Best Practices
Some basic tips for working with Ana
This doc provides some basic recomendations on how to prompt Ana in her two main interfaces:
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The Chat Interface (Includes Slack Chats)
Ana Chat Best Practices
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🤖 Treat Ana as you would a coworker: Have interactive conversations to clarify or refine requests, just as you would with a real analyst.
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🧩 Break complex requests into smaller steps: Language models can get overwhelmed when given too many steps at once.
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❓ Ask follow-up questions: This helps uncover deeper insights or correct misunderstandings along the way.
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📈 Request charts and visualizations: These can be easy ways to understand data and insights, as well as verify Ana’s assumptions.
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❗ Expect occasional misunderstandings or assumptions: Ana might interpret questions differently, so be prepared to rephrase or specify your request when needed.
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🔄 If a chat gets too long or isn’t going well, start fresh: Ana has access to many tools and may stumble when handling too many things at once. Sometimes, the best solution is to start a new chat.
TextQL Agents Best Practices
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📝 Write comprehensive prompts: TextQL agents are designed for deep asynconous analysis, triggered on an event or schedule. Unlike Ana chats, which are designed to expect followup messages, TextQL Agents generally assume that you have provided a comprehensive descrition of the task (at a high level), so that they can continually work without the need to ask for clarifying questions. To that end, Agents tend to perform best with long prompts, that include as many steps, actions, and corner cases as the user is aware of. After an Agent completes its action, you will have the option of continueing the agent conversation with followup messages through the chat interface.
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🕵️♀️ Preview your analysis before activating the agent: Previewing agent runs allows you to quickly iterate and refine your prompt, ensuring your agent is able to reliably execute your desired task. Generally, we recomend running atleast 3 previews before activating your agent.
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🎁 Attach any relevant datasets you’re aware of: If you believe that an Agent will regularly use a partiular dataset of interest, you should attach that dataset explicitly to the agent at setup time. This helps reduce ambiguity and streamline the Agent’s planning at runtime. If you aren’t aware of any such datasets, or you have low farmilirarity with your companies datawarehouse, you can safely disregard this step/ TextQL Agents have the ability to search your datawarehouse at runtime to identify the data of interest relevant to your query, it just might take longer to get to the result.
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🧾 Direct Ana to relevant parts of a dashboard: If your agent uses Tableau as a datasource, specify the sheet or sub‑figure relevant to your question or task. Call out any non‑obvious filters or business logic that effect your dashboard: for example, “The orders dashbaord only visualizes orders jobs with Status = Active and Region = USA.” Remember, you see the dashboard visually, but the agent also sees the underlying data tables used to generate the dashboard: If Ana’s results differ from what you see visually, your dashboard likely shows a restricted data view.
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🖼️ Specify output format and include code snipbits: For reporting use cases, where an Agent is asked to perform the exact same analysis on a schedule, it’s useful to explicitly specify your desired output format so that plots and reports are consistent across agent runs. Consider prototyping your agent in a chat and refining output plots until they fully capture your desired report; then, copy the visualization code to an agent prompt.
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📅 Be mindful of dates and data updates: Users often want to ask “Tell me how we are doing today” or “Send me a report on today’s changes.” These types of analysis are supported and encouranged; however, certain datasets within your companies datawarehouse may update on a daily or hourly basis. To that end, in certain instances, complete results are only attainable by asking about previous days or time periods, for example “Tell me how we did yestreday, and compare this to previous days and weeks at the same time.”