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Docs: Add capabilities page (#164)
## Description 📝 Adds a capabilities page in the style of others from the HEP. Fortunately, there's lots of opportunity for cross-linking at this point. ## Quick Links 🚀 [Capabilities](https://add-how-to-talk.promptql-docs.pages.dev/capabilities/)
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docs/automation.mdx

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---
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sidebar_position: 1.7
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sidebar_position: 1.8
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sidebar_label: Automation
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description:
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"Learn how to build automated workflows and processes with PromptQL for reliable, repeatable business tasks."

docs/capabilities.mdx

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---
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sidebar_position: 1.6
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sidebar_label: What can PromptQL do?
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description:
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"Learn what PromptQL can do to help you make decisions quicker or automate tasks with relability and accuracy."
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keywords:
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- promptql
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- accurate
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- reliable
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- capabilities
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toc_max_heading_level: 4
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---
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import Thumbnail from "@site/src/components/Thumbnail";
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# What can PromptQL do?
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## Introduction
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PromptQL combines data analysis, AI capabilities, and automation tools into a conversational interface. It's designed to
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help you explore data, make decisions, and create reliable workflows—all through natural dialogue.
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## Core features
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### Agentic Semantic Metadata Layer
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PromptQL doesn't just execute queries—it builds and maintains a dynamic understanding of your data landscape. This
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"semantic metadata layer" grows more sophisticated with each interaction, learning your business terminology, data
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relationships, and analytical patterns.
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When you correct a definition or clarify a business rule, PromptQL remembers. It uses this knowledge to:
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- Translate natural language to precise queries
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- Apply consistent business rules across analyses
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- Suggest relevant connections between datasets
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- Self-correct based on feedback
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Think of it as an intelligent layer between you and your data that learns and adapts. It remembers that "active users"
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means something specific in your business, or that certain metrics should always be filtered in particular ways.
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:::info Learn more
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You can learn more about PromptQL's semantic understanding [here](/data-modeling/overview.mdx).
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:::
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### AI Primitives
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At its core, PromptQL works with data and AI functions, which we call **AI primitives**. It's helpful to think of these
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as tools in PromptQL's toolbox.
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For data, PromptQL can run complex SQL queries with built-in safety checks and optimization. It handles joins, and
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aggregations naturally, with automatic error handling and self-correction.
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The AI functions extend these capabilities beyond pure data analysis:
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| Primitive | Description |
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| --------- | -------------------------------------------- |
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| Classify | Sort text into dynamic categories. |
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| Summarize | Create concise summaries of longer text. |
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| Extract | Pull structured data from unstructured text. |
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| Visualize | Create interactive data visualizations. |
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By combining these primitives, PromptQL can tackle complex analytical tasks. For example, it might extract structured
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data from a batch of documents, classify the results, and visualize the patterns—all in a single workflow. Or it could
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summarize a large dataset, then extract key metrics from those summaries for deeper analysis.
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These combinations aren't just powerful—they're precise. Each primitive builds on the others, with error checking and
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validation at every step. The result is reliable, reproducible analysis that can be turned into automated workflows.
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:::info Learn more
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You can learn more about these primitives [here](/promptql-apis/execute-program-api.mdx#introduction).
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:::
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### Artifacts
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Think of artifacts as PromptQL's memory system. When you're exploring data or building workflows, artifacts help
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maintain context and enable reuse.
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| Type | Purpose |
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| ------------- | ---------------------------------------- |
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| Table | Store and reference structured data |
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| Text | Preserve documents and long-form content |
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| Visualization | Interactive charts and graphs |
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| Automation | Reusable, parameterized workflows |
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:::info Learn more
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You can learn more about artifacts [here](/promptql-playground/artifacts.mdx).
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:::
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## Use cases
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### Make decisions
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When it comes to decision-making, PromptQL acts as your analytical partner. It can explore data, detect patterns,
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visualize trends, and run comparisons. Each analysis can be saved for future reference, shared with your team, or used
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as part of your audit trail.
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The key is that PromptQL doesn't just show you numbers—it helps you understand them. Ask it to explain its methodology,
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break down complex metrics, or look at the same data from different angles. It's built to clarify, not just calculate.
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[Learn more](/decision-making.mdx) about making decisions with PromptQL.
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### Automate tasks
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Once you've found a valuable analysis pattern, PromptQL can turn it into a reliable automation. These aren't just saved
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queries—they're full workflows that can process new data, generate reports, monitor metrics, or alert on conditions.
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Each automation comes with built-in error handling and can be parameterized to handle different inputs. Chain them
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together for more complex operations, or schedule them to run regularly. The goal is to turn your one-off analyses into
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reliable, repeatable processes.
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[Learn more](/automation.mdx) about automating tasks with PromptQL.

docs/decision-making.mdx

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sidebar_label: Decision Making
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description: "Learn how you can use PrompQL for accurate AI in your decision-making processes."
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keywords:

docs/how-to-talk-to-promptql.mdx

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analysis you've done manually before. From there, PromptQL will learn your expectations, surface useful patterns, and
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help you turn those insights into reliable, repeatable workflows.
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We recommend picking up with [our overview of PromptQL's capabilities](#) but, if you're eager, jump in to one of these
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use cases:
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We recommend picking up with [our overview of PromptQL's capabilities](/capabilities.mdx) but, if you're eager, jump in
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to one of these use cases:
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- [Making decisions](/decision-making.mdx)
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- [Automating tasks](/automation.mdx)

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