mid-2025, as people building AI agents found that clever prompts were not the bottleneck. Anthropic's engineering team defined it in September 2025 as "the set of strategies for curating and maintaining the optimal set of tokens (information) during LLM inference". Tokens are the units of text a model reads.
Put simply, a model can only use what is in front of it. It does not know your customers, your pricing or last week's decision unless that information is supplied. Context engineering is the discipline of supplying it.
How is context engineering different from prompt engineering?
Prompt engineering is about how you phrase a single instruction. Context engineering covers everything else the model sees: the background facts, examples, tools, retrieved documents and memory. A prompt is one sentence in a briefing. Context engineering is preparing the whole briefing folder.
An analogy helps. Imagine handing a task to a capable contractor on their first day. Prompt engineering is choosing the words of your request: "write a proposal for this client". Context engineering is everything you hand them with it. That includes the client's background, your pricing rules, two past proposals that won, the objections this client raised and your house style.
The contractor's talent matters, but the folder decides whether the proposal is any good. The same is true of AI models. Modern models write well. What they lack is your business.
| Prompt engineering | Context engineering | |
|---|---|---|
| Focus | Wording of one request | Everything the model sees |
| Scope | One task, one person | Every task, every person |
| Lives in | A chat window | Shared files, tools and rules |
| Skill held by | The individual | The business |
Why does context matter so much?
Context matters because a model's output is bounded by its input. Given generic context, a model produces generic work. Given the right facts, examples and rules, it produces work that fits the business. Too much context also hurts, because models attend less reliably to information buried in a long input.
Two findings make the point. First, people feel the absence of business context. According to Atlassian's 2026 Pulse survey of 1,002 US knowledge workers, only 12% say their AI understands their organisation like a tenured senior colleague, while 45% say it is like a new junior employee, at best.
Second, more is not automatically better. Anthropic's engineers describe "context rot": as the context window fills, a model's ability to recall information from it declines. They conclude that context "must be treated as a finite resource with diminishing marginal returns". Their recommendation is to find "the smallest possible set of high-signal tokens" for the job.
That is why dumping every document into an AI rarely works. The skill is choosing.
What context does a business AI need?
A business AI needs five kinds of context: facts about the company and product, including which claims are approved; knowledge of the customer, in their own words; the methods for doing each job well; the voice it should write in; and the rules about what needs approval. Each one changes the output in a specific way.
- Facts. Product capabilities, pricing rules and approved claims, so the AI does not invent features.
- Customers. The ideal customer profile, common objections and verbatim customer language.
- Methods. How your best people do each job, written as steps an AI can follow.
- Voice. How the company, or a named person, sounds in writing.
- Rules. What data may be used, and what needs a person's approval before it goes out.
Kept together and maintained, these form a company brain: the shared context every AI in the business reads.
How do you engineer context without engineers?
Most of the work is editorial, not technical. Decide which facts matter, write them as short sourced statements, capture methods in plain steps, and keep them current. The technical part, connecting those files to the AI tools your team uses, is small by comparison and increasingly standardised.
Three habits do most of the work:
Distil, do not dump. Turn long documents and transcripts into short, canonical statements with a source for each. That respects the finite budget Anthropic describes.
Write methods as instructions. A method an AI can follow is a set of steps, checks and examples, not a policy document. Agent skills, folders of instructions an AI loads when relevant, package this neatly. The guide to Claude Skills explains how.
Maintain it. Business context changes weekly. Sweep new calls, tickets and emails for updates, and send contradictions to a person to settle.
There is also a people reason to treat context as shared. According to Microsoft and LinkedIn's 2024 Work Trend Index, only 39% of AI users have had AI training from their employer. If good results depend on each person engineering their own context, results will vary with skill. Shared context makes the best setup everyone's default.
What goes wrong with too much context?
Too much context dilutes the signal. The model may miss the fact that matters among pages that do not, pick up an outdated version instead of the current one, or slow down and cost more. Long, unsorted context also makes output harder to check, because nobody can tell what it relied on.
The common failure looks sensible at first: connect the AI to the whole shared drive and let it search. It works on easy questions. On hard ones, it finds three versions of the pricing, two old proposals and a Slack message, and blends them. The answer sounds right and is not.
The fix is curation: one canonical version of each fact, with its source and owner, kept current. Skein builds that curated context as the shared knowledge layer at the base of a Company OS. It suits growing teams where several people rely on AI daily. A solo operator can often manage context well enough with one carefully kept instructions file.
Frequently asked questions
What is context engineering in AI?
Context engineering is curating everything an AI model sees when it works: instructions, facts, examples, retrieved documents, tool results and history. Anthropic defines it as the strategies for curating and maintaining the optimal set of information during inference. The aim is to give the model exactly what the task needs.
What is the difference between context engineering and prompt engineering?
Prompt engineering focuses on the wording of a single request. Context engineering covers the whole information environment around it, including background facts, examples, tools and memory. In a business, prompting is an individual skill, while context is a shared asset that every person's AI can use.
Why is context engineering important?
A model can only use the information in front of it. Without business context, it produces generic or wrong work. With too much, it misses what matters. Good context engineering supplies the right facts, methods and rules, so output fits the business and can be trusted.
Is more context always better for AI?
No. Anthropic's engineers describe context rot, where recall declines as the context window fills, and recommend the smallest set of high-signal information for the task. Curated, current, canonical facts usually beat large volumes of unsorted documents, which can hold conflicting versions of the truth.
How does a business do context engineering?
Write the facts, customer insight, methods, voice and rules the AI needs as short, sourced files with owners. Connect them to the AI tools your team uses, and keep them current from calls, tickets and emails. Most of the work is editorial rather than technical.
Related reading
- Anthropic. "Effective context engineering for AI agents." Anthropic Engineering, 29 September 2025. https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents
- Atlassian Teamwork Lab. "Want more confidence in AI? Give it more context." Atlassian, 31 August 2026. https://www.atlassian.com/blog/ai-at-work/want-more-confidence-in-ai-give-it-more-context
- Microsoft and LinkedIn. "2024 Work Trend Index." Microsoft, 8 May 2024. https://blogs.microsoft.com/blog/2024/05/08/microsoft-and-linkedin-release-the-2024-work-trend-index-on-the-state-of-ai-at-work/