What can PromptQL do?
Introduction
PromptQL combines data analysis, AI capabilities, and automation tools into a conversational interface. It's designed to help you explore data, make decisions, and create reliable workflows—all through natural dialogue.
Core features
Agentic Semantic Metadata Layer
PromptQL doesn't just execute queries—it builds and maintains a dynamic understanding of your data landscape. This "semantic metadata layer" grows more sophisticated with each interaction, learning your business terminology, data relationships, and analytical patterns.
When you correct a definition or clarify a business rule, PromptQL remembers. It uses this knowledge to:
- Translate natural language to precise queries
- Apply consistent business rules across analyses
- Suggest relevant connections between datasets
- Self-correct based on feedback
Think of it as an intelligent layer between you and your data that learns and adapts. It remembers that "active users" means something specific in your business, or that certain metrics should always be filtered in particular ways.
You can learn more about PromptQL's semantic understanding here.
AI Primitives
At its core, PromptQL works with data and AI functions, which we call AI primitives. It's helpful to think of these as tools in PromptQL's toolbox.
For data, PromptQL can run complex SQL queries with built-in safety checks and optimization. It handles joins, and aggregations naturally, with automatic error handling and self-correction.
The AI functions extend these capabilities beyond pure data analysis:
| Primitive | Description |
|---|---|
| Classify | Sort text into dynamic categories. |
| Summarize | Create concise summaries of longer text. |
| Extract | Pull structured data from unstructured text. |
| Visualize | Create interactive data visualizations. |
By combining these primitives, PromptQL can tackle complex analytical tasks. For example, it might extract structured data from a batch of documents, classify the results, and visualize the patterns...all in a single workflow. Or it could summarize a large dataset, then extract key metrics from those summaries for deeper analysis.
These combinations aren't just powerful, they're precise. Each primitive builds on the others, with error checking and validation at every step. The result is reliable, reproducible analysis that can be turned into automated workflows.
You can learn more about these primitives here.
Artifacts
Think of artifacts as PromptQL's memory system. When you're exploring data or building workflows, artifacts help maintain context and enable reuse.
| Type | Purpose |
|---|---|
| Table | Store and reference structured data |
| Text | Preserve documents and long-form content |
| Visualization | Interactive charts and graphs |
| Automation | Reusable, parameterized workflows |
You can learn more about artifacts here.
Use cases
Make decisions
When it comes to decision-making, PromptQL acts as your analytical partner. It can explore data, detect patterns, visualize trends, and run comparisons. Each analysis can be saved for future reference, shared with your team, or used as part of your audit trail.
The key is that PromptQL doesn't just show you numbers: it helps you understand them. Ask it to explain its methodology, break down complex metrics, or look at the same data from different angles. It's built to clarify, not just calculate.
Learn more about making decisions with PromptQL.
Automate tasks
Once you've found a valuable analysis pattern, PromptQL can turn it into a reliable automation. These aren't just saved queries, but full workflows that can process new data, generate reports, monitor metrics, or alert on conditions.
Each automation comes with built-in error handling and can be parameterized to handle different inputs. Chain them together for more complex operations, or schedule them to run regularly. The goal is to turn your one-off analyses into reliable, repeatable processes.
Learn more about automating tasks with PromptQL.