How to Prepare Context Before Starting a New AI Chat
Open a fresh chat, type "continue where we left off," and the model just guesses — it knows nothing about the project, the decisions, or the goal. Here's how to assemble a short context packet first, so a new chat starts aimed instead of blind.
Build Your Context Packet as a TemplateWhen a new chat starts from nothing
You open a new chat and type the thing you'd say to a colleague who was in the room yesterday: "continue from where we left off," or "can you help me improve this?," or "write the next task." But this chat wasn't in the room. It doesn't know the project, the goal, which decisions are settled, which file is the real one, or what you already ruled out. So it does the only thing it can — it guesses — and the next few messages are you patching the guess: "no, it's for existing users," "we already decided three screens," "don't touch the pricing." The work you skipped at the start comes back as correction in the middle.
That's the gap this guide closes. A fresh chat has no memory of you or your project; every conversation is a cold start. The fix is to spend a minute up front assembling an AI context packet — a short, ordered block that tells the model what it needs to know before it does anything: the goal, the current state, the decisions already made, the constraints, the source material, and what to leave alone. NewPrompt gives you the structure to build that packet and reuse it; it doesn't remember your context between chats, import your history, or check that the model understood — you paste the packet into your own AI tool each time, and you still read what comes back. A good packet doesn't guarantee the model gets it right. It makes the start far less of a guess.
Why a fresh chat can't infer what you didn't say
A new chat isn't withholding context; it genuinely has none. It can't see your files, your last session, or the decision you made in a meeting, so anything you don't put in front of it is invisible — and it fills the blanks with the most generic plausible answer. The deeper problem is that even when you do paste things in, the model can't always tell what kind of thing each one is:
- Background isn't the task. Paste three paragraphs about the project and the model may treat the description as the thing to act on, instead of the one-line task buried at the end.
- A decision isn't a suggestion. "We went with three tiers" is settled; without a label, the model reads it as an option still open for debate and helpfully proposes a fourth.
- Source material isn't permission to invent. Give it a doc to work from and, unless you say "use only this," it blends the doc with its own training and you can't tell which sentence came from where.
- A constraint isn't a preference. "Keep it to one screen" is a hard line; phrased casually, it lands as a nice-to-have the model trades away for something it thinks is better.
- Old context isn't current context. Paste the whole history and the contradicted early idea sits next to the final decision with equal weight — the model has no way to know which one you still stand behind.
Step 1: Sort what you paste into context vs. instruction
Before you write the packet, make the distinction the model can't: which of the things in your head is context (what it should know) and which is instruction (what it should do). The task is one line — "draft the copy for screen 2." Everything else — the project, the decisions, the constraints, the files — is context that shapes how it does that one thing. Keeping the two separate is what stops the model from acting on your background or ignoring your one real ask.
Source material deserves its own fence. When you hand the model a document, spec, or transcript to work from, mark where it starts and ends and say how far it may go — because a pasted doc with no boundary gets treated as both material and instruction at once. The Long Input Formatter does this deterministically in your browser: it wraps your material in explicit delimiters, labels its sections, and adds a grounding rule — up to a strict mode where anything the source doesn't say gets answered "the source does not say" rather than filled in. It packages the material and keeps it separate from your instructions; it doesn't read or verify the content, and running the result happens in your own AI tool. That single boundary is the difference between "use this doc" and "use this doc, and don't invent around it."
Step 2: Assemble the packet from a fixed set of fields
A context packet is short and always covers the same ground, so nothing load-bearing gets left out. A workable field set: goal (the one outcome), current state (where things stand), relevant background (only what bears on this task), decisions already made (settled, not up for debate), constraints and must-not-change, source material (and what to use it for), what to ignore, output requested, format, an assumptions rule (what to do when something's missing), open questions (explicitly undecided), and the next step you actually want. Not every field every time — but a fixed checklist means you leave things out on purpose, not by accident.
Because you'll build this for task after task, write it once as a reusable template. The Prompt Template Builder is where that lives: put each field in as a `{{goal}}`, `{{decisions}}`, `{{constraints}}` slot, and it hands you a fill-in version with a live preview and an export, so the next new chat starts from the same structure instead of a blank line. Be exact about what it does: it holds and fills the template and nothing more — it doesn't run the model or keep your context after you close the tab, and it can't tell you whether a field is right. A complete packet buys you a consistent, complete starting point every time; whether the content in it is correct is on you.
Step 3: Check it's complete, then cut what isn't load-bearing
Two failures sink a packet from opposite directions: a missing piece and a pile of noise. On the missing side, run a quick completeness pass before you send it — is there a decision the task depends on that you didn't write down, a file you referenced but didn't include, a constraint you're assuming the model knows? The Handoff Completeness Review Prompt is built for exactly this check: it reads a context or handoff package and reports what's missing against a checklist — decisions, files, assumptions, risks, open questions — and names each gap instead of inventing something to fill it. Read it for what it is: a human-readable review you run in your own assistant, not an automated gate — it doesn't validate anything or confirm the packet is right, it just surfaces the holes so you can fill them on purpose.
On the noise side, more context is not better context. Pasting the entire previous conversation, every old idea, and three tangents buries the five things that matter — and the contradicted early decision, sitting there with equal weight, actively misleads. A packet is the destination, not the road: carry the decisions and the current state, not the whole journey that produced them. If a line doesn't change how the model should do this task, it's weight, not context. Cut it.
Step 4: Ask the model to restate the task before it acts
The cheapest way to catch a misread is to make the model show its understanding before it commits to a full answer. End the packet with a line like: "Before you start, restate the task in one sentence, list the constraints you'll honor, and name any open question you see — then wait." If it restates the goal wrong or treats an open question as settled, you've caught it in one cheap message instead of after a long, confidently-wrong output.
This confirm-first move is baked into some ready-made packets. The Agent Task Template — a reusable task structure for autonomous agents, with objective, context, constraints, and success criteria as fields — opens execution by making the model confirm its understanding of the objective in one sentence, state its plan as steps, and flag any ambiguity that would block it, before it does the work. It's built for agent workflows rather than an ordinary chat, but that opening discipline transfers to any packet: a restatement you can check beats a result you have to unwind. The confirmation shows you how the model read the packet — not whether the packet was right, and not the model checking anything on your behalf.
Step 5: Read the first output against the packet
A context packet lowers the odds of a wrong start; it doesn't remove the review. When the first real output comes back, read it against the packet you wrote, not against a vague sense of whether it looks fine. Three checks catch most of what slips through: did it do the actual task, or drift into the background? Did it respect the settled decisions, or quietly reopen one — re-suggesting the option you rejected, renaming a thing, softening a constraint? And did it invent an answer to an open question you deliberately left open?
That last one is the subtle failure a packet is most likely to hide. You listed a question as undecided precisely so the model wouldn't guess — but a model that wants to be complete will often fill it in smoothly, and a filled-in blank reads exactly like a settled fact. Reading the output against your own open-questions line is how you catch it. The packet did its job by pointing the start; confirming the model actually understood, and didn't overwrite something you meant to protect, is a judgment that stays with you.
Common mistakes
The ways a new chat goes wrong before the first real answer:
- Opening with "continue where we left off." There is no shared memory; the new chat needs the state written down, not referenced.
- Burying the task in the background. Three paragraphs of context with the ask hidden at the end reads as "act on the paragraphs." Put the one-line task where it can't be missed.
- Leaving decisions unlabeled. A settled call that isn't marked settled is an open question to the model — and it'll reopen it.
- Pasting the whole history as context. The old, contradicted, and irrelevant travel with equal weight; extract the state, don't dump the transcript.
- Giving a source with no boundary. Without "use only this," the model blends your doc with its own guesses and you can't tell which is which.
- Assuming the packet was understood. A clean-looking start isn't a confirmed one; ask the model to restate before it commits, and read the output against what you wrote.
A worked example: resuming a project in a new chat
Take a blind start and give it the packet it was missing.
A blind "continue the work" vs. the same start with a context packet: the model resumes aimed instead of guessingBLIND START (a new chat, no context):
"Continue the onboarding work and write the copy for the next screen."
what the model can't know:
- which product, which onboarding, which screen
- what's already decided (screen count, what to skip)
- what must not change, and which file is the real one
CONTEXT PACKET (paste as the first message):
Project: Mobile app onboarding redesign (Acme, a habit tracker)
Goal: write the copy for screen 2 of 3 (the "pick a habit" screen)
Current state: flow is mapped; 3 screens agreed; screens 1 and 3 drafted
Decisions made: 3 screens only; email verification is skipped for now (settled)
Constraints: do not add screens; one action per screen; no jargon
Source: the flow doc below is the only source of truth -- use only this
Ignore: the old 5-screen draft; it was rejected
Output: a headline, one supporting line, and a button label
Assumptions: if a screen's purpose isn't in the flow doc, ask -- don't invent it
Open question: should screen 2 allow multiple habits? (undecided -- flag, don't assume)
Before you start: restate the task and the constraints, and name any open question.
WHAT THE PACKET BUYS YOU:
- it writes screen 2 only, inside the agreed 3-screen flow
- it doesn't re-propose email verification (a settled decision)
- it works from the flow doc or asks, instead of inventing screen 2's purpose
- it flags the multi-habit question instead of quietly deciding it
STILL YOUR JOB (the packet points; it doesn't verify):
- read the copy: did it honor "one action per screen" and drop the jargon?
- did it keep the multi-habit question open, or silently answer it?
- the packet aimed the start; the check is still yoursWhere this fits in NewPrompt
A starting packet is one move in a small family, and it helps to know which neighbor you actually need. If your context already lives in a previous chat that got long or slow, you're not writing a packet from scratch — you're carrying a session forward, and the Context Handoff Builder extracts the decisions, constraints, and open tasks from that conversation into a package to paste into the next one; that's the handoff case, a narrower job than starting fresh. If the same facts should hold across every chat you ever have about a project — the stack, the glossary, the never-assume rules — that's permanent project knowledge, a standing profile rather than a per-task packet. The packet in this guide is the one in between: the this-task, this-chat context you assemble at the start, whether or not a prior session exists.
Whichever you're building, the pieces are the same and so are their limits. The Prompt Template Builder holds the packet as a reusable template, the Long Input Formatter fences your source material, the Handoff Completeness Review Prompt names what's missing, and the Agent Task Template shows the confirm-before-acting habit. Every one of them structures what you write; none of them writes the model's answer, remembers your project, or confirms the context landed. Think of it like a first day: the model walks into every new chat as a stranger to your project, and the packet is the briefing you'd give anyone on their first morning — enough to start well, no substitute for you checking the work they hand back.