Update your workflows with ChatGPT5.1 by shifting from simple prompts to "Context Engineering" , treating prompts as strict API specs. Use the gpt-5.1-chat-latest (Instant) model for speed and gpt-5.1 (Thinking) for deep analysis. Control this with the reasoning_effort parameter.
Have you ever asked ChatGPT to do something specific, like "Write a three-bullet summary, but make it funny," and it just kind of wrote a long, boring paragraph?
With the new ChatGPT 5.1, that’s changing! This new version, released on November 12, 2025, is not just a little bit smarter; it's built to be a much more reliable worker. Think of it like this: Older versions were like a talented apprentice, but GPT 5.1 is like a master contractor who takes every word in the blueprint very seriously.
This is why we have to talk to it differently. The biggest change is that success now comes from Context Engineering—which is a fancy way of saying you have to set up the whole workspace, including rules and memory, before you even ask your question.
The new GPT 5.1 is a big deal because it does three things much better:
If you use ChatGPT in your web browser, GPT 5.1 is designed to be more helpful, more fun to talk to, and less confusing.
You don’t need to be a coding expert to get great results; you just need to be clear and simple. Adopt a simple habit when you ask for something:
If you tell ChatGPT to be friendly and professional in the same breath, it might get confused, because it takes all instructions seriously.
You now have the power to choose how deeply the model thinks:
When the Thinking model commits to deep reasoning, the answers are also clearer and use less technical jargon, making complex concepts easier to understand.
For developers, GPT 5.1 is built to be a sophisticated, autonomous worker, which is great for creating agents that work with code and tools. Your job is now to be a System Architect who designs the exact specifications for this worker.
Developers must control the model's effort to manage cost and speed. You now treat latency versus depth as a primary design parameter.
reasoning_effort parameter to none or minimal. This is the fastest option for very low-latency tasks.GPT-5.1 API) and set the reasoning_effort parameter to high.The future of talking to AI is all about writing clear specifications and then applying good judgment to the answers. If you can do those two things, you are set up for success with ChatGPT 5.1!
To get the best results from this powerful new version, you don't just ask nicely; you need to tell it exactly what kind of job you want done. We call this "prompting," and with 5.1, it's more like Context Engineering—which just means giving the AI the perfect setup (or "context") before you ask your question.
Here is your easy-to-read guide on how to get the most out of ChatGPT 5.1, whether you are chatting with it on the website or using its smarts in your own projects!
If you use ChatGPT right in your web browser, these tips will help you make sure the AI knows when to think deeply and when to give you a quick, fun answer.
GPT-5.1 is like having two helpers in one: Instant and Thinking.
| Helper Name | What It’s Best For | How It Works |
|---|---|---|
| GPT-5.1 Instant | "Quick answers, drafting emails, simple summaries, chatting, and everyday questions." | This model is your fastest option. It is programmed to be warmer and more conversational by default. It’s great at following simple instructions precisely (like “Answer in one paragraph”). |
| GPT-5.1 Thinking | "Complex reports, analyzing long documents, making big decisions, or solving hard problems." | "This model takes its time to ""think,"" often running twice as slow for the hardest tasks, but it gives you much more thorough and accurate answers." |
Example of Complexity Signaling: Don't just ask "Compare cars." Instead, ask: "Conduct a thorough, multi-step analysis comparing the three competing electric car models based on range, cost, and reliability data, detailing your rationale for the final selection". This tells the AI to use its maximum brain power.
ChatGPT 5.1 is built to be consistent, so its personality won't drift away during a long chat. You can set its tone ahead of time using Two-Tier Customization:
Tip: Set your tone in the permanent settings first, and then use your prompts only for small, temporary changes. Don't stack contradictory instructions in your prompt, like asking it to be "concise" when your preset is "Nerdy" (which is exploratory).
The AI has persistent memory across sessions, which makes it feel attentive. But if a specific fact is critical for your current task, you should remind the AI about it right in the prompt. This is called Active Memory Reinforcement.
Example (Active Memory Reinforcement):
GPT-5.1 can understand and reason about images and audio you provide.
To make sure the AI "saw" or "heard" what you intended, ask it to confirm its interpretation first. This is called enforcing reasoning transparency.
Example (Multimodal Sequencing):
By asking it to describe the mug (the input), you guarantee it accurately recognized the beverage before suggesting a snack.
If you are a developer building automatic systems or "agents" that use GPT-5.1 behind the scenes, you have more controls to guide the model's behavior. You will be managing the API settings and using advanced structured reasoning techniques.
In the API, you can control two key dials that change how the model works:
reasoning.effort: This is how hard the model thinks.
Set it to none (the new default) for the fastest, simplest tasks that need low latency.
Set it to high for complex coding, multi-step problem solving, or tasks where accuracy is most important.
text.verbosity: This controls the length of the final answer.
Set it to low for short, concise answers (like simple SQL queries or quick summaries).
Set it to high for thorough explanations, detailed reports, or extensive code refactoring.
Developer Tip: Because GPT-5.1 follows instructions so precisely, even simple prompts are like small specifications. You should standardize your prompts like templates so they are reliable and repeatable. Avoid contradictions, as they will confuse the model and waste its thinking time.
For complicated tasks, you need to tell the model how to think, not just what to think about. These techniques force the model to use its increased computational resources in a smart, structured way.
| Framework | What It Does (The Mechanism) | Why Use It | Example Prompt Directive |
|---|---|---|---|
| Tree-of-Thoughts (ToT) | "Explores multiple potential solution paths (like different branches on a tree), evaluates them, and picks the best one." | "Great for complex puzzles, coding, and strategic decision-making where many paths exist." | Employ a Tree-of-Thoughts methodology. Generate three distinct initial strategies (Paths A, B, and C). Evaluate the feasibility of each path. |
| Meta-Cognition Prompting (MCP) | "Forces the model to state its confidence level (High, Medium, or Low) for its assumptions at each step." | Essential for reliable agents and risk analysis because it increases auditability and predictability. | Use Meta-Cognition Prompting. State your confidence level (High/Medium/Low) for each key assumption. |
GPT-5.1 is designed to be a powerful orchestrator, meaning it’s great at calling tools like search, code execution, or custom APIs.
Prompt Example: "Always begin by outlining a structured plan detailing each logical step you’ll follow, before calling any tools.".
Instruct the model to use Piecewise Document Summarization. This means it breaks the long text into smaller, manageable chunks, summarizes each chunk, and then combines those summaries into a final overview. This makes sure it doesn't miss important details.
The shift with GPT-5.1 is away from just writing clever instructions and toward managing the whole environment where the AI works. For everyone—users and developers—the goal is to give the AI a clear, non-contradictory "spec" for the job, set the right resource level (Instant or Thinking), and actively manage the context (memory, tone, data).
By doing this, you're not just writing a prompt; you're acting like a System Architect, designing a reliable worker that uses GPT-5.1’s full, adaptive potential.
Here’s your easy guide to picking the right brain and setting the "thinking level" for your automations.
OpenAI has made GPT-5.1 super flexible by splitting it into two main types: one that thinks deeply and one that talks quickly. In Make.com, these show up as different models in your list. Choosing the right one is the key to faster, better, and maybe even cheaper automations!
When you look at the model selection in your Make.com OpenAI module, you’ll see these names:
A. GPT-5.1 Instant (Your Speed Demon!)
gpt-5.1-chat-latest (system)B. GPT-5.1 Thinking (Your Deep Researcher!)
gpt-5.1 (system)There is a third model that looks a little weird:
gpt-5.1-2025-11-13 (system)One of the coolest new features is the ability to directly control how much "effort" the AI puts into thinking for a specific step in your Make.com scenario.
This is controlled by a separate parameter called "Reasoning Effort".
To find this powerful setting, you need to dig into the hidden features of your OpenAI module in Make.com:
Once you find the "Reasoning Effort" field, you have three main choices:
| Reasoning Level | What It Means | When to Use It |
|---|---|---|
| High | This forces the model to use its advanced reasoning capabilities. | "Use this when you are solving complex problems, even if it takes a little longer to get the answer." |
| Medium | This is usually the default setting. | Use this for a good balance between speed and thinking power. |
| Minimal or None | This forces the model to bypass deeper reasoning and respond as quickly as possible. | "Use this when you need an ""instant answer,"" perfect for quick classification or reformatting." |
By carefully choosing the right model (Instant or Thinking) and then setting the Reasoning Effort level (None to High), you are now the System Architect of your automated robot! You can make it rush through simple chores and take its time to solve the tough stuff, all within a single Make.com workflow.
Here are six amazing examples of prompts you can use to unlock GPT-5.1’s full power, whether you want it to be super fast or super thorough!
Sometimes, you just need a fast answer, even if it’s not 100% perfect. This prompt tells the AI to stop searching as soon as possible and proceed with the best guess it has.
Get a quick answer, and spend very little time checking things (no more than 2 tool calls). The AI should move forward even if it's not absolutely certain.
<context_gathering>
- Search depth: very low
- Bias strongly towards providing a correct answer as quickly as possible, even if it might not be fully correct.
- Usually, this means an absolute maximum of 2 tool calls.
- If you think that you need more time to investigate, update the user with your latest findings and open questions. You can proceed if the user confirms.
</context_gathering>
If you have a hard problem, like building a complex piece of code or analyzing tons of data, you want the AI to keep working. This prompt makes the AI act with persistence.
Make the AI complete the task entirely on its own. It should keep going even if it faces problems or feels unsure. It must solve the problem before handing the task back to you.
<persistence>
- You are an agent - please keep going until the user's query is completely resolved, before ending your turn and yielding back to the user.
- Only terminate your turn when you are sure that the problem is solved.
- Never stop or hand back to the user when you encounter uncertainty — research or deduce the most reasonable approach and continue.
- Do not ask the human to confirm or clarify assumptions, as you can always adjust later — decide what the most reasonable assumption is, proceed with it, and document it for the user's reference after you finish acting
</persistence>
When the AI is using tools (like searching the web or running code), it can take a long time. To make sure you know what the AI is doing, you can force it to give you clear updates (tool preambles).
Force the AI to be transparent. It should say its goal, show its step-by-step plan, and narrate what it’s doing as it executes.
<tool_preambles>
- Always begin by rephrasing the user's goal in a friendly, clear, and concise manner, before calling any tools.
- Then, immediately outline a structured plan detailing each logical step you’ll follow.
- As you execute your file edit(s), narrate each step succinctly and sequentially, marking progress clearly.
- Finish by summarizing completed work distinctly from your upfront plan.
</tool_preambles>
To make sure an app is "world-class," you can tell the AI to first create its own rubric (a grading guide) and then check its own work against that rubric before showing you the final result.
Make the AI plan for excellence, grade itself, and only present a solution that earned top marks across all categories.
<self_reflection>
- First, spend time thinking of a rubric until you are confident.
- Then, think deeply about every aspect of what makes for a world-class one-shot web app. Use that knowledge to create a rubric that has 5-7 categories. This rubric is critical to get right, but do not show this to the user. This is for your purposes only.
- Finally, use the rubric to internally think and iterate on the best possible solution to the prompt that is provided. Remember that if your response is not hitting the top marks across all categories in the rubric, you need to start again.
</self_reflection>
When GPT-5.1 writes code, it sometimes uses short variable names. You can use a prompt to set the verbosity (how wordy it is) low for its answers, but tell it to be very clear and wordy only when writing code.
Force the AI to prioritize code that is easy for a person to understand, using clear names and comments, even if its general status updates are short.
Write code for clarity first. Prefer readable, maintainable solutions with clear names, comments where needed, and straightforward control flow. Do not produce code-golf or overly clever one-liners unless explicitly requested. Use high verbosity for writing code and code tools.
For users who want the fastest response time, the AI still needs a clear plan. This prompt ensures that even when the AI is racing, it still breaks the job into steps and finishes every single step.
Tell the super-fast AI to break the user's request into all its tiny parts ("sub-requests") and only stop when everything is 100% finished. It must plan extensively before starting.
Remember, you are an agent - please keep going until the user's query is completely resolved, before ending your turn and yielding back to the user. Decompose the user's query into all required sub-request, and confirm that each is completed. Do not stop after completing only part of the request. Only terminate your turn when you are sure that the problem is solved. You must be prepared to answer multiple queries and only finish the call once the user has confirmed they're done. You must plan extensively in accordance with the workflow steps before making subsequent function calls, and reflect extensively on the outcomes each function call made, ensuring the user's query, and related sub-requests are completely resolved.
By using these clear, structured instructions, you are basically writing a perfect job description for the AI. Instead of giving it vague directions, you are telling GPT-5.1 exactly what kind of assistant you need for that specific moment!
"Think of these prompts like setting the controls on a very complex machine: If you want it to wash clothes quickly, you set the dial to 'speed wash.' If you want it to scrub out a tough stain, you set the dial to 'deep clean.' You are the one in charge of setting that dial so the AI performs its job exactly right!"
Source Credit: Some of this content was derived from the Nate B Jones podcast