How to Prompt Claude Opus 4.7 Differently Than 4.6

The One Behavioral Shift That Changes Everything

If your prompts worked well with Claude Opus 4.6 but feel slightly off with 4.7, you’re not imagining it. Something real changed — and it’s not about capability. It’s about how the model interprets what you say.

Claude Opus 4.7 follows instructions more literally than its predecessor. Where 4.6 would often infer what you probably meant and fill in reasonable gaps, 4.7 tends to do exactly what you asked — no more, no less. That sounds like a minor tuning decision. In practice, it rewrites how you need to prompt across every surface you use: the chat interface, Claude Code, the API, and co-work projects.

This guide breaks down four distinct prompting playbooks — one for each context — so you get reliable, useful output from 4.7 instead of wondering why it’s suddenly being so… precise.

Before getting into the playbooks, it helps to understand why 4.7 behaves this way. If you want the full model comparison, here’s what actually changed between Opus 4.6 and 4.7.

Why 4.7 Stopped Inferring Intent

Opus 4.6 had strong “intent inference” behavior. Ask it to “clean up this email” and it would rewrite the whole thing, adjust the tone, tighten the structure, and maybe flag a factual inconsistency it noticed. You got more than you asked for, and often that was exactly right.

Opus 4.7 draws a tighter boundary around the instruction as stated. “Clean up this email” now means fix the grammar and formatting — and that’s it. It won’t reorganize your argument unless you ask it to. It won’t adjust tone unless you specify a target tone. It won’t volunteer observations unless you invite them.

This isn’t a regression. It’s a deliberate shift toward more predictable, controllable behavior. For agentic workflows and API integrations, that’s genuinely better. You want a model that does what the system prompt says, not one that improvises. But if you’re used to 4.6 filling in the blanks, 4.7 will feel terse — even unhelpful — until you adjust your prompting style.

The fix is straightforward: you need to be more explicit about scope, format, and what “done” looks like.

Playbook 1: Prompting 4.7 in Chat

State your scope, not just your task

With 4.6, you could say “improve this proposal” and get a comprehensive rewrite. With 4.7, that same prompt will likely return minor edits to word choice. The model interprets “improve” conservatively unless you tell it otherwise.

Instead, define the scope in the prompt itself:

4.6 style: “Improve this proposal.”

4.7 style: “Rewrite this proposal from scratch. Tighten the argument structure, cut anything that doesn’t directly support the main recommendation, and improve the opening paragraph so it hooks the reader in the first sentence. Keep the same overall recommendations but improve how they’re presented.”

Yes, that’s more words. But it’s not padding — it’s precision. Each instruction clause does work.

Tell it what you don’t want

4.7 takes negative constraints seriously. Where 4.6 might ignore a “keep this brief” instruction if the topic seemed to warrant more, 4.7 will respect it. Use this to your advantage:

“Don’t add new sections.”
“Don’t change the tone — just fix the grammar.”
“Don’t explain what you changed. Just show me the revised version.”

Ask it to ask you questions first

If you’re working through something complex and you’re not sure what scope to specify, prompt 4.7 to clarify before it acts:

“Before you start, ask me any clarifying questions you need to produce the best output. Don’t assume. Once I’ve answered, proceed.”

4.6 often skipped clarifying questions and made reasonable assumptions. 4.7 will follow this kind of instruction reliably.

Invite the observations you want

4.6 volunteered insights. 4.7 doesn’t, unless you specifically ask for them. If you want the model to flag potential problems, inconsistencies, or things you might have missed, say so explicitly:

“After completing the task, tell me if you noticed anything I should be aware of that I didn’t ask about.”

That single line brings back the “proactive assistant” behavior that 4.6 had by default.

Playbook 2: Prompting 4.7 in Claude Code

Claude Code with Opus 4.7 is where the literal-instruction shift has the biggest practical impact. In a coding context, doing exactly what you asked — and nothing more — can either be a precision superpower or a frustrating limitation, depending on how you write your prompts.

Use the CLAUDE.md file to set standing context

With 4.7, the claude.md file is more important than ever. Since the model won’t infer preferences it hasn’t been told about, your permanent instruction file for Claude Code becomes the place to encode your defaults:

Preferred language version (e.g., TypeScript strict mode)
File structure conventions
When to run tests vs. just write them
Whether to refactor adjacent code or leave it alone
How to handle uncertainty (stop and ask, or add a TODO comment)

Everything you had to repeat to 4.6 mid-session, 4.7 will follow from the start — as long as you’ve written it down.

Describe the outcome, not just the action

4.6 could often infer what a “completed” feature looked like. 4.7 needs you to state it. A prompt like “add authentication” is underspecified. 4.7 might add a basic auth check and call it done. Be explicit:

“Add authentication to the /api/profile endpoint. This means: (1) verify the JWT in the Authorization header, (2) return 401 if the token is missing or invalid, (3) attach the decoded user object to the request context, (4) add a test that covers the 401 case and a test that covers successful auth. Don’t touch any other endpoints.”

That last sentence — “don’t touch any other endpoints” — is the kind of scope fence that 4.6 assumed, but 4.7 needs stated.

Use effort levels intentionally

Claude Code’s effort level settings interact directly with how literally 4.7 interprets a task. At low effort, the model will do the minimum to satisfy the instruction as written. At high or max effort, it applies more reasoning before acting — which gives you more of the “fill in reasonable gaps” behavior you got from 4.6 by default.

For quick targeted fixes: low effort, highly specific prompt. For open-ended or complex tasks: high effort, and still be explicit about scope.

The Opus plan mode workaround

If token costs are a concern, Opus plan mode lets you use Opus 4.7’s reasoning for the planning phase and route execution to Sonnet or Haiku. The benefit here isn’t just cost — it’s that a separate planning pass gives you a chance to review and correct the scope interpretation before any code gets written.

This is particularly useful for larger refactors where 4.7’s literal interpretation could otherwise cause it to miss the forest for the trees.

Playbook 3: Prompting 4.7 via the API

If you’re integrating Opus 4.7 directly into a product or pipeline, the literal-instruction behavior is actually what you want. The challenge is writing system prompts that specify everything you used to rely on the model to infer.

Rewrite your system prompts from scratch

Don’t just copy your 4.6 system prompts into a 4.7 integration and expect the same behavior. 4.6 system prompts often had implicit gaps that the model bridged through inference. 4.7 won’t bridge them.

Audit your existing system prompts for:

Undefined output format: Does your prompt say what format to use, or did you rely on the model to pick something reasonable? With 4.7, specify it.
Ambiguous scope signals: “Be helpful and thorough” doesn’t mean much to 4.7. “Always answer the direct question first, then offer relevant context only if it materially changes the answer” does.
Missing fallback behavior: What should 4.7 do if the user’s request is outside the intended scope? Define it explicitly. Otherwise, 4.7 will follow whatever instruction is closest to what it was asked.

Use the system prompt to define what the model should and shouldn’t volunteer

4.6 was proactive with related information. 4.7 isn’t. In a customer-facing product, that can make responses feel thin or incomplete. Fix it at the system prompt level:

“After answering the user’s question, always check: is there a closely related fact that would change how they interpret this answer? If yes, include it as a brief follow-up note. If no, don’t add anything.”

This is essentially teaching 4.7 to mimic 4.6’s proactive behavior, but with you controlling the conditions under which it applies.

Be precise about response length

4.7 is more likely to give you short, literal answers to short, literal questions. If your product needs consistent response depth, specify minimum and maximum length in the system prompt — not as vague guidance (“be thorough”) but as concrete instruction (“responses should be 3–5 sentences unless the question genuinely requires more or less”).

If you want a deeper look at the underlying tradeoffs in API prompt design, the prompt engineering vs context engineering distinction is worth reading. With 4.7, context engineering — managing what’s in the prompt rather than how you phrase the ask — becomes substantially more important.

Playbook 4: Prompting 4.7 in Co-work Sessions

Claude Co-work projects are designed for ongoing, context-rich collaboration — the kind where you’re working through something over multiple sessions, building on earlier decisions. The behavioral shift in 4.7 shows up here differently than in chat or code contexts.

Treat each session instruction as a full brief

In 4.6, you could often start a co-work session with a short reference to an earlier conversation and expect the model to extrapolate. “Keep going with the strategy we discussed” would work because 4.6 would reconstruct the context and make reasonable assumptions about direction.

4.7 won’t do this reliably. It will operate on the literal content available to it. If you want continuity, you need to provide it explicitly — either by restating the key context or by using a structured project brief that stays pinned to the session.

Pin your project context as a document, not a conversation thread

Instead of relying on the conversation history to carry context, write a short project brief and reference it explicitly:

“We’re working on [project name]. The current goal is [specific objective]. Decisions already made: [list]. Decisions still open: [list]. Today’s task: [specific task].”

This sounds tedious, but it takes two minutes to maintain and it means 4.7 knows exactly where to start rather than inferring from conversation history.

Separate “thinking with me” from “executing for me”

One underused pattern in co-work sessions is the explicit mode shift. 4.7 responds well to being told which mode it’s in:

“Right now I want you to think with me — ask questions, push back, offer alternative framings. Don’t produce final output yet.”

Then later:

“Okay, now execute. Take everything we just discussed and produce the deliverable. No more questions.”

With 4.6, the model often read the room and switched modes without being told. With 4.7, you get better results by stating the mode directly. This is also consistent with the Anthropic advisor strategy — using Opus as a deliberate thinking layer, separate from execution.

A Note on the Bitter Lesson

There’s a tempting response to all of this: just write longer, more detailed prompts for everything. But that’s not quite right either.

The research on LLM prompting consistently shows that prompt length and prompt quality aren’t the same thing. Stuffing more words into a prompt doesn’t compensate for a poorly structured instruction. With 4.7, the goal is precision — not verbosity.

The difference between a good 4.7 prompt and a bad one isn’t length. It’s specificity. A short, precise prompt outperforms a long, vague one every time. What you’re doing when you “write more” for 4.7 is closing the inferential gaps — not adding padding.

How Remy Sidesteps Most of This

One of the reasons spec-driven development is compelling right now is that it naturally handles the “write it down explicitly” requirement that 4.7 enforces.

When you build with Remy, you’re writing a spec — a structured document that captures what the app does, what the edge cases are, and what the rules are. That spec is the source of truth. The underlying model (currently Claude Opus for core reasoning) works from that explicit document rather than inferring intent from a casual prompt.

In other words, Remy’s spec format already gives you the kind of precision that 4.7 now expects. You’re not trying to “write the right prompt.” You’re building a structured description of the thing you want, and the compiled output follows from that.

If you’ve been frustrated by prompt drift — where the model interprets your intent slightly differently each time — this is worth looking at. The spec stays constant. The model reads it the same way every time.

Try it at mindstudio.ai/remy.

Frequently Asked Questions

Does Claude Opus 4.7 require completely different prompts than 4.6?

Not completely, but meaningfully different. The core difference is that 4.7 follows instructions more literally and fills in fewer gaps on its own. You don’t need to rewrite everything — but you do need to be more explicit about scope, output format, and any context you want the model to use. Prompts that worked reliably with 4.6 and were somewhat vague should be revised before using them with 4.7.

Why does Claude Opus 4.7 give shorter, more literal answers than 4.6?

This is an intentional behavioral shift. Anthropic tuned 4.7 for more predictable, instruction-precise behavior — which is particularly valuable in agentic and API contexts where you don’t want the model improvising. The tradeoff is that the model is less likely to volunteer information or expand on a task unless you explicitly ask it to. The fix is to state your desired scope and invite any additional context you want.

Should I rewrite my Claude Code CLAUDE.md file for 4.7?

Yes, and it’s worth doing carefully. With 4.6, the CLAUDE.md file was useful but the model would often infer sensible defaults if your instructions had gaps. With 4.7, your instruction file for Claude Code becomes the primary way to encode standing preferences and conventions. Audit it for anything you previously relied on the model to figure out, and write those things down explicitly.

Does literal instruction-following make 4.7 better or worse for agentic coding?

Better, for most production use cases. When a model infers intent and gets it wrong in an agentic loop, the error compounds. A model that does exactly what you specified — and stops when it hits ambiguity — is much easier to debug and control. The Claude Opus 4.7 review for developers covers this in more detail, but the short version is: 4.7’s literal-instruction behavior is a feature for structured workflows, not a limitation.

How do I get Claude Opus 4.7 to be more proactive again?

Add an explicit instruction to your prompt or system prompt that tells the model to volunteer relevant information. Something like: “After completing the task, note anything you observed that might affect how I use this output — even if I didn’t ask about it.” This recreates the proactive behavior from 4.6 on demand, without the unpredictability of having the model decide when to volunteer things.

Is it worth upgrading to Opus 4.7 if my 4.6 prompts are working fine?

It depends on your use case. If you’re doing agentic work, API integrations, or anything where predictable behavior matters, 4.7 is worth it. If you’re using Claude primarily for creative writing or open-ended chat where 4.6’s inferential behavior was a feature, the upgrade is less urgent. Check out what changed between 4.6 and 4.7 before deciding, especially if a migration is involved.

Key Takeaways

Claude Opus 4.7 follows instructions more literally than 4.6 and fills in fewer inferential gaps.
In chat: define scope explicitly, state what you don’t want, and invite observations rather than expecting them.
In Claude Code: use CLAUDE.md to encode standing preferences, describe the completed outcome (not just the action), and use effort levels to control behavior.
In the API: rewrite system prompts to close inferential gaps, specify response format and length explicitly, and define fallback behavior.
In co-work sessions: pin your project context as a document, brief the model at the start of each session, and explicitly signal which mode you’re in (thinking vs. executing).
Precision beats verbosity. The goal isn’t longer prompts — it’s prompts with fewer ambiguous gaps.

If you want a workflow where explicit, structured intent is already built into the format, try Remy. The spec approach means you’re describing what you want precisely by default — which is exactly what 4.7 rewards.