How to Use GPT-5 to Extract Information From Videos

8/13/2025

The Ultimate Guide to Using GPT-5 for Extracting Information from Videos

What’s up, everyone? Let's talk about something that's honestly been on my mind a lot lately: video. We’re all drowning in it, right? From endless social media feeds to critical business intelligence locked away in meeting recordings & security footage. Actually using the information stuck inside all that video content has always been a HUGE pain. Until now, that is.

The recent release of OpenAI's GPT-5 is a genuine game-changer. I'm not just talking about another incremental update here; this is a serious leap forward, especially when it comes to understanding more than just text. Its new visual perception capabilities are off the charts, which means we can finally start to think about AI not just watching videos, but truly understanding them.

So, how do we actually do it? How do we use this powerful new AI to pull valuable, actionable information out of a video file? It's not quite as simple as just uploading a movie & asking, "What happened?"... at least not yet. But we're getting surprisingly close. Here’s the deep dive on how it works, what’s possible today, & what’s just around the corner.

First Off, Why Is Video So Hard for AI to Understand?

Before we jump into the "how," it's important to get why this is such a big deal. For years, AI has been great with text. But video is a different beast entirely. It's not just one type of data; it's a whole bunch of them layered together. This is what the experts call "multimodal data."

Think about it. A single video contains:

Visual Data: The actual moving images, objects, people, & scenes.
Audio Data: Spoken words, background noise, music, & tone of voice.
Textual Data: Subtitles, on-screen text, or even text that appears on objects within the video.

To truly understand a video, an AI needs to process all of these things at once & understand how they relate to each other. A person saying "I'm fine" can mean WILDLY different things depending on their facial expression & tone of voice. That's where multimodal AI comes in, & it’s the secret sauce that makes video analysis possible.

The GPT-5 Era: What’s Different This Time?

So, what makes GPT-5 the key to unlocking all this? A few things, really.

First, its enhanced visual perception is a massive step up. Early models could identify basic objects, but GPT-5 is much better at understanding context, relationships between objects, & even subtle visual cues.

Second is its improved tool intelligence. This is a BIG one. GPT-5 is much better at using other software tools to get a job done. This means it can, for example, call on a specialized audio transcription tool, then a video frame analysis tool, & then combine the results to form a comprehensive summary. It’s like a brilliant project manager that can delegate tasks to a team of specialists.

Finally, it has a much larger context window—up to 256,000 tokens, to be exact. In simple terms, this means it can "remember" a lot more information at once. This is critical for long videos, where you need to track events & conversations over an extended period.

There's even a "minimal reasoning mode" that's specifically designed for tasks like data extraction, which means it can pull out information quickly & efficiently without getting bogged down in deep, complex thought.

A Step-by-Step Guide: How to Extract Information from Videos Using AI

Alright, let's get practical. While there isn't a single "Upload Video Here" button for GPT-5 (yet!), the process will likely look something like this, either done through a developer using the API or, more likely, through user-friendly platforms that will build on top of GPT-5's technology.

Step 1: The Pre-Processing & Multimodal Breakdown

The first thing that needs to happen is breaking the video down into its core components. This is the "multimodal" part in action.

Video to Frames: The video file is split into thousands of individual still images (frames). Specialized computer vision models, likely powered by something like GPT-5's visual capabilities, will then analyze these frames to identify objects, scenes, people, & actions.
Audio to Text: The audio track is separated & fed into a speech-to-text transcription service. This will create a full, time-stamped transcript of everything that was said in the video.
Text Recognition (OCR): The AI scans the video frames for any visible text—on signs, computer screens, t-shirts, you name it—and extracts it using Optical Character Recognition.

Step 2: The "Smart" Analysis & Feature Extraction

Now that we have all this raw data (images, transcribed text, recognized text), the real magic begins. This is where a powerful model like GPT-5 will start to connect the dots.

It's not just about what's in the data, but what the data means. For example, the AI can now perform:

Sentiment Analysis: By combining the words spoken, the tone of voice, & the facial expressions, the AI can determine the sentiment of the people in the video. Is the customer in this feedback video genuinely happy or being sarcastic?
Object & Action Tracking: The AI can track a specific object or person throughout the video. Imagine asking, "Show me every time the new product logo appears in this marketing video" or "When did the worker in the yellow vest enter the restricted area?"
Event Detection: The AI can be trained to look for specific events. In a security context, this could be "alert me if a car is parked in the fire lane for more than five minutes." In a marketing context, it could be "find the moment the audience starts laughing in this focus group recording."

Step 3: The Synthesis & Information Extraction

This is the final & most important step. All the analyzed data from Step 2 is fed into a large language model like GPT-5 to be synthesized into a coherent, human-readable format. This is where you, the user, finally get to ask your questions.

You could ask things like:

"Generate a bullet-point summary of the key decisions made in this two-hour project meeting."
"What were the main customer complaints mentioned in this product review video?"
"Create a table of all the speakers in this webinar, the topics they discussed, & the timestamps for when they spoke."
"What was the overall sentiment of the crowd during the product reveal?"

The AI will then use all the data it has gathered—the transcript, the visual analysis, the sentiment scores—to answer your question in detail, providing you with the exact information you need, complete with timestamps or even short video clips of the relevant moments.

Real-World Applications: Who Will Use This?

This technology isn't just for tech nerds; it has the potential to revolutionize how businesses operate. We're already seeing specialized AI video analytics platforms emerge for industries like retail & security, but with the power of models like GPT-5, this will become much more widespread.

Marketing & Customer Research: Imagine being able to analyze hundreds of customer interview videos in minutes to find common themes & direct quotes. Or being able to pinpoint the exact moment viewers lose interest in your video ads.
Internal Communications & Training: Companies can automatically create summaries & searchable transcripts of all their town hall meetings, training sessions, & presentations. New hires could simply ask an AI, "What was the CEO's main message about our Q3 goals?" & get an instant answer with video clips.
Website Engagement & Customer Support: This is where things get REALLY interesting. Imagine having a customer on your website who is confused about a product. Instead of just a standard text-based chatbot, you could have an AI assistant that can pull up the exact clip from a product demo video that explains the feature they're asking about.

This is actually something we're incredibly excited about at Arsturn. We help businesses build no-code AI chatbots that are trained on their own data. Right now, that's mostly text-based content like help articles & website pages. But as this technology evolves, you can see a future where Arsturn chatbots could be trained on a company's entire library of video tutorials & webinars. A customer could ask a complex question, & the Arsturn chatbot could instantly provide a text answer and a link to the precise moment in a video that shows them how to solve their problem. It's about providing instant, highly relevant support, 24/7, & video is the next frontier for that.

Security & Compliance: Security teams can move from passively watching camera feeds to actively being alerted about potential issues, like a person entering a restricted area or a safety protocol being ignored.

The Challenges & The Future

Of course, it's not all sunshine & roses. There are still challenges to overcome. The computational power needed to process all this data is immense, & there are always concerns about privacy & the ethical use of this kind of analysis. We'll also likely see some debate around the accuracy of these models, as some users have already pointed out inconsistencies in early versions.

But the trajectory is clear. The future of video is not just about creating & consuming it; it's about understanding it. As AI models like GPT-5 become more powerful & accessible, the ability to extract information from video will become a standard tool for businesses & individuals alike. The manual, time-consuming process of watching hours of footage to find a single piece of information is coming to an end.

We're moving into an era where every video is a searchable, queryable database of information. It's a pretty exciting time.

Hope this was helpful & gives you a good sense of where we're headed. Let me know what you think