OpenAI's 2025 ChatGPT Update Boosts Custom Instructions, Thinking Speed

The landmark OpenAI's 2025 ChatGPT update boosts Custom Instructions and Thinking Speed, giving users unprecedented control over how the AI communicates and reasons. By defining a persistent persona and adjusting reasoning depth, you can transform ChatGPT from a generic tool into a specialized assistant - a coding mentor, legal aide, or brand-aligned copywriter - that fits right into your daily work. These settings cascade across all chats, ensuring consistent, high-quality output tailored precisely to your needs.

For professionals and creators, optimizing ChatGPT for more relevant answers is a top priority. This guide provides a practical walkthrough of two key features: Custom Instructions for persona-setting and Thinking Speed for adjusting reasoning depth. Mastering these tools allows you to fine-tune ChatGPT's output for any task.

A significant enhancement introduced in the November 2025 update, detailed in the OpenAI release notes, is that all setting changes are now persistent and apply globally in real time. This allows you to define a tone, format, or specific behavioral guardrails once and have them take effect instantly across all conversations.

Fine-Tuning ChatGPT: A Guide to Custom Instructions and Thinking Speed

Tuning ChatGPT involves two primary controls. Use Custom Instructions to set a consistent persona, defining its role, audience, and response style. Then, use the Thinking Speed slider to balance response latency with reasoning depth, switching between fast, surface-level answers and slower, more accurate analysis.

Custom Instructions are configured in two 1,500-character text fields:
1. User Profile: Define key context for ChatGPT, such as your professional role, target audience, and specific brand voice.
2. Response Guidelines: Specify the desired output format, including response length, use of lists, and citation requirements.

For best results, start with a simple directive (e.g., "Act as a senior marketing analyst writing for a C-suite audience") and iterate based on the output quality.

The Thinking Speed setting functions as a dial for reasoning effort. Low-effort mode delivers rapid responses (often under two seconds) suitable for simple queries, but may produce shallower insights. High-effort mode utilizes more tokens and advanced chain-of-thought processes. As shown in GPQA Diamond benchmarks, this boosts accuracy on complex tasks from 77.8% to 85.7%, though it introduces a noticeable processing delay.

Practical Workflow Templates

Combine these settings to create powerful, task-specific templates:
* Coding Assistant: Set persona to "Senior Rust Mentor" with low-latency thinking for quick code comments.
* Meeting Summarizer: Use an "Analyst" persona with medium speed to generate numbered takeaways.
* Legal Reviewer: A "Paralegal" persona with high-effort thinking ensures a thorough, clause-by-clause analysis.
* Customer Support: Employ a "Support Agent" persona with low-speed thinking for an empathetic tone.

To maximize efficiency, integrate these settings with automation tools like Zapier. A new GitHub issue, support ticket, or calendar event can automatically trigger the appropriate ChatGPT configuration, ensuring seamless context-switching throughout the day.

Measuring Performance and Impact

To objectively measure the impact of your settings, focus on accuracy and latency. Create a benchmark test set of representative prompts for each persona. Grade the generated answers using a simple rubric that evaluates completeness, adherence to instructions, and factual accuracy.

Begin by adjusting the Thinking Speed setting; if performance plateaus, refine the wording in your Custom Instructions. Simultaneously, monitor latency and token consumption, as high-effort modes can increase both. If response times exceed your workflow's tolerance, dial back the thinking effort and consider caching responses for recurring queries.

When to Use High-Effort Thinking

High-effort thinking is most valuable for tasks demanding precision and depth, such as complex research, multi-step quantitative problems, and medical or scientific analysis. For instance, internal 2025 health benchmark trials showed that enabling deeper thinking reduced significant factual errors by approximately 20% and improved accuracy on difficult questions by 15%. For high-stakes applications, this dramatic gain in reliability justifies the longer wait time.

Key Takeaways

Ultimately, Custom Instructions control ChatGPT's voice, while Thinking Speed governs its cognitive depth. By strategically combining these features - and integrating them into automated workflows - you can elevate ChatGPT from a generic tool into a powerful, context-aware partner that adapts dynamically to your professional needs.

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How do I set up custom instructions so every ChatGPT reply follows my exact persona and output rules?

Open the side menu, choose Settings → Personalization, and fill the two 1,500-character boxes:
- "What should ChatGPT know about you?" - background, role, audience
- "How should it respond?" - tone, format, things to always or never do

Save once; since the November 7 2025 update the rules now apply to all existing and future chats automatically, so you never have to repeat them. (Need a walk-through? This 2026 video shows the clicks at 1:35.)

What is the "thinking speed" slider and how does it change answers?

It is a tunable reasoning-effort switch:
- Fast/low - ~1,500 internal tokens, great for quick drafts or chat
- Deep/high - up to ~8,000 tokens, multi-step reflection that lifts GPQA science accuracy from 77.8% to 85.7% and cuts major errors by roughly one fifth

Expect higher latency in deep mode, so reserve it for tasks where accuracy outweighs waiting time.

Which built-in persona templates are worth trying first?

Popular 2025 presets you can paste straight into the instruction boxes:

1. Data Analyst - "Return only CSV-style tables, then a two-sentence insight."
2. Support Agent - "Use empathy markers, summarize next step in bold, keep answers under 80 words."
3. Code Reviewer - "Spot security issues first, follow PEP8, finish with a runnable pytest."

Combine them with the thinking-speed toggle (fast for support, deep for code) and you have a ready-made toolkit.

How can I measure whether my new settings really perform better?

Run a mini-benchmark:
1. Keep a bank of 10-15 representative prompts
2. Record accuracy (did it meet the spec?) and completeness (any missing step?)
3. Average scores across fast vs deep modes; expect up to 10-20% accuracy jump on complex reasoning when deep is on

Store results in a simple spreadsheet and update instructions iteratively.

Can I automate context switching so ChatGPT uses "slow & thorough" for meetings and "fast & casual" for Slack?

Yes. Link ChatGPT to a workflow tool (Zapier, Make, or a short Python script) and let triggers prepend a hidden line like:

/thinking:high /persona:"Executive Analyst"

before the user's prompt. Salesforce, Calendly, or even a calendar event can act as the trigger, handing off customer data or meeting agendas in the same call. Enterprises using this omni-channel approach in 2025 report 24/7 personalized support and faster internal reports without manual toggling.