How to classify large CSV files with LLMs (timeouts not welcome)

When people first discover that LLMs can classify, enrich, and summarize data, the instinctive next step is obvious: "What if I just give it my CSV?"

That works — briefly. Then things fall apart. If you've seen it work for small CSV files to then break after 20-40 lines, you're not alone. This guide is for you and it will explain the three common approaches people try, why the first two fail, and what actually works once datasets grow beyond toy size.

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Attempt #1: “I’ll just paste the CSV into ChatGPT”

This is the most common starting point.

You:

• export a CSV
• paste it (or upload it) into ChatGPT, Gemini, or Claude
• ask: “Classify each row and give me a table back”

For small files, it feels magical (at first): zero setup, immediate results, great demos.

But very quickly, it breaks and you hit hard limits:

• file size caps
• token limits
• truncated outputs
• hallucinated rows
• missing or reordered columns

Even worse, failures are often silent. The model may drop rows or summarize instead of processing line by line.

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Attempt #2: “Let’s just write a small Python script”

The next step is usually: “Ok, I’ll ask ChatGPT or a colleague to write a Python loop.”

At first glance, it looks trivial:

for row in csv:
    call_llm(row)

In practice, this is where complexity explodes. A real-world loop must handle:

• Rate limits:you must throttle requests, detect 429 errors, and retry safely.
• Partial failure:What happens if row 742 fails? Or the script crashes after 2 hours? You now need progress tracking and resumability.
• Cost control: retries resend prompts, token usage grows unpredictably, bills arrive after the fact.
• Structured output enforcement: LLMs sometimes return invalid JSON or mix explanations with data. You need validation and retries.
• Operational ownership: Someone must maintain the script, credentials, logs, and reruns.

This is no longer a “small script”. It’s a mini data pipeline.

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The real issue: prompts vs workloads

Both approaches fail because they treat LLMs like interactive tools. But once you cross a few hundred rows, you’re running a workload:

• asynchronous
• failure-prone
• cost-sensitive
• output-critical

Spreadsheets and ad-hoc scripts are the wrong abstraction.

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What actually works: batch jobs

Batch jobs introduce structure and reliability:

• upload once
• process in chunks
• track progress
• retry failed rows only
• merge clean results

Jobs run independently of browser sessions or local machines.

You need this approach when datasets exceed a few hundred rows, outputs must be reused, failures must be visible and costs must be predictable. At that point, the problem is execution, not prompting. And if you're looking to build your own batch job, read this guide that goes through the process and describe the required architecture.

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How RowSherpa fits in

RowSherpa was built for teams stuck between spreadsheets and custom scripts.

You upload a CSV, define the schema, and run a batch job that:

• scales safely
• retries automatically
• produces clean, structured data

👉 If ChatGPT worked once but broke the second time, you’re already here.

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Try RowSherpa for free to see how it works: signup here.