What surrounds a prompt now matters more than its wording: the model answers from everything it can see, so manage that deliberately.
- Context is everything the model sees. Your instruction, the material you supply, the conversation history and the tools; change any one and the answer changes.
- Placement matters, not just volume. Do not bury what must not be missed in the middle of a long context; for long documents, Anthropic advises putting them above the question. Irrelevant material competes with what matters.
- Supply the source. A model working from a document you gave it has far less room to invent than one working from training.
- Memory can be tampered with. Anything a system keeps between sessions can be edited between sessions, so check stored state before acting on it.
The category has moved. Prompting is now the small part of the job; what surrounds the prompt is the large part. This page is about that.
A 2026 industry guide puts it plainly: prompt engineering as a standalone discipline is shrinking, because frontier models are good enough that a decent prompt works most of the time. What moves the needle now is context — the documents, memory, tools and history surrounding the request.Taskade, AI Prompt Generator guide, 2026 — an industry source, not a study; read at source 23 Sep 2026: “prompt engineering as a standalone discipline is shrinking. Frontier models are good enough that a decent prompt works most of the time.”
A 2024 workflow was: write a good prompt, paste it in. A 2026 workflow is: assemble the context, attach the tools, then ask.
What context actually is
Everything the model can see when it answers. Four things, and most people only manage the first:
- The instruction — what you typed. The part everyone optimises.
- The material — documents, files, pasted text, retrieved passages.
- The history — what was said earlier in this conversation, and on some systems, earlier sessions.
- The tools — what the model is permitted to call, and what those calls returned.
A model produces the most probable continuation given all four. Change any of them and you change the answer — which is why the same prompt gives different results in different places.
Four rules that hold
Position matters, not just volume
Where material sits in a long context changes the answer. A working rule from practice, not a measurement on this page: if something must not be missed, do not bury it in the middle of a long document.
The practical version, for long inputs, follows Anthropic’s guidance: put the documents at the top, the question and instructions below them, and restate the key constraint at the end.Anthropic, Prompting best practices, long-context section (20k+ tokens), read at source 17 Sep 2026: “Place your long documents and inputs near the top of your prompt, above your query, instructions, and examples.” Other vendors order prompts differently; follow the one you are prompting. Corrected 17 Sep 2026: this line said to state the task at the top and put the reference material below it, the reverse of Anthropic’s advice.
More context is not better context
Filling a window with everything available is an easy mistake now that windows are large. Irrelevant material does not sit inertly — it competes.
The test: could a competent stranger do this task from exactly what you supplied, and nothing else? If yes, stop adding.
Retrieved is stronger than remembered
A model working from a document you supplied has far less room to invent than one working from training. This is the same mechanism described in why AI makes things up: rare facts have a floor, and supplying the fact removes the floor.
Pasting the source is worth more than any phrasing improvement.
Memory is an attack surface
Anything the system remembers between sessions can be edited between sessions. A 2026 study of a widely deployed personal agent found that poisoning its persistent state raised average attack success from 24.6% to between 64% and 74%.Wang et al., "Your Agent, Their Asset", arXiv 2604.04759, read at source 9 Sep 2026: “poisoning any single CIK dimension increases the average attack success rate from 24.6% to 64-74%”
The rule that follows: never let fetched content become instruction. See guardrails.
What this changes about how you work
- Stop collecting prompts. Start collecting context. A reusable brief with your constraints, your examples and your standards outlives every phrasing trick.
- Front-load the specifics only you have. The model fills every gap with an average; your job is to leave fewer gaps.
- Treat the window as a budget. Everything you add costs attention that something else needed.
- Assume anything remembered can be wrong or hostile. Verify state before acting on it.
Retrieval stacks, memory layers and context platforms turn over constantly. The four rules above are properties of how attention works, not features of a product, which is why they will still hold when this year's tooling is gone.
This page teaches you what to do. When not to use AI covers the other side — where these tools are worse than doing it yourself, and how to tell in advance.