ChatGPT Doesn’t Eat—So Why Do I Keep Asking It to Cook?

Teams chat, Thursday 9:07 AM
Them: “Let me write out my recipe. One moment.”
Me: “Sounds like a job for ChatGPT—I’m starting a Griddle Recipes notebook in NotebookLM.”
Them: “Yeah, but you gotta make it perfect. ChatGPT doesn’t eat.”

That throw-away line—ChatGPT doesn’t eat—has been ricocheting around my head for days. It’s funny because it’s painfully true: language models can list ingredients, theorize heat zones, even riff on plating aesthetics—but they can’t taste the result. They don’t have tongues, noses, or the satisfied shoulder-drop that comes from a bite of perfect, crispy-edge pancake. No sensory grounding means no lived feedback loop.

The two-week fidelity spiral

Flash back to my latest side quest: refactoring the image carousel on nino.photos. All I wanted was:

1. Portrait-stacking pattern from my working test.html.
2. Swipe support and a tap-to-next fallback.
3. Smarter pre-loading (just the first two images—my lighthouse score was crying for help).

Simple, right? I fire up ChatGPT, paste constraints, and wait for magic. What arrives is… almost there. Margins off by six pixels. Swipe inertia feels like molasses. Pre-loader still drags in all nineteen images because Array.from is buried in a helper the model forgot to rewrite.

Cue the iterative pinball:
“No, move the character anchor to bottom-right, but only on portrait. Nope, you swapped the z-index again. Try removing the global transform—it’s nuking my blur layer.” Twenty minutes turns into ninety. Eventually I rip the code apart by hand, line-by-line, until it behaves.

This is the fidelity gap: the gulf between what an LLM can describe and what it can prove in the real world. The same gap shows up when I ask it to sketch brand systems, draft contract language, or—yes—write recipes. ChatGPT predicts tokens; I need outcomes.

Why we keep reaching anyway

I’m not throwing shade at the model. It’s astonishing at ideation, pattern recognition, and speed—an army of junior assistants who never sleep. But when the work demands tactile nuance—pixel precision, mouthfeel, leadership dynamics in a tense stakeholder room—text predictions alone don’t cut it.

We keep reaching because leverage is intoxicating. I could slog through boilerplate solo, or I could shotgun a prompt and get 80 % of the way there in seconds. That math pencils out—as long as I remember I’m still the chef.

A better recipe for using LLMs (notes to self)

Ingredients

• Clear constraints: exact breakpoints, performance budgets, tone guides.
• Rapid test kitchen: local sandbox, feature flags, throwaway branches.
• Domain context: what success feels like, smells like, looks like.
• Human QA palate: trusted colleagues, my own eyeballs, the prod error log.

Steps

1. Prep the prompt. Lead with non-negotiables and supply a minimal working example.
2. Small-batch outputs. Ask for a function, not a whole app. Taste often.
3. Stage gates. Lint, run, screenshot; circle back before stacking layers.
4. Hand-finish. Align edges, rename variables, sprinkle the cilantro.
5. Reflect and log. What tripped the model? Capture the pattern for next time.

Leadership lens: mirror versus mouthfeel

Quiet leadership—my default mode—is about holding a mirror so teams see their own blind spots. LLMs are mirrors too, just hyperactive ones. They reflect the pattern in our prompts but can’t feel whether the reflection hits. That sensing—that moment where you taste the sauce and know it needs acid—is still squarely human work.

So when frustration spikes, I remind myself: ChatGPT isn’t failing; it’s succeeding at exactly what it is. I’m the one expecting a stand mixer to plate a soufflé.

Closing thought

LLMs can draft the menu, generate the shopping list, even ghost-write the IG caption. But if the dish is going on my table—or my code repo—I’m still tasting the sauce before it leaves the kitchen.

Have your own fidelity-fail war story? Drop me a line. We’ll trade recipes—and maybe commiserate over pancakes that only exist in text.