Streamlining Content Creation With Claude Projects
Most people use AI in "Zero-Shot" mode. This means they simply ask Claude for what they want in a chat (e.g., "Write a blog post about marketing") without providing any examples. This forces the AI to guess your preferred style, often resulting in generic, robotic output.
Casey Meehan relies on a method he calls Few-Shot Learning with Claude Projects.
Few-Shot Learning means providing the AI with a small set of carefully selected examples (usually 3–10) before asking it to perform a task. Instead of describing your tone with adjectives like "professional" or "witty" (which the AI might misinterpret), you show the AI exactly what you want.
The Few-Shot IPO Content Creation Framework for Claude Projects
Here's a look at Casey's workflow:
Notion serves as Casey's central command center and energy management tool. He maintains a dedicated page for his weekly schedule that contains direct hyperlinks to his specific Claude projects. This setup is designed to reduce friction; regardless of his energy level or mindset, he simply clicks the link for the task at hand (e.g., "Generate Titles") and is immediately transported into the correct project with the right context, allowing him to start working instantly.
Claude Projects function as the engine of his production assembly line. Rather than treating Claude as a generic chatbot, he builds distinct Projects for each stage of content creation—such as a "Title Bot," "Hook Creator," or "Critique Bot". Each Project houses a specific knowledge base containing examples of his best-performing work (the Few-Shot method), allowing the AI to reference his unique style and data to generate high-quality, brand-specific assets.
With this setup, he is able to consistently publish on-brand videos and repurpose those videos for ebooks without losing focus or feeling overwhelmed.
To build these Projects effectively, you should use the IPO Framework:
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Input: The raw material you give the AI (e.g., a messy transcript).
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Process: The specific instructions + your Few-Shot Knowledge Base. Your Few-Shot examples must be clean and consistent. A good shot file usually looks like this:
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Context: A brief note on what the content is (e.g., "A LinkedIn post about SEO").
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The Content: The actual high-quality text you previously wrote.
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The Logic (Optional): A short sentence explaining why this piece worked (e.g., "Note how the hook uses a contrarian opinion to grab attention").
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Output: The clear deliverable you expect (e.g., a polished article).
Here is how to set up this workflow for your own production process.
Step 1: The Title and Thumbnail Generator
Casey combines his title and thumbnail creation in a single project for a strategic reason: he views the thumbnail text and the video title as a single, cohesive unit.
The text on the thumbnail acts as the main headline, while the video title functions more like a subhead that pulls the viewer in. By generating them together, the AI ensures the two elements complement each other rather than repeating the same information or clashing in tone.
Here's how to set up a project to generate concepts that match your historical winners.
First, create a new Claude Project.
In the Project's instruction box, type:
The user will input ideas for a video. You will generate 10 titles and thumbnail ideas based on the patterns found in the files in your Knowledge Base.
In the knowledge base, give your project the few-shot examples it needs to give you on-brand content. Create a document titled Best_Titles.txt. Inside, list 10 of your best-performing titles. Next to each title, add a one-sentence description of the thumbnail that pairs with it. Upload this document to the knowledge base.
Pro Tip: Do not use artistic jargon for the thumbnail descriptions. Instead, focus on the text on the screen (e.g., "Text says: 'Stop Doing This'").
To use your newly-trained project, begin with your input. Type in a rough topic, like "email marketing tips."
Claude will output 10+ options that follow the exact structure of your best performers.
Step 4: The Critique Bot
This project acts as a virtual editor that checks your work before you publish.
Your system instructions are:
Act as a critic. Compare the user's transcript against the successful examples in your Knowledge Base and identify weaknesses.
In your knowledge base, upload the full transcripts of your absolute best videos or articles—the ones that got the most engagement or sales.
To use your project, input the raw transcript of the content you just recorded. Next, ask:
What is missing from my new transcript compared to my best examples?
The AI output might tell you that your intro is too long or that you forgot your signature sign-off, allowing you to edit and adjust the video before publishing.
Other topics discussed include:
Today's advice provided with insights from Casey Meehan, a featured guest on the AI Explored podcast.
Watch the full interview on YouTube P.S. Add
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Opted in on: 2021-09-06 17:03:43 UTC.
