Data Science Prompts Simple

Why Most Data Science Prompting Fails Before It Starts

I spent three years debugging prompt outputs that looked correct on the surface but produced garbage models downstream. The problem was almost never the model itself. It was the structure of the prompt. Data Science Prompts Simple is not a branded product or a piece of software you download. It is a documented approach to writing prompts for data science tasks that prioritizes explicit structure over creative wording. The original framework and reference examples can be found on GitHub under repos that use that exact phrase in their titles. The core idea is straightforward enough that people either overlook it or overcomplicate it. You are giving a language model a job description, input data, output format, and constraints in that specific order. Anything else is decoration. I found this out after a client asked me to build an automated reporting pipeline using GPT-4, and every prompt variation produced inconsistent column names, hallucinated missing values, and dropped edge cases without warning. The prompts kept getting longer instead of clearer. The method works by separating concerns inside the prompt itself. You do not ask the model to figure out what task it is solving. You tell it exactly what the inputs are, what transformation to apply, and what the output must look like. Then you give it a worked example if the task is non-trivial. That is it.

Here is how I actually structure these prompts in production. First section is the role and objective. Second section is the input schema. Third section is the processing rules. Fourth section is the output schema. Fifth section is the constraints. I keep each section under 150 words unless the logic genuinely demands more. Length does not correlate with quality here. One concrete example I use constantly involves feature engineering requests. A bad prompt says something like generate good features for a churn dataset. A proper one says the input contains columns for customer tenure months, monthly charges, contract type, and support ticket count. Generate three derived features that capture usage intensity and billing friction. Return them as a CSV with exactly these headers and no additional columns. Do not use logarithmic transforms unless the input column has values below zero. This approach cuts my prompt iteration time from about forty-five minutes per task down to roughly eight minutes. The tradeoff is that you have to write out schemas explicitly, which some people find tedious. It is tedious until you are maintaining a codebase where five people are editing prompts and three different models are producing three different formats on the same input.

There is a common pitfall that catches everyone at least once. When you include a worked example in the prompt, the model will sometimes treat that example as the only valid output format. If your real data differs even slightly from the example, the model will either hallucinate to match the example or silently drop records that do not fit. I learned this the hard way when I was building a prompt to classify support tickets into categories. The example had seven fields and the real input had nine. The model dropped two fields without telling me and returned results that looked structurally correct until someone actually used them. The workaround is to include the full schema first, then show a minimal example, and explicitly state that the example may not contain all fields from the schema. This sounds obvious. It is not obvious to the model. Another nuance that people miss is temperature selection. For Data Science Prompts Simple workflows, I keep temperature at zero point one or lower. Anything above that introduces variability in formatting decisions that you did not intend. The model might reorder columns or choose different naming conventions based on random tie-breaking. You do not want that in a pipeline.

The biggest limitation of this method is that it does not solve ambiguous problems. If your business question is unclear, a well-structured prompt will still produce garbage. It will just produce garbage with consistent formatting. I have seen teams use this framework and then complain that the outputs were wrong. The problem was not the prompt structure. The problem was that nobody had written down what success actually looked like for their use case. It also struggles with tasks that require genuine mathematical reasoning. If you ask a model to calculate statistical significance or perform a join across two datasets with mismatched keys, the prompt structure does not help much. The model will still make arithmetic errors or skip edge cases. In those situations, I recommend writing Python code instead of relying on the model to do the calculation directly. Use the prompt to generate the code, not to perform the math. If you want to start using this approach today, the easiest entry point is to take your existing prompts and rewrite them following the five-section structure. Role and objective, input schema, processing rules, output schema, constraints. That alone will improve output consistency by maybe sixty to seventy percent based on my experience. You do not need any special tools. You need to stop treating prompts like natural conversation and start treating them like technical specifications.

The reference material for this framework is organized on GitHub. The README includes the template structure, several real-world examples covering classification, feature engineering, and data cleaning tasks, and a benchmark showing improvement over unstructured prompts. Search for Data Science Prompts Simple on GitHub and you will find the main repository with downloadable templates and example notebooks. I stopped trying to make prompts sound polite or conversational months ago. They are not messages to a person. They are instructions to a pattern-matching engine that occasionally understands them correctly. Write them like instructions. Check the output against the schema. Iterate only when the schema is wrong or the constraints are insufficient. Everything else is noise.