Basics
Chat or search
Chat with your data and get answers with data from sources
Chat
The Chat feature in Leapfrog enables users to interact with research data using natural language queries. It utilizes AI models to process questions and provide relevant answers based on the data within the workspace.
Functionality
- Input Query: Enter a question or prompt in the chat input field. Queries can address specific topics, request summaries, or seek insights related to research data.
- AI Analysis: The AI model analyzes the query and searches through workspace documents, transcripts, and other data sources to find relevant information.
- Response Generation: The AI generates a response grounded in the available data, including relevant quotes, excerpts, or summaries.
- Follow-up: Users can ask follow-up questions or refine queries based on previous responses.
Use Cases
- Exploratory analysis of data
- Targeted queries for specific information
- Summarization of documents or datasets
- Hypothesis validation or refutation
- Sharing insights with team members
Best Practices
- Provide context in queries to improve AI understanding
- Ask clear and specific questions for focused responses
- Refine queries if initial responses are insufficient
- Verify AI responses against original data sources
Customizing Chat Behavior
- Navigate to Settings > Chat in the workspace
- Enter custom instructions in the “Chat Instructions” section to guide the AI’s response style
- Save changes or reset to default instructions as needed
Custom instructions allow users to tailor the AI’s responses to specific research needs and standards.