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Use Gemini to Instantly Summarize Google Forms Responses

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Use Gemini to Instantly Summarize Google Forms Responses

As Google Forms collect more responses, manually reading through hundreds or thousands of answers becomes a serious time drain. Here's where Gemini steps in to transform how you analyze feedback. Instead of slogging through every single response, Gemini handles the heavy lifting by distilling responses into concise summaries, spotting recurring themes, clustering similar opinions, and flagging noteworthy comments. The result? Evaluating survey results and making data-driven decisions becomes genuinely manageable. Let's walk through how to leverage Gemini for summarizing Google Forms responses quickly.

What's interesting here is that Gemini essentially becomes your personal research assistant, automatically identifying patterns you might otherwise miss in large datasets.

How to Summarize Google Forms Responses with Gemini

Important note: This feature currently works only with Google Forms displayed in English.

Step 1:

Open your survey form that already contains responses. Navigate to the Responses section to view all the feedback collected.

Phần phản hồi trong Google Forms

Step 2:

You'll see the aggregated response data displayed on this page. Scroll down and you'll find the summary section where Gemini has already processed your answers. Click Summarize responses to view the AI-generated summary.

Tóm tắt câu phản hồi trong Google Forms bằng Gemini

Step 3:

Gemini now displays a high-level overview of participant feedback based on its analysis.

Nội dung tóm tắt phản hồi trong Google Forms bằng Gemini

If you want a different angle on the data, hit Retry to regenerate the summary. Gemini bases this on the most common themes and presents them in clearer language.

Tạo lại tóm tắt phản hồi trong Google Forms bằng Gemini

Reviewing and Validating Gemini's Summary

Once Gemini generates the summary, take time to review it carefully. Make sure all critical information has been captured accurately. While Gemini excels at rapid synthesis, the real concern is that summaries serve as a support tool—they may not encompass every single response or nuance.

When evaluating the summary, watch for:

  • Whether the main points accurately reflect what participants actually said.
  • Spot-checking against original responses to verify accuracy.
  • Standout opinions or exceptional feedback that Gemini might have overlooked.
  • Using the summary as supporting evidence, not the sole basis for major decisions.

This validation step ensures your analysis is solid and prevents you from missing valuable insights.

When Should You Use Response Summaries?

Gemini's summary feature shines in these specific scenarios:

  • Forms with high response volume—saves enormous amounts of reading and synthesis time.
  • Lots of open-ended questions that need quick pattern recognition and trend identification.
  • Preparing survey reports, where you need to highlight key findings before diving into detailed analysis.
  • Rapidly isolating critical issues so you can prioritize which responses deserve closer attention.

For forms with just a handful of responses or mostly multiple-choice questions? You can skip Gemini and review results directly in Google Forms. Matching the right tool to your specific situation means getting better results with less wasted effort.


Description: Learn how to use Gemini AI in Google Forms to automatically summarize feedback and identify key themes from hundreds of responses.

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How to Remove People from Photos in Canva Using AI

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How to Remove People from Photos in Canva Using AI

Canva's Magic Eraser tool has become the go-to solution for anyone wanting to clean up photos by removing unwanted subjects without investing in expensive software like Photoshop. The beauty of this AI-powered feature? It automatically detects what you want to remove and intelligently reconstructs the background behind it, leaving your image looking natural and polished. Just a few clicks, and you're done.

This guide walks you through the entire process of removing people from images using Canva's Magic Eraser. Follow along and you'll produce clean, professional results while saving yourself considerable editing time.

What is Magic Eraser on Canva?

Magic Eraser is an AI-driven photo editing tool built into Canva that lets you effortlessly remove unwanted elements from images—people, objects, text, or any distracting visual noise. The tool works by recognizing the area you want to delete and then automatically regenerating the background beneath it, keeping your photo looking natural without requiring professional-grade editing software.

Canva created this feature to streamline the editing workflow, helping users save time when they need to polish images before publishing them online.

Step-by-Step Guide to Removing People from Photos in Canva

Step 1:

Start by uploading your image into Canva. Magic Eraser is part of Canva's Magic Studio suite—a collection of AI-powered design tools.

Step 2:

In the image editor, click on the Magic Eraser tool.

Magic Eraser on Canva

You'll see two removal options: use a brush to manually outline the person you want to delete, or use the tap-to-select feature to let Magic Eraser identify and highlight subjects for you to remove.

Magic Eraser options on Canva

Step 3:

Choose your removal method. If you select the brush tool, pick your brush size and paint over the person you want to remove, then click Erase.

Selecting and removing people in Canva

Wait a moment while the AI processes. If the result isn't what you wanted, hit the Reset Content button to restore your original image.

Reset original image

Finally, click the save icon to download your edited photo, or click Edit Design to continue refining it in Canva.

Download edited photo from Canva

Here's a side-by-side comparison of the before and after results.

Original image
Image after removing person in Canva

Step 4:

Beyond removing people, you can eliminate any unwanted details from your image using the exact same process.

Original image in Canva

Canva handles removing those extra elements just as effectively.

Removing unwanted details from image in Canva

Tips for Getting the Best Results with AI Removal in Canva

To achieve natural-looking, polished edits when using Magic Eraser, keep these best practices in mind:

Start with high-quality images: High-resolution, sharp photos give the AI better data to work with. It'll accurately identify subjects and regenerate backgrounds that look convincingly natural.

Be precise with your selection: Only mark the area containing the person or object you want removed. Selecting too large an area risks the AI accidentally erasing important details.

Favor simple backgrounds: Images with minimal background detail and fewer overlapping objects produce better results. Complex, cluttered backgrounds make AI removal trickier.

Work in sections on complex images: For photos with lots going on, erase one small area at a time rather than trying to remove an entire subject in one go. This gives the AI better precision.

Combine with Canva's other editing tools: Magic Eraser is fast, but it can struggle with crowded scenes, detailed patterns, or partially hidden subjects. What's interesting here is that using complementary tools in Canva afterward can help you refine the final result and catch any imperfections the AI missed.


Description: Learn how to use Canva's AI-powered Magic Eraser tool to remove unwanted people and objects from your images in minutes.

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10 Essential AI Newsletters That Actually Keep You Current in 2026

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10 Essential AI Newsletters That Actually Keep You Current in 2026

Artificial intelligence moves so fast that traditional news cycles can't keep up anymore. By the time a major publication breaks a story about a new AI model, open-source communities have already analyzed it, fine-tuned it, and integrated it into a dozen different applications. For AI engineers, data scientists, and developers in 2026, email has become one of the most valuable tools for staying ahead—if you're subscribed to the right sources.

Here's the problem though: not all newsletters are created equal. Right now, thousands of AI-generated roundups flood inboxes with recycled content that lacks depth and real utility. If you genuinely want to stay on top of the AI industry, you need curated newsletters built by people who are actively researching, building, or shipping AI products.

Below are 10 of the best AI newsletters available today, organized into four groups based on how you'll actually use them: daily news updates, deep research dives, policy and strategy analysis, and resources for product builders.

Daily AI News Briefings Worth Your Time

If you have five minutes each morning to catch what's happening in AI, these three newsletters approach the job differently. One focuses on market overview. Another prioritizes technical links. A third tackles practical AI application.

The Rundown AI

The Rundown AI currently holds the title of largest AI newsletter globally, with over two million subscribers. Founded by Rowan Cheung, it stands out for razor-sharp clarity and lightning-fast coverage of industry movements.

Each edition summarizes freshly released AI models, new product launches, notable deals, and major industry events in snackable chunks. It's perfect if you want a complete picture of what's happening in AI without spending your day reading technical papers. The format respects your time—no fluff, just signal.

TLDR AI

Where The Rundown AI takes a broad market view, TLDR AI caters to technical audiences. Part of the well-regarded TLDR newsletter family, this version strips out marketing entirely and embraces extreme minimalism.

Each story gets a headline, a few summary sentences, and a direct link to the original source. Rather than dwelling on business news or executive quotes, TLDR AI points you toward research papers on arXiv, GitHub repositories, and technical blogs from research teams.

This is for engineers and ML practitioners who want primary sources, not interpretations.

Superhuman AI

While the first two focus on news and research, Superhuman AI tackles a more pragmatic question: how do I use AI to get better work done today?

Instead of dissecting model architecture, it shares AI tool tutorials, prompt engineering tips, workflow automation strategies, and AI applications you can deploy in a business environment right now.

If you're an office worker, marketer, content creator, or business owner looking to boost productivity with AI rather than debate neural network optimization, this newsletter deserves your attention.

For Serious AI Research Enthusiasts

Want to understand how AI models actually work? Tracking cutting-edge research? Following the latest developments in large language models (LLMs)? These three newsletters are basically required reading.

The Batch

Published by DeepLearning.AI under Andrew Ng's leadership, The Batch ranks among the most respected AI resources available.

Its superpower lies in translating dense research papers into clear analysis without sacrificing accuracy. Weekly issues don't just summarize prominent studies—they include Letters from Andrew Ng, where Ng shares perspective on AI trends, industry shifts, and what deserves your attention. What's interesting here is that it bridges the gap between academic rigor and accessibility.

Perfect for staying current on AI research without drowning in academic papers.

Ahead of AI

Sebastian Raschka authored several respected machine learning books and is a highly regarded AI researcher within the community.

His newsletter Ahead of AI zeroes in almost exclusively on open-source language models, fine-tuning techniques, and model evaluation.

What sets Raschka apart: he doesn't just write about technology—he runs the code before analyzing it. Topics like PEFT (Parameter-Efficient Fine-Tuning), LoRA (Low-Rank Adaptation), and model optimization get explained with university-level detail but written in an approachable blog voice. This is definitely for ML engineers training and deploying open-source AI models in production.

Interconnects

As AI development shifts focus from initial training to post-training optimization, fine-tuning models after their base training phase has become one of the most important technical challenges.

Interconnects is gaining serious respect in AI circles because it tackles exactly this space. Run by Nathan Lambert, a researcher with deep expertise in RLHF (Reinforcement Learning from Human Feedback), the newsletter explores how AI models get refined after initial training, the open-source model ecosystem, model evaluation standards, and the latest optimization techniques.

The real value here is that Lambert bases analysis on experiments he's personally conducted, giving AI researchers genuinely practical insights rather than information synthesis.

Understanding AI Strategy and Policy

AI today isn't just a technology story. It's deeply entangled with policy, geopolitics, and national development strategy.

Import AI

Launched in 2016 by Jack Clark, co-founder of Anthropic, Import AI stands as one of the oldest and most credible voices in AI coverage.

Each week blends research summaries with substantive analysis of compute power, AI policy, AI governance, and how nations are building AI strategy. Here's what makes it distinctive: Clark closes every issue with a science fiction short story inspired by AI—a feature that's become Import AI's signature and has built its own loyal following.

If AGI concerns, AI policy, or global tech competition matter to you, this is essential reading.

The Median

Published by the learning platform DataCamp, The Median takes a different approach entirely.

Rather than just reporting news, it answers a crucial follow-up question: "What should you learn as a result?"

Each edition highlights significant events in data and AI, then recommends courses, tutorials, or resources to help readers quickly plug knowledge gaps. This works exceptionally well for programmers, data scientists, and anyone building a structured AI learning roadmap.

For People Building AI Products

If you're launching an AI startup, building AI agents, or shipping new AI applications, track these two newsletters closely to stay connected to what's actually being built.

Ben's Bites

Want to know which AI startups launched this week or what AI tools are getting buzz? Ben's Bites is almost always first with the news.

This newsletter functions as the hub for the AI startup community, venture investors, and independent builders. It covers new products, demos, AI tools, and creative projects often weeks before major tech outlets catch on. For founders, product managers, and engineers seeking product inspiration, it's invaluable.

Latent Space

If Ben's Bites tilts toward products, Latent Space tilts toward engineering. Run by Swyx, many view it as the magazine for AI engineers.

Articles dig deep into building modern AI applications—API integration, RAG (Retrieval-Augmented Generation), AI agents, vector databases. It also features a podcast interviewing engineers behind projects like LangChain, LlamaIndex, and other major AI frameworks.

The real concern here is that it assumes readers understand code and programming fundamentals, so technical depth is serious.

Choose Quality Over Quantity

In an era where AI shifts almost daily, trying to consume everything is impossible. Instead of chasing hundreds of articles weekly, building a system around quality newsletters filters out the noise and lets you focus on what genuinely matters.

You don't need to subscribe to all 10. A smarter approach: pick one newsletter from each category that matches your priorities. Follow it for a month. Then ask yourself which ones you actually open first. Those are the sources worth keeping long-term.

In a world where AI changes constantly, choosing the right information sources sometimes matters more than consuming the most information.


Description: Skip the noise. Here are the 10 best AI newsletters curated by real researchers and builders, organized by what you actually need to know.

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How to Use Copilot in PowerPoint: A Complete Guide

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How to Use Copilot in PowerPoint: A Complete Guide

Once you've integrated Copilot into Microsoft Office 365, you unlock a powerful assistant for all your office productivity tasks. PowerPoint gets particularly interesting here—Copilot helps you brainstorm creative slide concepts, polish weak content, and build professional presentations in minutes instead of hours. Below, we'll walk through exactly how to leverage Copilot's capabilities in PowerPoint to transform the way you create presentations.

Using Copilot on PowerPoint Online

1. Build an Entire Presentation From a Single Idea

Here's what makes this feature remarkable: just type a brief description, and Copilot handles everything.

  • Generates a complete presentation from scratch
  • Suggests logical titles and content structure
  • Distributes content across slides naturally
  • Writes original text for each slide

Command to create slides using Copilot

Copilot will ask clarifying questions about your preferred style and slide layout approach. Once you confirm, it creates exactly what you requested—with the right number of slides and matching your specifications. Need additional slides with specific content? Just ask, and Copilot adds them with the same professional quality.

Slide content generated by Copilot

2. Convert Word Documents or PDFs Into Slides

This is particularly useful when you've got existing documentation that needs visual presentation. Upload your file or select one from your Office files, and watch Copilot work:

  • Reads through your entire document
  • Extracts and summarizes key information
  • Transforms content into individual slides
  • Preserves logical flow and structure

File upload option for Copilot PowerPoint

You can refine the output by providing additional instructions about how you'd like the slides organized or formatted.

Custom request to generate slides from document using Copilot PowerPoint

The result is a polished presentation that captures the essence of your source material.

Slides generated from document using Copilot PowerPoint

3. Generate Slide Content With Copilot

Got a slide with just a title? Copilot fills in the blanks intelligently:

  • Writes descriptive paragraphs
  • Creates logical bullet points
  • Adds relevant examples
  • Explains complex concepts simply

The key is being specific about what you need on that particular slide so Copilot delivers exactly what fits your narrative.

Writing slide content using Copilot PowerPoint

4. Condense Lengthy Slide Text

When a slide feels text-heavy and needs trimming, let Copilot streamline it:

  • Compresses verbose passages without losing meaning
  • Converts paragraphs into concise bullets
  • Maintains impact while reducing word count

5. Rephrase Existing Slide Content

Already have content but want to adjust the tone or style? Copilot handles various approaches:

  • Elevates language to a more professional register
  • Makes text punchier and more concise
  • Simplifies complex terminology
  • Adapts tone for specific audiences

6. Generate Diagrams and Charts

Copilot can create or suggest visual representations including:

  • SmartArt graphics
  • Timeline visualizations
  • Process flows
  • Organizational charts
  • Comparison diagrams
  • Bulleted lists in visual format

For example, you might request: "Create a SmartArt diagram showing the recruitment process."

What's interesting here is that Copilot doesn't just generate random diagrams—it researches your topic to ensure the diagram structure and styling match your subject matter perfectly.

Generating diagrams and charts in slides using Copilot PowerPoint

Useful Prompts for Copilot in PowerPoint Online

Here are battle-tested commands that work exceptionally well with Copilot in PowerPoint Online:

Creating New Presentations

Goal: Quickly generate a full presentation from a topic.

Create an 8-slide presentation on digital transformation in business.

Create a company introduction presentation for new clients about Company ABC.

Develop a cybersecurity training presentation for office staff.

Building Presentations From Documents

Goal: Transform existing Word or PDF documents into slide decks.

Convert this Word document into a presentation and condense it to 10 slides.

Turn this report into a presentation suitable for executive leadership.

Create slides from this document that highlight the three main findings.

Writing Slide Content

Goal: Add substance to title-only slides.

Write content for the slide titled "Benefits of Cloud Computing".

Add 5 key talking points to the "Project Goals" slide.

Provide a brief explanation of artificial intelligence in simple terms.

Summarizing Content

Goal: Compress lengthy text to fit a slide format.

Condense this slide into 5 main bullet points.

Shorten this paragraph to fit on a single slide.

Convert this text block into a bulleted list.

Revising and Rephrasing

Goal: Adjust tone and style of existing content.

Rewrite this content in a professional tone.

Make this shorter and easier to understand.

Rephrase this for a high school audience.

Convert this to formal business language.

Design and Layout

Goal: Improve the visual presentation structure.

Suggest an appropriate layout for this presentation.

Split this slide into 3 separate slides for better pacing.

Rearrange these slides in logical order.

Apply a modern design aesthetic to this presentation.

Adding Visual Elements

Goal: Make presentations more visually engaging.

Suggest appropriate images for each slide.

Add icons that represent each main point.

Recommend illustrations related to digital transformation.

Creating Charts and Diagrams

Goal: Visualize data and processes.

Create a chart showing quarterly revenue by division.

Convert this description into a process flowchart.

Add a SmartArt diagram illustrating the hiring workflow.

Editing and Refinement

Goal: Speed up revisions and improvements.

Add an introductory slide at the beginning.

Remove duplicate content throughout the presentation.

Reduce this from 20 slides down to 10 slides.

Add real-world examples to support each section.

Presentation Preparation

Goal: Support the speaker with notes and frameworks.

Create speaker notes for each slide.

Write a strong conclusion for this presentation.

Suggest questions the audience might ask.

Write a 1-minute opening statement.

Translation and Language Adaptation

Goal: Localize presentations for different audiences.

Translate the entire presentation to English.

Translate this slide to Japanese.

Convert content to English while maintaining the layout.

Optimization and Enhancement

Goal: Polish and improve overall quality.

Check the entire presentation for spelling and grammar errors.

Suggest ways to make this presentation more engaging.

Reduce text on each slide while keeping the key information.

Review this presentation and suggest improvements.

Pro Tip: Structuring Your Prompts Effectively

To get the best results from Copilot, structure your commands as: Action + Topic + Quantity/Audience + Style

Here are well-structured examples:

  • Create a 10-slide presentation about AI in education aimed at teachers, using a modern design approach.
  • Summarize this report into 6 slides with concise bullet points that are easy to scan.
  • Rewrite this content in professional language suitable for presentation to senior management.

Getting Started With Copilot in Desktop PowerPoint

Step 1: Launch Copilot

Open PowerPoint and click the Copilot icon to open the Copilot panel. Select Create a presentation, then enter your topic or idea. You can also use the microphone to dictate if you prefer voice input. When ready, hit the send button to submit your request.

Using Copilot in PowerPoint

Step 2: Copilot Builds Your First Draft

Copilot immediately starts generating content. Within moments, you'll have a foundational presentation with multiple slides featuring coordinated images, text, consistent layout, and unified theme.

Slides created by Copilot in PowerPoint

Step 3: Add More Slides

In the Copilot panel, click Add a slide about, then describe what content you want on the new slide. Copilot will insert the slide right after your current position.

Specifying new slide content for Copilot

Step 4: Organize Your Slides

Copilot adds the new slide to your deck. You retain full control to rearrange slides in whatever order works best for your narrative flow.

New slide added to PowerPoint

Step 5: Insert Images

When you need imagery, describe what images you want and which slide should contain them. Be specific about slide numbers or simply work with the currently selected slide.

Adding images to slides

Step 6: Generate Presentation Summary

Want a high-level overview? Simply click Summarize this presentation and let Copilot work.

Summarizing slides in PowerPoint

Copilot generates a comprehensive summary along with relevant quotes from your presentation, giving you quick reference material.

Summary content created by Copilot

Pro tip: Click the View Prompt icon for additional options and refinement tools.

Copilot prompt viewing options


Description: Master Copilot in PowerPoint with our step-by-step guide. Learn to create presentations, generate content, and design slides faster.

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Claude Opus 4.7 vs GPT 5.5: Which AI Coder Actually Wins in Real-World Testing?

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Claude Opus 4.7 vs GPT 5.5: Which AI Coder Actually Wins in Real-World Testing?

We finally have real data. DeepSuite — the first benchmarking tool that actually measures what developers experience in practice — has put Claude Opus 4.7 and GPT 5.5 head-to-head. The results are more nuanced than the hype suggests. Let's break down which model wins where, and more importantly, when you should use each one.

Claude Opus 4.7: Strengths and Limitations

Claude Opus 4.7 represents Anthropic's most capable coding-focused model in the Opus line. It inherits exceptional long-context reasoning abilities from its predecessors, then layers on improved instruction-following and multi-step planning — both essential for tackling complex programming challenges.

Where Claude Opus 4.7 Excels

Codebase comprehension is Claude's clearest competitive advantage on DeepSuite. Hand it a massive, unfamiliar repository — say a 40,000-token monorepo — and it consistently generates architecture summaries that senior engineers rate as both accurate and well-organized. It tracks file dependencies without losing context, something weaker attention-based models struggle with.

Bug diagnosis is another area where Opus 4.7 pulls ahead. In DeepSuite's debugging tasks, it identifies root causes (not just surface symptoms) at higher rates than GPT 5.5, particularly for logic errors involving state management and async flows. Claude's explanations are detailed enough to use as internal documentation.

Constrained refactoring also favors Claude. When given explicit style guidelines or architectural rules, it adheres to them more consistently than GPT 5.5. For enterprise teams with strict coding standards, this matters.

Where Claude Opus 4.7 Falls Behind

Speed is slower. Opus 4.7 takes roughly 20-35% longer to complete complex coding tasks compared to GPT 5.5. For repetitive work where you're waiting for responses in a chat interface, that latency compounds quickly.

Cost is higher. Claude Opus 4.7 carries a premium price tag compared to earlier versions. For tasks requiring long context windows — like generating multiple files — the cost delta versus GPT 5.5 becomes significant at scale.

GPT 5.5: Strengths and Weaknesses

GPT 5.5 reflects OpenAI's relentless push toward speed without sacrificing quality. It's a meaningful step forward from GPT-4o in code generation, with notable improvements in multi-file consistency and test writing.

Where GPT 5.5 Excels

Speed is GPT 5.5's most obvious advantage. On most DeepSuite tasks, it returns results 25–40% faster than Claude Opus 4.7. In real-world development — especially interactive coding sessions — this speed difference genuinely affects how often developers feel willing to ask follow-up questions.

Test writing is where GPT 5.5 demonstrates its strongest performance in the benchmark. It generates more comprehensive test suites, catches more edge cases, and structures tests in ways reviewers consistently rate as "production-ready" or close to it. This likely reflects OpenAI's training data composition, but the results are consistent.

Multi-file generation is slightly stronger for GPT 5.5 on raw pass rates. When implementing a feature across multiple files, it tends to keep changes internally consistent, though reviewers noted it sometimes introduces patterns that diverge from the existing codebase style.

Lower cost for most workloads. GPT 5.5's token pricing makes it more economical for high-volume programming workflows, especially when running agents that hit the model dozens or hundreds of times per task.

Where GPT 5.5 Falls Behind

Deep error diagnosis is a relative weakness. GPT 5.5 finds bugs — it's not bad — but tends to fix the immediate symptom without fully resolving the underlying cause. On DeepSuite's debugging tasks, its output lands in the "technically correct but incomplete fix" category.

Documentation quality lags Claude. GPT 5.5 tends to generate accurate but generic documentation — technically correct but not illuminating for developers who weren't present when the code was written. Claude's documentation scores higher on the metric that actually matters: "would a future reader find this genuinely useful?"

Head-to-Head: DeepSuite Results by Category

Here's how both models stack up across DeepSuite's six categories, based on combined automated and human evaluation scores:

Category Claude Opus 4.7 GPT 5.5 Winner
Codebase Understanding 87.4% 81.2% Claude
Multi-File Generation 79.1% 82.6% GPT
Bug Detection and Fixing 83.7% 76.9% Claude
Constrained Code Refactoring 85.2% 79.4% Claude
Test Writing 76.3% 84.8% GPT
Documentation 88.1% 78.3% Claude

Overall DeepSuite Score: Claude Opus 4.7 leads overall (83.3% vs 80.5%), but GPT 5.5 wins in two categories — test writing and multi-file generation — both heavy hitters in typical daily programming work.

The gap is closer than many predicted. Neither model dominates completely. What's interesting here is that both have legitimate strengths depending on what you're actually trying to build.

Speed and Cost: The Numbers That Matter at Scale

Benchmark accuracy matters, but speed and cost often determine which model developers actually reach for in practice.

Latency Comparison

Across typical DeepSuite tasks:

  • Short coding tasks (<500 token output): GPT 5.5 averages ~2.1 seconds, Claude Opus 4.7 averages ~3.4 seconds
  • Medium tasks (500–2,000 token output): GPT 5.5 averages ~6.8 seconds, Claude Opus 4.7 averages ~9.2 seconds
  • Long tasks (2,000+ token output): GPT 5.5 averages ~14.3 seconds, Claude Opus 4.7 averages ~19.1 seconds

For interactive coding assistants, this matters. A 3-second response feels instant. A 9-second response breaks your flow.

Cost Per Task

Exact pricing shifts regularly, but across DeepSuite's task distribution, GPT 5.5 costs roughly 30-40% less per task than Claude Opus 4.7 for equivalent output length. For teams running AI coding agents at scale — processing thousands of tasks daily — that difference adds up fast.

When the Cost Premium Makes Sense

Claude Opus 4.7's higher cost is easier to justify when:

  • Tasks require deep understanding of large, complex codebases
  • Bug-finding accuracy is critical (the cost of a missed bug exceeds the cost of a better model)
  • Code documentation quality matters (open-source projects, regulated industries)
  • Teams need consistent compliance with strict coding standards

GPT 5.5's speed and cost advantages win when:

  • Developer iteration speed is the top priority
  • Test writing is your primary use case
  • Volume and per-task cost are constraints
  • Tasks are well-defined and don't require deep contextual reasoning

Claude Opus 4.7 or GPT 5.5? A Task-Based Guide

Rather than declaring an overall winner, here's a task-by-task guide based on DeepSuite results and real developer feedback:

Reach for Claude Opus 4.7 when:

  • Onboarding into a new codebase
  • Finding and explaining subtle bugs, especially in async/stateful code
  • Writing documentation people will actually read
  • Refactoring when you need to follow strict style guidelines
  • Any task where you need detailed explanations of the why, not just the what

Reach for GPT 5.5 when:

  • Writing test suites, especially when you need broad exception coverage fast
  • Generating bulk code where speed is critical
  • Interactive coding sessions where latency affects your workflow
  • Implementing multi-file features with clear specifications
  • Cost-sensitive pipelines running many tasks daily

Here's the real take for most teams: the answer isn't "pick one" — it's "use both strategically." Build workflows that route tasks to the right model based on task type. That's exactly the problem platforms like MindStudio exist to solve.


Description: DeepSuite benchmark reveals how Claude Opus 4.7 and GPT 5.5 compare on speed, cost, and code quality. Which model should you actually use?

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Using Gemini in Google Docs: AI-Powered Writing That Actually Saves Time

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Using Gemini in Google Docs: AI-Powered Writing That Actually Saves Time

Google Docs has evolved far beyond a simple online text editor. Google has integrated Gemini—its powerful AI assistant—directly into the platform, transforming how we write. With natural language understanding at its core, Gemini can help you draft content, rewrite passages, fix grammar, summarize information, and develop ideas with just a few simple commands. What's interesting here is how seamlessly it works within your existing workflow.

Instead of staring at a blank page wondering how to start a blog post, email, or report, you can now ask Gemini for instant assistance right inside your document. This fundamentally changes how writers approach content creation. In this guide, we'll walk you through using Gemini in Google Docs to generate AI-powered text from start to finish.

How to Create Text in Google Docs Using Gemini

Step 1:

Open Google Docs and click on the Gemini option, then select Create a new document.

Create a new Google Docs document

At the bottom of your document, you'll see Gemini's text generation tool. Here's where you describe what you want to create. Enter your request in English to generate content, which can then be displayed in Vietnamese or any language you prefer.

Request text generation in Google Docs using Gemini

Step 2:

After submitting your idea, Gemini generates content that matches your specifications. Once you have this initial draft, you can customize the formatting to your liking.

Google Docs document created by Gemini

You can ask Gemini to adjust the tone, edit specific sections, or even generate new content like images for your document to enhance your work. Common edits include:

  • Rewriting sentences for clarity
  • Fixing spelling and grammar errors
  • Making content more professional
  • Condensing text for brevity
  • Expanding ideas with additional details

Edit Google Docs content created by Gemini

Gemini regenerates the text completely, showing new content in blue and striking through the old version so you can easily compare. When you're satisfied with the revision, click Accept all.

Review changes to Google Docs content from Gemini

Step 3:

Here's a clever feature: Gemini can mirror the formatting of existing documents to create similar documents on new topics. Click Match document format and select a Docs file to use as your style guide.

Match document format in Google Docs

Need to switch to a different template? Click the Match document format icon and select Choose a different document.

Match a new document format in Google Docs

You can also choose a template document so that Gemini generates new content following that style reference.

Create content matching a template style in Google Docs

Step 4:

When you highlight text in your document, Gemini offers additional customization options in the More options menu. Simply click your desired edit and Gemini applies it instantly, or type a custom instruction for more specific changes.

Customize Google Docs content using Gemini

A summary version of your content also appears below, letting you decide whether to use it. If you want to replace your current text with the summary, just click Accept.

Additional customization options in Google Docs with Gemini

How to Write Better Gemini Prompts for Google Docs

An effective text generation prompt typically includes five core elements:

Action (What you want) + Topic (Subject) + Audience (Who it's for) + Style (Tone) + Length (How long)

Keep these best practices in mind:

  • Be as specific as possible in your request
  • Define a clear role for Gemini: Assigning specific roles helps Gemini match the right tone and perspective
  • Specify your desired output format: Gemini will structure content as lists, tables, or outlines rather than generic paragraphs
  • Request multiple rounds of refinement

After Gemini creates your initial content, continue refining with requests like:

  • Rewrite this in a more professional tone
  • Condense this to the main points and make it easier to scan
  • Expand this section and add real-world examples
  • Polish these sentences to improve flow and readability
  • Make the introduction more compelling to grab reader attention
  • Adjust the tone to suit a corporate audience
  • Convert this into a bulleted list format
  • Summarize this document down to the five most important takeaways
  • Check for spelling, grammar, and suggest ways to improve the writing
  • Rewrite this for beginners using simpler language

Description: Learn how to harness Gemini's AI capabilities in Google Docs to generate, edit, and refine content faster than ever before.

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Complete Guide to Building Google Docs Tables with Gemini

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Complete Guide to Building Google Docs Tables with Gemini

Beyond basic content creation and editing, Gemini in Google Docs can generate polished tables in seconds using straightforward natural language prompts. No manual formatting needed.

Gemini adapts to whatever table structure you need. Just describe your purpose—whether you need a project tracker, comparison chart, or data list—specify your column count, and the AI suggests an appropriate layout ready to insert directly into your document. Here's everything you need to know about building tables with Gemini in Google Docs.

How to Create Tables in Google Docs with Gemini

Step 1:

Click the Gemini icon in your Docs interface and describe the table you want to create.

Request to create a table in Docs using Gemini

Wait a moment and your table appears directly in the Google Docs editor.

Table in Docs created by Gemini

Fill in the table cells with your content to match your original specifications.

Edit table content in Docs created by Gemini

Step 2:

Once your table is created, you can ask Gemini to modify it—add columns, reorganize data, whatever you need. The clearer your request, the better Gemini executes it.

Edit columns in table created by Gemini in Docs

If you're happy with the revised table, click Accept to replace the old version. If not, select Reject and try again.

Edit table content in Docs created by Gemini

Here are other table operations you can request:

  • Add new columns.
  • Remove unnecessary columns.
  • Insert additional data.
  • Reorganize table structure.
  • Rewrite headers.
  • Improve readability.

Step 3:

Highlight any data in the table and you'll see options to refine content if needed.

Content options for table in Docs created by Gemini

Converting Text into Tables with Gemini

Gemini isn't limited to creating tables from scratch. It can also transform existing text into structured tables. What's interesting here is how useful this becomes when you're dealing with documents containing lists, comparisons, or unorganized data that begs for better structure.

Highlight the text you want to convert into a table, then use this prompt.

Convert this paragraph into a clearly structured table.

Other useful commands:

Organize information from the above content into a table with appropriate columns.

  • Transform this list into a table with columns for: [specify column names]
  • Analyze the content and create a comparison table between items.
  • Restructure this content as a table for easier tracking.

Command to convert text into table in Docs using Gemini

Gemini generates and inserts the table at your original content location. Click Accept to place it into your document.

Content options for table in Docs created by Gemini

Effective Gemini Prompts for Google Docs Tables

To get Gemini to build exactly what you need, describe your content clearly, specify column requirements, mention what data should appear, and explain the table's purpose.

Simple task tracking table

Create a task tracking table with columns: Task Name, Owner, Deadline, Status.

Weekly project plan table

Create a one-week project plan table with columns: Day, Tasks, Assigned To, Results.

Feature comparison table

Create a comparison table between Google Docs and Microsoft Word using criteria: Features, Advantages, Disadvantages, and Cost.

Product inventory table

Create a product list table with columns: Product Name, Price, Quantity, Description.

Or:

Convert this customer list into a table with columns: Full Name, Phone, Email, Notes.

Converting existing content

Transform the above content into a table with columns organized for easy reference.

Or:

Analyze this paragraph and create a summary table highlighting key information.

Purpose-specific table formats

You can also request tables designed for specific business needs.

Example:

Create a revenue report table with columns: Month, Revenue, Expenses, Profit, Notes.

Or:

Create a class schedule table with columns: Day, Subject, Time, Room Number.

Tips for writing prompts that Gemini understands better

  • State the exact number of columns you need.
  • Name each column explicitly.
  • Explain what the table will be used for.
  • Request additions or removals if the result isn't quite right.
  • Feel free to ask for revisions after the table is generated.

Description: Learn how to create, edit, and convert text into structured tables in Google Docs using Gemini AI with simple commands.

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