Make Decisions with PromptQL

Introduction

Decision making with PromptQL supports deeper analysis and structured exploration across your data. You can ask complex, layered questions and get responses that adapt to your systems and terminology. PromptQL helps you drill into root causes, compare across categories, and evaluate tradeoffs without being limited by context windows or informal language.

This is useful for scenarios that require exploration and judgment, such as:

• Investigating anomalies
• Comparing performance across teams or regions
• Understanding contributing factors behind trends

Guides

Below, we've split out a few different use cases as examples. You can run these against the sandbox-movie-studio project.

Q&A

Take this example:

How did our PG-13 portfolio perform against R-rated titles during the streaming transition period?

It would seem that asking questions about data is simple, but this is difficult because business terms often map to multiple systems or concepts. For example, "performance" might refer to revenue, engagement, or critical ratings, depending on who’s asking. Systems don’t always agree on how those values are calculated, either.

PromptQL solves this by mapping ambiguous terms to precise system definitions and building a plan that retrieves data from the right sources in the correct form. The generated plan accounts for relevant time windows, content ratings, and distribution channels, producing a structured answer tailored to your domain.

Use this when you want direct answers that reflect your business logic and definitions.

Interrogation

PromptQL allows you to interrogate your data by following up naturally, asking for more detail, and adjusting the scope as you go. The system preserves your analysis trail and ensures consistency across steps.

Looking at our 2015–2020 release slate, what's the correlation between our talent investment strategy and audience retention metrics?

PromptQL solves this by generating a multi-step plan that fetches relevant datasets, applies statistical methods, and structures results in a way that's easy to pivot or extend. You can modify thresholds, change groupings, or backtrack to explore a different angle—all without losing context.

Use this when a surface-level answer isn't enough and you need to go deeper with confidence.

Edit the query plan

Most AI tools are a black box: you don't know what's happening under the hood, how answers were arrived upon, or what data was used.

With PromptQL, every response is backed by a transparent query plan that you can inspect, modify, and re-run. This gives you full control over the logic, data sources, and assumptions behind each result—so you can refine, extend, or validate the analysis as needed.

Deep Research

You can perform deep research that explores multiple hypotheses, benchmarks external data, and evaluates internal patterns across time or categories.

Can you analyze the ROI patterns of our genre-blending titles compared to pure-genre releases between 2010–2020?

PromptQL is unique because it treats research as a process and not just a query. It generates a plan that defines discovery phases, collects and segments relevant data, and evaluates each hypothesis systematically.

Use this when you're trying to answer open-ended questions that require context and exploration.

Cross-Source Intelligence

Since you can join any source using your semantic metadata layer, PromptQL can resolve data across structured, semi-structured, and unstructured systems in a single plan.

What's the risk profile of working with first-time directors who came from our star talent pool?

PromptQL builds a plan that pulls structured records (e.g., director metadata), aggregates historical performance metrics, and layers in qualitative signals from reviews or production notes. Relationships that span systems—like casting history, sentiment, and audience reception—are captured and evaluated together.

Use this when your answers require stitching together multiple systems and surfacing insights that aren't visible in any single source.

Smart Visualizations

Visualizations make it easy to understand complex patterns or communicate findings across stakeholders. PromptQL automatically selects appropriate formats—charts, tables, or graphs—based on the type and scale of your analysis.

Use this when you want to share findings with others or spot trends across segments or time periods.

Best Practices

• Start specific, then expand. Narrow, well-defined questions help PromptQL build better initial plans. You can always widen scope through follow-ups.
• Use your own terms. PromptQL is designed to understand your internal terminology, so write queries as you would naturally ask a colleague.
• Follow the thread. PromptQL preserves your reasoning trail—feel free to pivot, rewind, or dig deeper without losing previous steps.
• Review the plan. Each result is backed by a structured plan. Reviewing it helps validate how PromptQL interprets your intent.
• Use visualizations for communication. When sharing results, use PromptQL’s built-in visualization capabilities to highlight key insights clearly.

Next Steps

It's great to be able to ask questions and get accurate, reliable responses. But, what if you could turn these into automations? Check out how easy PromptQL makes it to automate tasks with the same level of accuracy and reliability 🚀