If you are tired of ChatGPT "apologizing" or Claude "refusing" because your prompt was ambiguous, ditch the language. Use the feed.
Because v2.fewfeed is so good at pattern matching, it has a tendency to "over-fit" to your bad data. If you feed it a biased dataset by accident, the AI doesn't question it—it doubles down .
The future of AI isn't talking to it. It's showing it the receipts.
April 16, 2026
“Act as a data entry specialist. Extract name, email, title. Ignore fluff. Format as JSON…” (Fails because one card says "C-Suite" and another says "Boss Man").
The result? The AI stops trying to "answer" you and starts trying to complete the pattern . I tested v2.fewfeed on a nightmare task: cleaning 10,000 messy business cards.
I fed it 5 examples of clean data. No instructions. No "please." v2.fewfeed
Is v2.fewfeed the Death of the Prompt Engineer? (Or Your New Secret Weapon?)
3 minutes
Disclaimer: This post discusses emerging patterns in LLM architecture. Always validate outputs for production use. If you are tired of ChatGPT "apologizing" or
You know the drill: “Explain it like I’m five.” “No, that’s too simple.” “Do it again, but in the style of Hemingway.”
We’ve been prompting . And frankly, it’s exhausting.
Instead of typing a command, you the model a messy, real-world data structure—usually a JSON blob, a CSV snippet, or a scraped HTML table. You don't tell the AI what you want. You just show it the pattern of the world. If you feed it a biased dataset by