GPT-5.1 Prompting Guide: How To Get Better Results in 2025

Most people opened GPT-5.1, typed the same prompts they've been using for months, and wondered what changed.

Here's the problem: GPT-5.1 isn't just a faster version of GPT-5.

It thinks differently about easy versus hard tasks, responds to instructions with more precision, and introduces tools that fundamentally change how you should write prompts.

Use it like you used GPT-4 or even GPT-5, and you're missing the point entirely.

This guide shows you exactly what works.

You'll learn how to control tone and verbosity, when to use different reasoning modes, how to stop GPT-5.1 from quitting early, and the prompt patterns that produce reliable results in production systems.

Everything here comes from OpenAI's internal testing and real deployments with companies building AI agents and coding tools.

ALSO READ: 20 Rules Nobody Tells You About Vibe Coding (But Should)

What Makes GPT-5.1 Different (And Why Your Old Prompts Don't Work)

GPT-5.1 brings three major changes that affect how you should write prompts.

1. Adaptive reasoning

GPT-5.1 automatically adjusts how much "thinking time" it uses based on your question's difficulty.

Ask something simple like "What's 2+2?" and it answers instantly.

Give it a complex coding problem, and it takes the time it needs.

This means you don't need to tell it "think carefully" for hard questions or "be quick" for easy ones.

It figures that out on its own.

2. Better instruction following

GPT-5.1 pays extremely close attention to what you ask for. If you say "respond in 2 sentences," it actually does that.

If you specify a tone or format, it matches it precisely.

This is great when your instructions are clear. It's terrible when they're contradictory.

3. The "none" reasoning mode

This new mode turns off reasoning tokens completely, making GPT-5.1 behave like GPT-4.1 for low-latency tasks.

You still get access to tools like web search and file operations, but responses come faster.

The key difference from GPT-5's old "minimal" mode: GPT-5.1 with "none" actually works well with function calling and custom tools.

Why This Matters For Your Prompts

If you're coming from GPT-5, you'll notice GPT-5.1 sometimes gives shorter answers.

It's trying to be efficient, but that can feel incomplete.

If you're coming from GPT-4.1, GPT-5.1's "none" mode should feel familiar, but you now have access to reasoning modes when you need deeper thinking.

The bottom line: Your prompts need to be more explicit about what "complete" means for your use case.

Understanding GPT-5.1's Two Modes

GPT-5.1 comes with two response types that work differently.

Instant Mode (When Speed Matters)

Instant mode prioritizes speed and conversation flow. It's built for:

• Quick questions with obvious answers
• Brainstorming sessions
• Casual chat
• Summarizing articles
• First drafts of content

When to use Instant:

You're iterating fast and need responses that keep pace with your thinking.

The quality is still high, but the model doesn't spend extra time on complex reasoning.

Common mistake:

Using Instant mode for math problems, logic puzzles, or multi-step planning.

It'll give you an answer, but it might skip steps or miss edge cases.

Thinking Mode (When Accuracy Matters)

Thinking mode is slower but more thorough. It allocates more reasoning time for:

• Complex coding problems
• Mathematical reasoning
• Multi-step planning
• Logic puzzles
• Detailed analysis

When to use Thinking:

You need the answer to be right more than you need it to be fast.

The model will take longer, but the reasoning quality goes up significantly.

How to choose:

Ask yourself: "If this answer is wrong, does it matter?" If yes, use Thinking.

If you're just exploring ideas or drafting content, Instant works fine.

The Biggest Mistake: Letting GPT-5.1 Quit Early

Here's the most common complaint about GPT-5.1: it stops before finishing the job.

You ask it to build a feature.

It gives you 80% of the solution and waits for you to ask for the rest.

You want a complete analysis.

It provides highlights and assumes you'll follow up with questions.

This happens because GPT-5.1 is calibrated to be efficient with reasoning tokens.

It stops when it thinks you have enough to continue on your own.

The Fix: Solution Persistence

Tell GPT-5.1 explicitly that you want complete solutions.

Here's the prompt structure that works:

<solution_persistence>
Treat yourself as an autonomous senior developer. Once given a direction, proactively gather context, plan, implement, test, and refine without waiting for additional prompts at each step.
Persist until the task is fully handled end-to-end within the current turn whenever feasible. Do not stop at analysis or partial fixes. Carry changes through implementation, verification, and a clear explanation of outcomes.
Be extremely biased for action. If a directive is somewhat ambiguous, assume you should go ahead and make the change. If you answer "yes" to a question like "should we do x?", also go ahead and perform the action. It's very bad to leave the user hanging.
</solution_persistence>


This prompt section forces GPT-5.1 to complete tasks instead of bouncing decisions back to you.

Real Example

Without persistence prompting:

User: "Add authentication to this API"

GPT-5.1: "To add authentication, you'll need to:

1. Install passport.js
2. Set up user sessions
3. Create login/logout routes
4. Add middleware to protected routes

Would you like me to implement these changes?"

With persistence prompting:

User: "Add authentication to this API"

GPT-5.1: [Installs packages, creates auth routes, adds middleware, updates existing endpoints, tests the implementation, provides configuration instructions]

"Authentication is now fully implemented. I've added JWT-based authentication with login, logout, and protected routes.

All existing API endpoints now require authentication except /login and /register."

The difference is massive in real workflows.

How To Control GPT-5.1's Personality and Tone

GPT-5.1 is highly steerable, meaning you can shape how it communicates based on your use case.

This matters most for customer-facing agents that need to display emotional intelligence across different situations.

Why Personality Matters

A support bot needs to sound warm when a customer is frustrated, but efficient when they're in a rush. A coding assistant should be direct and skip pleasantries when you're debugging at 2 AM.

GPT-5.1 can adapt its personality, but only if you define what you want clearly.

Defining Your Agent's Character

The most effective approach is to define a clear persona. Here's the framework that works:

1. Core communication style

Is your agent formal or casual? Direct or explanatory? Brief or detailed?

2. Adaptive elements

When should tone shift based on user behavior? Should it match the user's energy level?

3. What to avoid

What phrases or patterns feel wrong for your use case?

Example: Customer Support Agent

Here's how OpenAI shaped personality for a support agent:

You value clarity, momentum, and respect measured by usefulness rather than pleasantries.

Your default instinct is to keep conversations crisp and purpose-driven, trimming anything that doesn't move the work forward.

Adaptive politeness:
- When a user is warm, detailed, or says 'thank you', offer a single, succinct acknowledgment like 'Got it' or 'You're welcome'—then shift immediately back to productive action.
- When stakes are high (deadlines, compliance issues, urgent logistics), move straight into solving without acknowledgment.

Core inclination:
- You speak with grounded directness. The most respectful thing you can offer is efficiency: solving the problem cleanly without excess chatter.

Relationship to acknowledgment tokens:- Treat acknowledgments as optional seasoning, not the meal. If the user is brisk, match that rhythm with near-zero acknowledgments.
- Never repeat acknowledgments. Once you've signaled understanding, pivot fully to the task.


This creates an agent that adapts to the user's tone while maintaining a consistent personality: helpful, efficient, and action-oriented.

Example: Coding Assistant

For a coding assistant, you want a different personality:

You are a senior pair-programmer focused on getting code working quickly.

Keep responses compact:
- Tiny changes (≤10 lines): 2-5 sentences, 0-1 short snippet
- Medium changes: ≤6 bullets, 1-2 snippets (≤8 lines each)
- Large changes: Summarize per file, avoid full code blocks

Skip process narration unless it blocks the change. If builds and tests succeed silently, don't mention them.

Never include "before/after" code comparisons or full method bodies. Reference file and function names instead.


This creates an assistant that respects your time and doesn't drown you in code blocks.

Making GPT-5.1 Talk Like You Want

Beyond personality, you need control over verbosity and formatting.

Controlling Verbosity

GPT-5.1 has a dedicated verbosity parameter, but you can also control it through prompting.

The parameter works globally, but sometimes you want different verbosity levels for different parts of the response.

For example: short explanations but detailed code snippets.

Prompt for consistent brevity:

<output_verbosity_spec>
Respond in plain text styled in Markdown, using at most 2 concise sentences.
Lead with what you did (or found) and context only if needed.
For code, reference file paths and show code blocks only if necessary to clarify the change.
</output_verbosity_spec>


Prompt for adaptive detail:

<verbosity_rules>
Simple questions: 2-4 sentences maximum
Complex analysis: Use sections with headers, but keep each section under 6 bullets
Code changes: Show only the changed parts, not entire files
</verbosity_rules>


The key is being specific about what "brief" or "detailed" means in your context.

Setting Output Format

GPT-5.1 follows formatting instructions precisely. You can specify:

• Markdown structure
• Code snippet limits
• When to use bullets vs paragraphs
• Whether to include examples

Example for coding output:

<final_answer_formatting>
Final answer compactness rules (enforced):
- Tiny single-file change (≤10 lines): 2-5 sentences or ≤3 bullets. No headings. 0-1 short snippet (≤3 lines) only if essential.
- Medium change (single area or few files): ≤6 bullets or 6-10 sentences. At most 1-2 short snippets total (≤8 lines each).
- Large multi-file change: Summarize per file with 1-2 bullets. Avoid inlining code unless critical (still ≤2 short snippets total).

Never include before/after pairs, full method bodies, or large scrolling code blocks. Prefer referencing file/symbol names instead.

No build/lint/test logs unless requested or blocking.
</final_answer_formatting>


This prevents GPT-5.1 from dumping entire files into responses and keeps output scannable.

Design System Enforcement (For Frontend Work)

If you're building user interfaces, you can constrain GPT-5.1 to match your design system.

<design_system_enforcement>
Tokens-first: Do not hard-code colors (hex/hsl/rgb) in JSX/CSS. All colors must come from globals.css variables (e.g., --background, --foreground, --primary, --accent).

Introducing a brand color? Add tokens in globals.css under :root and .dark first:
- --brand, --brand-foreground, --brand-muted

Then use Tailwind utilities wired to tokens:
- bg-[hsl(var(--primary))]
- text-[hsl(var(--foreground))]

Default to the system's neutral palette unless the user explicitly requests a brand look.
</design_system_enforcement>


This keeps generated code consistent with your existing styles.

User Updates: Keeping You In The Loop

User updates (also called preambles) are how GPT-5.1 keeps you informed during long tasks.

When GPT-5.1 is running multiple tool calls or working through a complex problem, it can send progress updates as it goes. This helps you understand what it's doing and catch issues early.

Setting Update Frequency

You can control how often GPT-5.1 sends updates:

<user_updates_spec>
Send short updates (1-2 sentences) every few tool calls when there are meaningful changes.

Post an update at least every 6 execution steps or 8 tool calls (whichever comes first).

If you expect a longer heads-down stretch, post a brief heads-down note with why and when you'll report back. When you resume, summarize what you learned.

Only the initial plan, plan updates, and final recap can be longer with multiple bullets.
</user_updates_spec>


What To Include In Updates

Good updates contain:

1. What changed since the last update

Not just what you're about to do, but what you actually accomplished.

2. Meaningful discoveries

If you found something important while exploring, share it.

3. Brief context

Enough detail so the user understands progress without drowning in minutiae.

Bad update:

"Checking files..."

Good update:

"Found the authentication logic in auth.js. The token validation is using an outdated method. Updating it now."

Avoiding Update Spam

The worst updates say nothing useful:

• "Working on it..."
• "Almost done..."
• "Checking one more thing..."

These waste attention. Only send updates when you have something concrete to report.

Getting Immediate First Updates

For better perceived speed, you can prompt GPT-5.1 to send an update before it starts thinking:

<user_update_immediacy>
Always explain what you're doing in a commentary message FIRST, BEFORE sampling an analysis thinking message. This is critical to communicate immediately to the user.
</user_update_immediacy>


This makes long tasks feel more responsive because the user sees activity right away.

The "None" Reasoning Mode (Your Speed Weapon)

GPT-5.1's "none" reasoning mode is different from anything in GPT-5.

What "None" Mode Actually Does

When you set reasoning_effort: "none", GPT-5.1 never uses reasoning tokens. It responds like GPT-4.1 or other non-reasoning models.

The key difference from GPT-5's old "minimal" mode: GPT-5.1's "none" mode has much better function-calling performance and works with hosted tools like web search and file operations.

When To Use It

Perfect for "none" mode:

• Low-latency chatbots
• Simple Q&A systems
• Quick code edits
• Summarization tasks
• Standard CRUD operations

Not good for "none" mode:

• Complex reasoning problems
• Multi-step planning
• Mathematical proofs
• Deep debugging

Getting Good Results Without Reasoning Tokens

Even without reasoning tokens, you can improve GPT-5.1's performance with good prompting:

Encourage explicit planning:

You MUST plan extensively before each function call, and reflect extensively on the outcomes of previous function calls, ensuring the user's query is completely resolved.

DO NOT rely solely on function calls to solve problems. Think through your approach first.


Ask for verification:

When selecting a solution, verify it meets all user constraints. Quote back the key details for confirmation before executing.


Emphasize persistence:

Remember, you are an agent. Keep going until the user's query is completely resolved before ending your turn. Be prepared to answer multiple queries and only finish once the user confirms they're done.


These prompts push GPT-5.1 to be more thorough even when reasoning is disabled.

Tool Calling That Actually Works

GPT-5.1 is excellent at using tools, but only if you set them up correctly.

Writing Better Tool Descriptions

The best tool descriptions split information between two places:

1. The tool definition — What the tool does functionally

2. The prompt — When and how to use it

Here's an example for a restaurant reservation tool:

Tool definition:

{
 "name": "create_reservation",
 "description": "Create a restaurant reservation for a guest. Use when the user asks to book a table with a given name and time.",
 "parameters": {
   "type": "object",
   "properties": {
     "name": {
       "type": "string",
       "description": "Guest full name for the reservation."
     },
     "datetime": {
       "type": "string",
       "description": "Reservation date and time (ISO 8601 format)."
     }
   },
   "required": ["name", "datetime"]
 }
}


In the prompt:

<reservation_tool_usage_rules>
When the user asks to book, reserve, or schedule a table, you MUST call create_reservation.

Do NOT guess a reservation time or name. Ask for whichever detail is missing.

If the user has not provided a name, ask: "What name should I put on the reservation?"

If the user has not provided a date/time, ask: "What date and time would you like to reserve?"

After calling the tool, confirm naturally: "Your reservation is confirmed for [name] on [date/time]."
</reservation_tool_usage_rules>


This separation keeps the tool definition focused on mechanics while the prompt handles usage policies and edge cases.

Encouraging Complete Solutions

When building agents that use multiple tools, GPT-5.1 sometimes stops after calling one tool and waits for your approval to continue.

Fix this with autonomous agent prompting:

You are an autonomous agent. When given a task, continue reasoning and using tools until the plan is complete.

Do not bounce decisions back to the user.

Make sensible assumptions about missing details and proceed.

Only ask clarifying questions when absolutely necessary for safety or correctness.


This pushes GPT-5.1 to chain tool calls together and work toward completion without constant check-ins.

Parallel Tool Calls (Work Smarter, Not Harder)

GPT-5.1 can call multiple tools at the same time, dramatically speeding up tasks like:

• Scanning a codebase (reading multiple files)
• Fetching from multiple APIs
• Running several searches simultaneously

Enable parallelism in your prompt:

Parallelize tool calls whenever possible. Batch reads (read_file) and edits (apply_patch) to speed up the process.


Example scenario:

You ask GPT-5.1 to find all references to a function across five files.

Without parallelism: It reads file 1, then file 2, then file 3... (5 sequential calls)

With parallelism: It reads all five files simultaneously (1 parallel call)

The speedup is substantial for I/O-bound operations.

Coding Prompts That Get Results

GPT-5.1 is strong at coding, but you need the right prompts to get clean, maintainable code.

Frontend Design System Enforcement

If you're building interfaces, constrain GPT-5.1 to your design tokens:

<design_system_enforcement>
All colors must come from globals.css variables (--background, --foreground, --primary, --accent, --border, --ring).

Do not hard-code colors (hex/hsl/oklch/rgb) in JSX/CSS.

When introducing a brand color:
1. Add tokens in globals.css under :root and .dark
2. Define --brand, --brand-foreground, --brand-muted
3. Use Tailwind utilities: bg-[hsl(var(--primary))]

Default to neutral palette unless user explicitly requests brand styling.
</design_system_enforcement>


Multi-File Changes Without Confusion

For changes spanning multiple files, use the planning tool and be explicit about scope:

When making changes across multiple files:

1. Create a plan with one item per logical change area
2. Mark exactly which item you're working on
3. Use apply_patch for each file
4. Update the plan after each file is complete
5. Summarize what changed at the end

Never work on multiple plan items simultaneously.


Testing and Validation

Prompt GPT-5.1 to test its changes:

After making code changes:

1. Run relevant tests if they exist
2. If tests fail, fix the issue and re-run
3. Only mark the task complete after tests pass
4. If no tests exist, explain how the change was verified manually

Do not skip verification steps.


When To Use GPT-5.1 vs GPT-5.1-Codex

Use GPT-5.1 when:

• Building full-stack features
• Working across multiple languages and tools
• Needing flexibility beyond pure coding

Use GPT-5.1-Codex when:

• Focused on interactive coding sessions
• Using Codex CLI or Codex IDE extensions
• Want the fastest coding-specific performance

GPT-5.1-Codex is trained specifically for coding and uses different prompting patterns. It prefers minimal prompts and doesn't support preambles.

Metaprompting: Let GPT-5.1 Fix Your Prompts

One of GPT-5.1's hidden superpowers is analyzing and improving prompts.

Instead of guessing which parts of your system prompt cause problems, you can ask GPT-5.1 to diagnose issues and suggest fixes.

Step 1: Diagnose What's Broken

When you notice consistent failures (wrong tool usage, incomplete responses, tone issues), gather a few examples and ask GPT-5.1 to analyze them.

Diagnosis prompt:

You are a prompt engineer debugging a system prompt for [describe your agent].

You are given:
1. The current system prompt: [PASTE YOUR PROMPT]
2. A set of failure examples: [PASTE FAILURE LOGS]

Identify distinct failure modes you see (e.g., tool usage inconsistency, verbosity issues, early termination).

For each failure mode:
- Quote the specific lines in the system prompt causing it
- Explain how those lines steer the agent toward the observed behavior
- Note any contradictions (e.g., "be concise" vs "err on completeness")

Return your analysis in structured format:

failure_modes:
- name: [failure mode name]
 description: [what's going wrong]
 prompt_drivers:
   - line: [problematic instruction]
   - why_it_matters: [how this causes the issue]


What to include in failure logs:

• User query
• Tools called
• Final answer (shortened if needed)
• Why it failed (user feedback, thumbs down, incorrect result)

Group similar failures together. If you submit 20 different issues at once, GPT-5.1 will struggle to connect them.

Step 2: Get GPT-5.1 To Patch Your Prompt

Once you have the diagnosis, run a second call asking for specific fixes.

Patch request prompt:

You previously analyzed this system prompt and found these failure modes: [PASTE DIAGNOSIS]

Original system prompt: [PASTE PROMPT]

Propose a surgical revision that reduces the observed issues while preserving good behaviors.

Constraints:
- Do not redesign the agent from scratch
- Prefer small, explicit edits
- Clarify conflicting rules
- Remove redundant or contradictory lines
- Make tradeoffs explicit (when to prioritize concision vs completeness, when tools must vs must not be called)
- Keep the structure and length roughly similar

Output:
1. patch_notes: List of key changes and reasoning
2. revised_system_prompt: Full updated prompt with edits applied


Real Example: Fixing an Event Planning Agent

Original problem:

An event planning agent was calling tools for simple conceptual questions, being overly verbose for small events, and refusing to make plans without excessive clarifications.

Diagnosis revealed:

• Conflicting tool usage rules ("prefer tools" vs "avoid tools for simple questions")
• Contradictory verbosity guidance ("err on completeness" vs "most responses should be 3-6 sentences")
• Ambiguous autonomy instructions ("make assumptions" vs "ask for key details")

GPT-5.1's patch:

• Merged tool usage rules into a hierarchy: "Never use tools for conceptual questions. Always use tools for events >30 people. Use judgment for events <30 people."
• Split verbosity by complexity: "Simple queries: 3-6 sentences. Multi-day events: detailed structure with sections."
• Clarified when to ask questions: "Ask only when date, city, or headcount is completely missing. Make reasonable assumptions for everything else."

Result:

The agent stopped over-calling tools, adapted response length to query complexity, and asked far fewer unnecessary clarifying questions.

Migrating From Older Models

If you're switching to GPT-5.1 from GPT-4.1 or GPT-5, here's what to adjust.

Coming From GPT-4.1

1. Switch to "none" reasoning mode

Set reasoning_effort: "none" for GPT-4.1-like behavior. This gives you low latency while maintaining GPT-5.1's better instruction following.

2. Prompting patterns still work

Few-shot prompting, detailed tool descriptions, and explicit formatting instructions all carry over from GPT-4.1.

3. Push for careful thinking when needed

Even in "none" mode, prompting GPT-5.1 to "plan extensively before function calls" improves accuracy on complex tasks.

4. Watch for:

• GPT-5.1 being more literal with instructions (which is good, but means conflicting instructions cause more problems)
• Function calling working better than GPT-4.1 (parallel calls are much more reliable)

Coming From GPT-5

1. Emphasize completeness

GPT-5.1 can be excessively concise compared to GPT-5. Add persistence prompting to avoid partial solutions:

Persist until the task is fully handled end-to-end. Do not stop at analysis or partial fixes. Carry changes through implementation, verification, and clear explanation of outcomes.


2. Control verbosity explicitly

GPT-5.1 tries to be efficient.

If you want detailed responses, say so:

For this task, provide comprehensive output including:
- Step-by-step reasoning
- All relevant code changes
- Testing approach
- Potential edge cases


3. Update your apply_patch implementation

If you have a custom apply_patch tool, migrate to the new named tool type:

tools=[{"type": "apply_patch"}]


This reduces errors by 35% compared to custom implementations.

4. Be explicit about output formatting

GPT-5.1 follows formatting instructions precisely. If your prompts were vague about structure, tighten them:

Format your response as:
1. Brief summary (1-2 sentences)
2. Changes made (bullet list)
3. Code snippets (only if necessary, max 10 lines each)


5. Check for conflicting instructions

GPT-5.1's better instruction-following means contradictions in your prompt cause more noticeable issues. Use metaprompting to find and fix conflicts.

7 Ready-To-Use Prompts You Can Copy Today

Here are seven prompts that work well with GPT-5.1. Copy them, adjust for your use case, and test.

1. Debugging Code

You are debugging this code. Analyze the error, identify the root cause, and provide a complete fix with explanation.

Code:
[PASTE YOUR CODE]

Error:
[PASTE ERROR MESSAGE]

Requirements:
- Explain what caused the error in one sentence
- Show the fixed code with clear comments
- Highlight what changed and why
- If multiple solutions exist, explain tradeoffs

Do not stop at identifying the problem. Provide the complete fix.


When to use: Any time you have a bug and error message

Expected output: Root cause explanation + complete fix + reasoning

2. Planning Complex Projects

You are planning [PROJECT DESCRIPTION].

Create a project plan with:
1. Core objectives (what success looks like)
2. Key milestones (3-5 major phases)
3. Critical dependencies (what blocks what)
4. Risk areas (what could go wrong)
For each milestone:
- Clear deliverable
- Estimated effort (small/medium/large)
- Prerequisites

Format as a structured markdown doc. Be thorough but concise.


When to use: Starting new projects or features

Expected output: Organized plan with clear phases and dependencies

3. Writing Documentation

Write documentation for [FEATURE/FUNCTION/API].

Target audience: [DEVELOPERS/END USERS/INTERNAL TEAM]

Include:
- Overview (what it does, why it exists)
- How to use it (with examples)
- Common pitfalls (what goes wrong)
- Related features (what else might be relevant)

Tone: Professional but approachable. Assume the reader is smart but unfamiliar with this specific feature.

Keep examples realistic and copy-paste ready.


When to use: Documenting code, features, or processes

Expected output: Complete documentation with examples

4. Customer Support Agent

You are a customer support agent for [COMPANY/PRODUCT].

Your communication style:
- Direct and action-oriented
- Warm when the customer is friendly
- Efficient when stakes are high
- Never robotic or overly apologetic

When responding:
1. Acknowledge the issue briefly
2. Provide the solution or next steps
3. Confirm resolution or ask a single clarifying question if needed

Avoid:
- Repeating yourself
- Excessive "I'm sorry" or "Thank you for your patience"
- Asking multiple questions at once

Focus on solving the problem quickly and completely.


When to use: Building support chatbots

Expected output: Efficient, helpful responses that solve problems

5. Code Review

Review this code for:

[PASTE CODE]

Focus on:
- Correctness (does it do what it should?)
- Edge cases (what inputs break it?)
- Readability (is it clear to other developers?)
- Performance (any obvious bottlenecks?)
- Security (any vulnerabilities?)

For each issue:
- Severity: Critical/Important/Minor
- Explanation: Why it matters
- Fix: How to address it (with code if relevant)

Be direct. Praise good patterns but focus on what needs improvement.


When to use: Reviewing code before merging

Expected output: Structured feedback with actionable fixes

6. Research Synthesis

Research [TOPIC] and synthesize findings.

Approach:
1. Identify 3-5 high-quality sources
2. Extract key insights from each
3. Synthesize into coherent summary
4. Note areas of agreement and disagreement
5. Highlight gaps or uncertainties

Format:
- Executive summary (3-4 sentences)
- Key findings (organized by theme, not by source)
- Source citations
- Open questions

Prioritize recent, authoritative sources. Be clear when sources conflict.


When to use: Researching topics for decisions or reports

Expected output: Synthesized research with clear takeaways

7. Frontend Development

Build [COMPONENT/FEATURE DESCRIPTION].

Tech stack:
- [FRAMEWORK: React/Vue/etc]
- [STYLING: Tailwind/CSS Modules/etc]
- [STATE: Context/Redux/etc]

Requirements:
[LIST FUNCTIONAL REQUIREMENTS]

Design constraints:
- Follow design system in globals.css (use CSS variables, no hard-coded colors)
- Mobile-responsive
- Accessible (proper ARIA labels, keyboard navigation)

Provide:
1. Component code (well-commented)
2. Usage example
3. Styling (adhering to design tokens)

Keep it production-ready. Don't cut corners on accessibility or responsive design.


When to use: Building UI components

Expected output: Production-ready component code with examples

Common Problems and Quick Fixes

Here are the issues people run into most with GPT-5.1, and the fast fixes that work.

"My responses are too short"

Problem: GPT-5.1 gives 2-3 sentences when you need detailed explanations.

Fix: Add this to your prompt:

Provide comprehensive responses. Include:
- Step-by-step reasoning
- Relevant examples
- Potential edge cases

Do not optimize for brevity at the expense of completeness.


"GPT-5.1 keeps asking questions instead of doing the work"

Problem: It asks for clarification on things it could reasonably assume.

Fix: Add autonomous agent instructions:

You are an autonomous agent. Make sensible assumptions about missing details and proceed.

Only ask clarifying questions when absolutely necessary for safety or correctness.

Be extremely biased toward action. If unsure, make the change and explain your assumptions.


"Output is too verbose"

Problem: GPT-5.1 writes paragraphs when you want bullets.

Fix: Be specific about format:

<output_format>
- Respond in 2-4 sentences maximum
- Use bullets only when listing 3+ items
- Keep code snippets under 10 lines
- No repeated acknowledgments or filler
</output_format>


"It's not following my instructions"

Problem: GPT-5.1 ignores parts of your prompt.

Fix: Check for conflicts using metaprompting:

1. Ask GPT-5.1 to analyze your prompt for contradictions
2. Look for competing instructions ("be brief" vs "be thorough")
3. Remove or clarify conflicts

GPT-5.1 follows instructions very literally. Contradictions confuse it more than GPT-5 did.

"Tool calls aren't working right"

Problem: GPT-5.1 doesn't call tools when it should, or calls them incorrectly.

Diagnosis:

1. Check your tool description (is it clear what the tool does?)
2. Check your usage rules in the prompt (do they match the tool's purpose?)
3. Look for conflicting guidance (are you saying both "use tools" and "avoid tools"?)

Fix:

<tool_usage_rules>
You MUST call [TOOL_NAME] when [SPECIFIC CONDITION].

Do NOT call [TOOL_NAME] when [OPPOSITE CONDITION].

After calling the tool, always [EXPECTED NEXT STEP].
</tool_usage_rules>


Add explicit examples of correct tool usage in your prompt if errors persist.

When To Use GPT-5.1 vs Other Models

GPT-5.1 isn't always the right choice. Here's how to decide.

GPT-5.1 vs GPT-5

Use GPT-5.1 when:

• You need faster responses on simple tasks
• Latency matters more than maximum reasoning depth
• You want better steerability over tone and format
• You're using the new tools (apply_patch, shell, planning)

Use GPT-5 when:

• Maximum reasoning quality matters more than speed
• You're working on extremely complex problems
• The task benefits from extended thinking
• Cost isn't a primary concern

Tradeoff:

GPT-5.1 is more efficient but can sometimes be excessively concise. GPT-5 is more thorough but slower and more expensive on simple tasks.

GPT-5.1 vs GPT-5.1-Codex

Use GPT-5.1 when:

• Building full-stack features
• Working across languages and tools
• Need flexibility for non-coding tasks
• Want detailed explanations and documentation

Use GPT-5.1-Codex when:

• Pure coding workflows (especially in Codex CLI/IDE)
• Interactive coding sessions
• Want the fastest coding-specific responses
• Prefer minimal prompting

Key difference:

GPT-5.1-Codex is optimized specifically for coding and uses different prompting patterns. It doesn't support preambles and works best with minimal prompts.

Speed vs Intelligence Tradeoffs

GPT-5.1 offers four reasoning levels:

1. none — No reasoning tokens, fastest responses

• Use for: Simple Q&A, quick edits, low-latency needs
• Skip for: Complex reasoning, multi-step problems

2. low — Light reasoning, good balance

• Use for: Most coding tasks, standard complexity
• Skip for: When maximum accuracy is critical

3. medium — Standard reasoning depth (default in GPT-5)

• Use for: Complex coding, analytical tasks
• Skip for: Simple queries where it's overkill

4. high — Maximum reasoning, slowest but most thorough

• Use for: Critical decisions, complex mathematics
• Skip for: Anything time-sensitive

Rule of thumb: Start with "none" or "low" and increase only when results aren't good enough.

Cost Considerations

Cheapest to most expensive:

1. GPT-5.1 with reasoning_effort: "none" — Similar pricing to GPT-4.1
2. GPT-5.1 with reasoning_effort: "low" — Moderate reasoning token usage
3. GPT-5.1 with reasoning_effort: "medium" — Higher reasoning token costs
4. GPT-5.1 with reasoning_effort: "high" — Most expensive
5. GPT-5 — Generally more expensive than GPT-5.1 for simple tasks

Cost optimization tips:

• Use "none" mode for high-volume, simple requests
• Reserve higher reasoning levels for complex tasks only
• Use extended prompt caching (24-hour retention) for repeated contexts
• Batch similar queries together to maximize cache hits

What's Next For GPT-5.1

OpenAI continues to invest heavily in agentic and coding models.

Improvements In Progress

OpenAI has stated they're working on:

• Even more capable models in the weeks and months ahead
• Better coding performance and reliability
• Improved function calling across all reasoning modes
• Enhanced steerability for complex workflows

How To Stay Updated

Official channels:

• OpenAI Platform Documentation (platform.openai.com/docs)
• OpenAI Cookbook (cookbook.openai.com)
• OpenAI Developer Forum
• OpenAI API changelog

Testing new features:

OpenAI releases updates regularly. Enable automatic model updates in your API configurations, but test thoroughly in staging before production rollout.

Community Resources

• OpenAI Discord (for real-time help)
• GitHub discussions on the OpenAI Cookbook repo
• r/OpenAI and r/PromptEngineering on Reddit
• Twitter/X accounts of OpenAI developers for early announcements

The prompting landscape changes fast. What works today might need adjustment in three months as models improve.

Conclusion

GPT-5.1 is fundamentally different from earlier models, and that difference shows up most in how you write prompts.

The core principle: Be specific, stay specific.

Don't tell GPT-5.1 to "be helpful" — define exactly what helpful means in your context. Don't say "respond appropriately" — spell out what tone, length, and format you want.

GPT-5.1 will follow your instructions with precision. The challenge isn't getting it to listen. The challenge is giving it instructions worth listening to.

One Thing To Try Today

Pick your most common use case for GPT-5.1. Write down exactly what you want the output to look like: tone, length, format, level of detail.

Now look at your current prompt. Does it actually specify those things?

If not, add them. Test the difference.

You'll see how much control you actually have over GPT-5.1's behavior once you're explicit about what you want.

Why Iteration Beats Perfection

No prompt is perfect on the first try. The best prompts emerge through testing, failure analysis, and refinement.

Use metaprompting to diagnose issues. Make small, targeted changes. Test with real examples. Repeat.

GPT-5.1 is excellent at following instructions. Your job is writing instructions that describe what you actually need, not what sounds good in theory.

The models will keep improving. The prompting skills you build now — being specific, diagnosing failures, iterating systematically — those transfer to whatever comes next.