by Michael Stelzner / June 2, 2026
Are you finding it tough to fight ad fatigue without exhausting your design crew? Curious how a small brand can produce the volume of ad creatives that Meta's algorithm now demands?
In this piece, you'll learn a three-step method for leveraging AI to crank out high-quality image and video ad creatives at scale.
Why Most Marketers Have It Wrong About AI Ad Creative
Two common misunderstandings prevent many marketers from even trying AI tools for ad creation.
The first is the belief that using AI is lazy. Fraser Cottrell, CEO of Fraggell, a direct-to-consumer ad creative agency, disagrees. He argues that getting AI to deliver exactly what you envision in the creative process demands substantial effort.
The second misconception is that AI generates low-quality work. In reality, AI is only as effective as the context and instructions you provide. Current models can produce images that are almost indistinguishable from professional photos. While video models still have room for growth, for static image ads, quality is no longer an obstacle.
The benefit when AI and ad creative work in tandem: it evens the playing field.
For e-commerce brands that used to need a studio shoot or a freelancer for decent product shots, this is a game-changer. Product images that once cost hundreds or thousands of dollars can now be created for mere cents.
Additionally, advertisers need genuinely distinct ad variations, and AI makes this volume achievable without a large team or hefty production budget. This is crucial now because Meta's Andromeda update eliminated the practice of running hundreds of minor tweaks to the same ad. The platform now sees those as a single creative.
However, none of this works without a foundational step: training generative AI on your brand—who your customers are, what your brand represents, and what a great ad looks like.
The aim of the three steps below is to systematically build that context before you ever start using AI tools for ad creation.
#1: Build Your Brand Knowledge Base With Deep Research
The first thing Fraser does when a new brand partners with Fraggell is conduct a deep research session in ChatGPT or Gemini. Deep research is a feature in LLMs that prompts the AI to thoroughly scan the internet before delivering a well-researched document instead of a quick response.
The goal is to create a thorough external view of your brand. Fraser instructs the AI to identify who is buying the product and why, but also—just as importantly—why people who encountered it chose not to buy. He wants to know what people are saying about the product on Reddit, the most frequent complaints and issues, and where the customer base is geographically concentrated. These specific angles uncover the objections and pain points that make an ad creative resonate with real buyers.
Rather than writing the deep research prompt himself, Fraser uses a voice dictation tool called Whisper Flow to speak his request to Claude, asking it to craft a deep research prompt for Google Gemini and specifying which areas to cover.
For the actual research, Fraser most often uses Gemini. Built on Google's data infrastructure, Gemini tends to produce larger, more detailed reports and operates faster. ChatGPT performs well but can sometimes hallucinate.
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The output from Gemini is a substantial document about your brand. It can be several pages long and takes a few minutes to generate. Once you have it, review it for accuracy before using it as a training source.
Fraser's method for quick verification: paste the document into Claude and ask Claude to quiz you on it to confirm the information is correct. Claude goes through the document with questions, and you can clarify or correct on the spot.
This is also the time to add what the internet can't know. The deep research captures publicly available information like reviews, forum discussions, and social mentions. But you have knowledge the AI can't find: proprietary customer insights, internal data, the nuances of how your product actually works, and what your best customers say during calls.
Fraser's instruction is to manually feed that information back into the document to fill in the gaps.
The final version should combine the AI's external research with everything only you know.
#2: Train a Claude Project on Your Brand
Once the deep research document is reviewed and accurate, load it into a Claude Project—a dedicated workspace within Claude with its own persistent memory and context, completely separate from your regular Claude conversations. Think of it as a fresh AI instance that only retains what you've given it. Every conversation within the project draws from that knowledge base, and nothing leaks in from your other Claude chats.
The starting contents Fraser loads into every project are:
Deep Research Document: This is the foundation built in Step 1, reviewed and corrected.
Customer Reviews and Testimonials: Export reviews directly from your store, pull them from a third-party review platform, or export them from a spreadsheet as a CSV file. This voice-of-customer language is some of the most valuable creative input you can give the AI.
An Internal Brand Document: Fraggell has a roughly ten-page internal document that every new team member receives—covering what the agency stands for, who they are, what they do, and what constitutes a good ad. Fraser feeds this same document into every client's Claude project. The definition of a good ad is entirely brand-specific, and making it explicit gives the AI a benchmark to aim for rather than guessing.
Past Ad Performance Data With Visual Context: Export your performance data from Facebook Ad Manager and add it to the project along with visual context.
Fraser takes the top ten performing ads from the past quarter and uses a tool called Poppy, which physically watches short-form video (rather than just transcribing it) and therefore understands visual elements, pacing, and on-screen action. He feeds each video into Poppy along with the performance data and asks it to summarize what made the ad perform the way it did and identify key visual elements.
Those learnings go into the Claude project alongside the numbers.
Pro Tip: As an alternative, Gemini has built-in vision and can watch a downloaded video directly. Upload the video to Gemini and ask it to describe the creative assets, hooks, and anything that stands out visually. Fraser updates this top-ad analysis quarterly.
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#3: Use AI for Ad Creative
With a trained Claude project in place, you can start producing creative. The workflow differs between image and video ads, but the Claude project is central to both.
When you're ready to create the image, Fraser recommends the hybrid approach: generate the image with AI and add the text manually. This way, if you want to test different copy against the same visual, you don't have to regenerate the image every time.
Generate Ad Copy With Claude
Start by opening your Claude project and asking it to generate copy with a prompt like:
Claude will return a set of headlines drawn from the brand knowledge in the project, potentially pulling in customer language from the reviews you uploaded. From that set, identify two you like and two you actively dislike. Tell Claude to give you more like the ones you want and less like the ones you don't—and explain why.
Because conversations within a Claude project compound over time, this feedback trains the AI's future responses. It learns your preferences progressively with each session.
Brainstorm and Generate Ad Images and Prompts With Claude
Once you have strong copy, ask Claude to brainstorm image concepts to accompany it. Give Claude your ideas and let it respond with its own. The more specific the brief, the more targeted the output.
If you already have a creative direction in mind—a specific message you want to hit and a target persona—bring that into the brief. For a sports hydration product, that might mean telling Claude:
If you find an ad from another brand that you admire, you can paste or upload it to Claude and ask it to rewrite the concept for your brand.
Should the image feature the product on a clean background? Drag a photo of your product into Claude, so it knows exactly what it's working with, then describe the shot you want in plain language, such as:
Keep the description fairly brief and invite Claude to ask questions if needed. Claude will write a formatted prompt for your image generation tool that references the product you've provided.
Read through the prompt to confirm it aligns with your direction before taking it to the next step.
Generate Ad Images With Nano Banana in Gemini
Fraggell's current go-to for image generation is Nano Banana 2 Pro, available within Gemini. When using Gemini directly via the chat interface, you'll receive one image per request. Via the API, through third-party platforms like Arcads or Higgsfield, you can generate three or four variations at once.
A few tips Fraser has learned from working inside Gemini:
When saving an image from Gemini, use the download arrow inside the interface to get the full 4K version. Right-clicking and saving gives you a lower-resolution copy.
If you're working via the API, you can pass it specific camera commands, such as zoom out or change the camera angle, and the platform will handle the adjustment cleanly without requiring you to rewrite the entire prompt.
If an initial image isn't right, paste it back into Claude with a description of what needs to change. Claude updates the prompt, and you run the revised version through Gemini.
Once you have an image you're happy with, don't touch it. Going back to tweak even a small detail risks the AI going in a completely different direction and ruining what you had. If the image needs a structural adjustment, like changing the aspect ratio or extending a canvas edge, switch to Photoshop's generative fill instead. It modifies only the targeted area without regenerating the whole image.
Once you have an image you're satisfied with, bring it into your favorite image editing tool and layer the copy on top.
Generate Video Ad Script Drafts With Claude
While Fraggell doesn't produce fully AI-generated videos for clients because the quality of current video-generation tools isn't yet up to the agency's standards, he says AI makes a significant difference in scripting and ideation.
The process uses the same Claude project.
Describe the video concept to Claude: who's in it, the general scenario, and the target length. For a running brand, that might look like:
Claude will return a timestamped, detailed script. If you only need a starting framework, ask for just a paragraph of script rather than full scene-by-scene direction.
The AI-written script isn't a finished product. Human writers understand nuance, conversational tone, and what resonates with other humans in ways AI still struggles to replicate. But AI is excellent at generating a first draft fast, which gets a creative person 30% of the way to the finish line in a fraction of the time.
A copywriter can take that draft, pull the two lines that work, and build from there.
Fraser Cottrell is the CEO of Fraggell, a direct-to-consumer ad creative agency that helps brands acquire customers with diverse ad creative across Meta and TikTok. He teaches through the Ad Creative Course and publishes the Nice Ads newsletter. Follow him on YouTube and X.
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