Grubby AI: Turning Messy Outputs into Reliable Automation

Grubby AI is the term I use for those moments when a language model spits out answers that are technically correct but buried in noise, repetition, or off‑topic fluff. If you’ve ever copied a model’s reply into a report and spent more time editing than generating, you know the pain. After reading this, you’ll be able to turn those grubby outputs into clean, usable data with a repeatable prompt pipeline and a few low‑cost tools.

What is grubby AI and why it matters

Grubby AI shows up when the model tries to be helpful but adds filler, repeats phrases, or wanders into tangents. It’s not wrong, just messy. For a solo operator, that mess eats up time that could be spent on billing or client work. The goal isn’t to stop the model from being creative; it’s to strip away the junk so the core insight stays intact.

Think of it like panning for gold. You shake the pan, let the dirt wash away, and keep the nuggets. The same idea applies to AI output: you run it through a cleaning step that removes the fluff and leaves the usable piece.

Why does grubby AI break at scale?

When you start feeding dozens of prompts into a workflow, the noise multiplies. One grubby reply might be a minor annoyance; ten of them can clog a spreadsheet, break a template, or trigger a validation error in a downstream app. The problem isn’t the model’s intelligence; it’s the lack of a consistent post‑process.

Most people try to fix grubby AI by tweaking the prompt each time. That works for a one‑off task, but it doesn’t scale. You end up maintaining a library of custom prompts that drift as the model updates. A better approach is to separate generation from cleaning, letting the model do what it’s good at and handing off the mess to a dedicated cleanup step.

A concrete named example: cleaning LinkedIn scraped data with the Make platform and a custom prompt

Let’s walk through a real scenario I ran last month. I needed to pull job titles from LinkedIn profiles scraped by a PhantomBuster agent. The raw output looked like this:


Senior Marketing Manager at TechCorp (formerly at StartupX)\nSenior Marketing Manager at TechCorp\nSenior Marketing Manager at TechCorp\nSenior Marketing Manager at TechCorp (ex‑StartupX)\nSenior Marketing Manager at TechCorp


Notice the duplication, the parenthetical notes, and the inconsistent formatting. I wanted a clean list of unique titles without any extra text.

First, I fed the raw text into a GPT‑4o prompt that asked for a JSON array of distinct titles. The prompt I used:


You are a data cleaning assistant. Given the following raw job title strings, return a JSON array containing each unique title exactly once. Do not add any extra commentary.

[PASTE RAW TEXT HERE]


The model returned a proper JSON array, which I then parsed with Make.com’s built‑in JSON module. From there I pushed the clean titles into a Google Sheet for further processing.

What made this work?

• I kept the generation prompt short and focused on a single task: deduplication and formatting.
• I used Make.com to orchestrate the steps: scrape → prompt → parse → store.
• I added a simple error handler that retries the prompt up to two times if the JSON is malformed.

The whole scenario costs about $0.004 per run in API calls, plus the Make.com operation count. On the Make.com core plan at $29/mo you get enough operations for hundreds of runs each day, which I think is fair for the time saved.

What most guides get wrong about AI output cleaning

Many tutorials suggest you should engineer a mega‑prompt that tries to do everything: generate, format, validate, and output in one shot. That approach creates a brittle monolith. If the model misses one instruction, the whole output fails.

Another common mistake is to rely on regex or string replace after the model finishes. Regex works for predictable patterns, but grubby AI often introduces novel phrasing that breaks those patterns. You end up chasing edge cases forever.

The better pattern is to treat cleaning as a separate, deterministic step. Let the model handle the fuzzy part (understanding intent, extracting entities) and hand off the structured transformation to a tool that excels at it—like a JSON parser, a CSV formatter, or a simple Python script.

How to debug when this breaks

When the cleanup step fails, start by checking the raw model output. Is the JSON missing brackets? Did the model add a preamble like “Here is the result:”? Those are the usual suspects.

Next, look at the prompt. Did you give it enough context to know what format you need? A vague prompt like “Make this clean” invites variability. Be explicit: “Return a JSON array of strings, no extra text.”

Finally, verify the tool you’re using to parse the output. In Make.com, the JSON module will throw an error if the payload isn’t valid JSON. Catch that error, log the raw payload, and trigger a retry with a slightly tweaked prompt that reminds the model to stay within the brackets.

If you keep seeing failures, add a fallback step: ask the model to output plain text separated by commas, then use a text‑to‑array module instead of JSON. That redundancy often saves a scenario when the model decides to be creative with brackets.

Concrete gripe, love, and price check

My gripe: the free tier of Make.com caps you at 1000 operations a month, which feels stingy when you’re testing prompts and running retries. I burned through that limit in two afternoons while debugging a scenario.

My love: I love how Make.com’s visual scenario builder lets me see each step of the cleanup pipeline in real time. I can drag a new module in, watch the data flow, and spot where the grubby bits appear without digging through logs.

Price mention: Make.com’s core plan at $29/mo gives you enough operations for a small agency, and I think that’s fair. The OpenAI API cost for the cleaning prompt is negligible—about $0.0005 per call at current rates—so the bulk of the expense is the automation platform, not the model.

— and good luck finding docs for this — the error handling UI in Make.com is tucked under the “Advanced” tab, which took me longer than I’d like to locate.

For more on this exact angle, deeper coverage of AI agent platforms.

If you’d rather skip the build and deploy a working version in an afternoon, we’ve packaged this workflow as a blueprint at deepusecase.com/vault.