Debugging Bad AI Outputs

We’re living in a time when AI tools like ChatGPT and MidJourney are either hailed as creative sidekicks or condemned as the death knell for human originality — and honestly, both sides have a point.

I believe that with solid ethical guardrails, generative AI can be an extraordinary force-multiplier — but only if we learn how to talk to it properly, which starts with crafting purposeful, precise prompts.

Identify If The Prompt Was Too Broad Or Ambiguous

Many AI fiascos happen because the initial prompt is like tossing a vague wish into a genie’s lamp — “Give me something cool” — and being shocked when the genie hands you a flaming pineapple.

Whether in ChatGPT or MidJourney, overly broad prompts invite the AI to freestyle, pulling in irrelevant tangents or unexpected styles because it’s trying to “fill in the gaps.”

One of the simplest debugging steps is to examine your prompt and explicitly define your intent: who the output is for, what format you want, what to avoid, and how much creative liberty the AI should take.

Case Study: Flaming Pineapple Prompt In MidJourney

SITUATION
I was designing a blog cover in MidJourney and typed, “Create a dramatic scene showing innovation in technology.”
TASK
I wanted a clean, modern graphic novel image suitable for a business article.
ACTION
The prompt was too broad, and MidJourney returned an exploding neon pineapple with circuit board leaves. I rewrote the prompt fragment-style, specifying: “graphic novel style, elegant linework, modern office, people collaborating over digital screens, intense focus, avoiding surreal fruit imagery.”
RESULT
The output matched the business context perfectly — no pineapples, just sleek, purposeful visuals ready for publication.

Check Whether Context Or Prior Messages Are Confusing The AI

One of the trickiest issues with AI tools is that they remember — sometimes too well — the messy trail of context from earlier messages or prompt fragments.

You might ask ChatGPT for a summary and accidentally inherit biases from a previous conversation about an unrelated topic, or in MidJourney, subtle carry-over from Remix Mode can warp your intended visual style.

A practical debugging tactic is to isolate your query into a fresh session or explicitly restate the context, giving the model a clean slate rather than assuming it knows what you mean.

Case Study: ChatGPT And The Phantom Novel

SITUATION
I was writing a product strategy document in ChatGPT after earlier chatting about gothic novels.
TASK

I needed a sober, business-style summary of a product roadmap.

ACTION
ChatGPT’s first attempt opened with, “In the shadow of thunderclouds, our Q3 strategy broods like a Victorian estate.” I started a fresh session, pasted only the relevant business content, and specified the tone I needed.
RESULT
The next draft dropped the spooky metaphors and read like a concise corporate report — exactly as intended.

## Test Smaller Chunks Of Text Before Large Asks

Many people overload AI tools, dropping in giant chunks of text or sprawling prompts, expecting one perfect answer — and then wonder why the output reads like a blender set to purée.

In both ChatGPT and MidJourney, big asks often hide smaller failures: sections that confuse the AI, instructions that conflict, or syntax that throws the model off balance.

Debugging effectively means breaking your job into smaller pieces: e.g. generating an outline before writing a full article in ChatGPT, or testing MidJourney prompt fragments before composing the entire scene.

Case Study: The Wall Of Text Fiasco In ChatGPT

SITUATION
I once fed ChatGPT a 3,000-word blog draft and asked it to “improve this and make it witty.”
TASK
My goal was a sharper, more engaging piece for publication.
ACTION
The AI produced a half-baked mishmash of jokes, dropped key points, and invented new sections. Instead, I split the draft into sections: intro, body, conclusion. For each, I asked for specific improvements, then stitched the parts together myself.
RESULT
The final piece retained my original ideas, incorporated clever humour, and avoided AI’s tendency to hallucinate or lose coherence over long texts.

Conclusion

The art of using AI isn’t about expecting magic — it’s about precision, clarity, and a willingness to troubleshoot like an engineer rather than a hopeful mystic.

Debugging your prompts doesn’t just save time; it protects the ethical use of AI by ensuring human intention remains firmly in the driver’s seat.

Tagged , , , , , , ,

Leave a Reply

Your email address will not be published. Required fields are marked *