What do users need to understand to get started?

Why Dust: Why did my company decide to use Dust, and how am I expected to use it?
Capabilities: What can Dust do?
Compliance Guidelines: Are there specific documents or data we shouldn’t use with Dust?
Data Usage: What data is available in Dust?
Instructions: How do I create good instructions?
Non-Deterministic Outputs and LLMs limitations: Large language models like Claude, GPT, Gemini or Mistral are non-deterministic, meaning the same input won’t always produce the same output. They don’t know how to read structured data easily.
Use Cases ideas: Examples like writing, coding, research, translation, idea generation, and task automation.
Assistants: What are Dust assistants? How do I use or create one?
Data Analysis: How can I use Dust Table Query tool for data analysis?
Dust is there to augment you: LLMs perform best with human oversight to review, guide, and improve outputs for accuracy.

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Watch our tutorials and demo to help you see Dust in action

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