LLMs vs OrcaSheets

Why can't I just upload my dataset to an LLM and get analysis straightaway?" 🤔

We hear this in almost every demo. Fair question! LLMs are smart, they read documents, they summarise things. So why not just throw a CSV at ChatGPT or Claude or Gemini and call it a day?

Here's what's actually happening when you take this approach.

*The context window problem*
LLMs can only process so much information at once. A 100,000-row dataset may not fit, so the model truncates your data silently and predicts the next possible token. Truncation and prediction are problematic for deterministic analysis. Here's why.

*The hallucination problem*
LLMs predict the next word or token based on everything they've seen before. That works beautifully for text or images, where close enough is often good enough. But for stats and analysis where 100% accuracy matters, that model breaks down. Recent LLMs generate and execute code to work around this, but to optimise for speed and scale, that code may only run on a sample of your data. Either way, you're analysing a slice, not the whole thing.

*The privacy problem*
When you upload a dataset to a cloud-based LLM, that data leaves your machine. The compute happens on someone else's server. And unless you have an explicit setting from the service provider confirming your data won't be used for model training, you're trusting a terms of service document. Enterprises can negotiate that guarantee. For everyone else, it's rarely that straightforward.

*The reproducibility problem*
Ask an LLM the same question twice and you may get two different answers. There's no query log, no audit trail, no way to hand a colleague a repeatable process and say "run this again next month." And every time you ask an LLM to solve the same problem, it won't reach for the previous solution, it consumes more tokens to generate results that may or may not match. You're also paying for a problem you already solved last month. With OrcaSheets Recipes, you can save your analysis as single-click flows. Once created, no additional resources are required for execution. It’s that simple.

Asking an LLM to do data analysis is like using a hacksaw to cut butter. Technically it works. But you won't get clean results, and you definitely won't get consistent ones.

So what do you do? You stop asking a language model to do a data engine's job. You need a platform that uses LLMs for something they are very good at: generating code. And uses that code to run the query on your entire data, not predict the answer

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