Using Natural Language Prompting to Query Data Instantly

The problem

Getting answers from large datasets means writing SQL, knowing exactly which tables to query, how to join them, and how to format the output, creating a bottleneck. Operators who understand the business questions often aren't the same people who can write the queries. When they are, time spent on data is time not spent acting on it.

The solution

We use natural language prompting to let anyone on the team ask complex business questions. In turn, they receive structured formatted answers, all without writing a single line of SQL.

The key isn't just giving a large language model access to data. The key is context priming before the model ever even runs a query.

Before asking it anything, we need to set Claude up with a complete picture of how our business works:

• Business model overview
• A glossary of key terminology (definitions and example use cases)
• Data documentation (on where that data lives in BigQuery)
• Output templates (how operators expect insights to be summarized and formatted)

Once the model has both data access and contextual framework, operators can ask questions in the way they actually think in natural language.

The impact

When the model handles the translation from question to query to insight, operators can shift their focus from working through data to acting on it.

Answers arrive with context. Decisions move faster. And the people closest to the business questions are no longer dependent on a few key operators for answers.

Data stops being a bottleneck and instantly starts being an advantage.

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