Optimizing ChatGPT Usage in 2025
This document summarizes key strategies and features for effective ChatGPT utilization in 2025, The document focuses on model selection, web search, deep research, the canvas feature, and general prompting best practices.
I. Model Selection: Basic Chat vs. Reasoning Models
The most critical decision for optimal ChatGPT performance is selecting the appropriate model. The source emphasizes that the choice should depend on the complexity of the task, not the type of task.
- Default Recommendation: For most users, "default to the latest O number reasoning model with the cleanest name." This is analogous to choosing a romantic partner "without the extra baggage at the end."
- Basic Chat Models (GPT-number):
- Purpose: Ideal for "low stakes" tasks where a "fast response" is prioritized.
- Examples:
- "Which fruits have the most fiber?"
- "Who is the guy who said uh success is never final...?"
- "Straightforward emails where we know exactly what to say."
- Reasoning Models (O number reasoning model):
- Purpose: Best for queries that are "important or hard" and when the user is "willing to wait a little bit for a better answer."
- Examples:
- "Act as a nutritionist and create a vegetarian breakfast..."
- "Act as a British historian and explain why Churchill was ousted..."
- "Write a diplomatic reply to a complex email chain."
General Recommendation: "Most of us should default to reasoning models since worst case we wait a bit longer for a better answer."
Pro Tips for Prompting Reasoning Models:
- Use delimiters to separate instructions from content.
- Avoid "Think step by step"; it hurts reasoning model performance.
- Examples are optional — reasoning models work well zero-shot.
II. Web Search Feature: ChatGPT vs. Google
The web search feature in ChatGPT is powerful, but should be used alongside traditional Google Search.
- Google Search Still Relevant: Better for quick, single facts.
- Rule of Thumb:
- Google: "If you need a single fact."
- ChatGPT Search: "If you need a fact with a quick explainer."
Pro Tips for ChatGPT Search:
- Quick fact checks work well.
- Use forward slash command to toggle search in the chat box.
III. Deep Research Feature: Automated Comprehensive Analysis
Deep Research functions like an agent that spends 10–20 minutes reading dozens of links to produce a detailed report.
- Key Advantage: No more manual tabs or stitching info together.
- Use Cases:
- Market analysis (e.g., AI chip roadmaps).
- Personal finance (e.g., savings account comparison).
- Business intelligence with internal + external data.
Pro Tips for Deep Research:
- Use detailed prompts for best results.
- Try custom GBT templates from Reddit or upload the OpenAI help PDF.
- Test results vs. Google Gemini for your use case.
IV. Canvas Feature: Iterative Content Creation and Editing
Use Canvas when you anticipate editing and refining multiple times. It opens a standalone window for iterative work.
- Use Cases:
- Performance reviews with rubric uploads.
- Powerful for coding.
- Copywriting with inline edits and rephrasing.
Pro Tips for Canvas:
- Use version history to jump between drafts.
- Take advantage of built-in shortcuts and suggest edits.
- Download final output as Markdown for Google Docs formatting.
V. General Text-to-Text Model Commands (Bonus Tips)
- "Elaborate": Adds more detail. Example: "Elaborate on these three bullet points."
- "Critique": Spots problems early. Example: "Critique my approach before my presentation."
- "Rewrite": Improves text. Example: "Rewrite the second paragraph in a friendly tone."
This briefing highlights the evolving landscape of ChatGPT functionalities, emphasizing strategic model selection, targeted use of search and deep research, and leveraging the Canvas feature for collaborative content development. Adopting these practices will significantly enhance user efficiency and the quality of AI-generated output.
ChatGPT Usage FAQ (2025)
How do I choose the right ChatGPT model for my task?
For most users, the default choice should be the latest "O number reasoning model" with the "cleanest name" (i.e., without extra descriptive baggage). The key distinction is between basic chat models (GPT-number) and reasoning models. Basic chat models are best for low-stakes tasks that require a fast response, such as asking for a simple fact like "which fruits have the most fiber." Reasoning models, on the other hand, are designed for important or complex queries where you're willing to wait for a more accurate and nuanced answer. Examples include tasks requiring the model to "act as a nutritionist" and create a specific meal plan, or to "act as a British historian" to explain a complex historical event. The choice of model should depend on the complexity of the task, not just the type of task.
What are the best practices for prompting reasoning models?
There are three key pro tips for prompting reasoning models:
- Use delimiters: Separate different pieces of information, such as your instructions and the content to be analyzed, using clear sections (e.g., "Task" and "Document").
- Avoid "think step by step": Reasoning models already inherently think step by step, and including this phrase can actually hinder their performance.
- Examples are optional: Reasoning models perform well with "zero-shot" prompting. Only add examples if you are consistently getting incorrect results.
When should I use ChatGPT's web search feature versus Google Search?
The general rule of thumb is:
- Google Search is ideal if you need a single, quick fact (e.g., "Nvidia stock price" or "weather forecast in Zurich"). It's often faster and more straightforward.
- ChatGPT with search enabled is better when you need a fact accompanied by a quick explainer, contextual information, or a structured output. For instance, "When was Nvidia's latest earnings call, did the stock price go up or down, and why?" or "Weather forecast in Zurich from December 1st to 7th, what clothes should I bring?" It's also useful for getting data in formats like tables.
What is ChatGPT's "Deep Research" feature and how is it used?
Deep Research is a powerful agent that can spend 10-20 minutes reading dozens to hundreds of links to produce a detailed report on a given topic. It's designed to automate comprehensive information gathering and analysis that would typically require significant manual effort. For example, instead of manually sifting through earnings reports from multiple companies, you can ask Deep Research to "analyze and compare the AI chip roadmaps for Nvidia, AMD, and Intel based on their latest earnings calls." It can also connect to private sources like Google Drive for business contexts, drawing from internal company data. Deep Research works best with detailed and comprehensive prompts, for which templates can be found from community resources or generated by the model itself.
What is the "Canvas" feature and when should I use it?
The Canvas feature should be enabled when you anticipate needing to edit and build upon ChatGPT's response multiple times. It provides a standalone window where the generated text can be collaboratively refined. For example, when preparing for a performance review, you can upload a company rubric and ask Canvas to draft an initial outline. You can then make inline edits, delete unnecessary sections, insert achievements, ask the model to rephrase specific sentences, and even generate an executive summary. It's particularly useful for tasks like copywriting and coding, allowing for iterative refinement and version control.
What are some useful tips for using the Canvas feature?
- Version control: Use the built-in back and forward buttons to jump between different versions of your document.
- In-line editing shortcuts: Use shortcuts within Canvas to modify the entire document or highlighted snippets. The "suggest edits" feature is especially helpful.
- Download and integrate: Download the final output in Markdown format, then upload to platforms like Google Drive for perfectly formatted Google Docs.
Are there any general commands I can use to improve text-to-text model outputs?
- "Elaborate": Add more detail to a point or section (e.g., "Elaborate on these three bullet points").
- "Critique": Identify problems early in your content or approach (e.g., "I'm arguing for more headcount based on this data. Critique my approach before my presentation").
- "Rewrite": Improve previous content by rephrasing or changing tone (e.g., "Rewrite the second paragraph using a friendly tone of voice").
How frequently are ChatGPT updates released, and how does that impact user experience?
ChatGPT has been rolling out over 1.1 updates per week, making it challenging for users to keep track of all the changes and identify which new features are worthwhile. This rapid evolution means that specific model numbers or features might change quickly. Therefore, the focus should be on understanding the underlying principles of when and how to use different model types and features (like reasoning vs. basic chat models, or features like deep research and canvas) rather than memorizing transient details.
