> ## Documentation Index > Fetch the complete documentation index at: https://docs.bagofwords.com/llms.txt > Use this file to discover all available pages before exploring further. # Chat with your data > Prompt governed agents for questions, advanced analysis, root-cause investigation, and artifacts. Reports are persistent conversations with governed agents. A report keeps your prompts, tool activity, results, artifacts, and follow-up context together so you can refine the work without starting over. A detailed root-cause prompt followed by parallel investigation, a note tool, and a Mermaid dependency map ## Choose the mode * **Chat** is for doing production work with approved instructions and access. * **Training** is for learning sources, clarifying business rules, and proposing reviewable instruction changes. Switch modes in the composer. Training changes do not silently become production behavior; review and accept the proposed instruction edits first. For a step-by-step walkthrough, see [Train an agent](/agents/train-an-agent). ## Write a strong prompt A useful prompt states the outcome, evidence boundary, validation standard, and deliverable. You do not need to prescribe every query or tool call. > Show weekly net revenue retention for enterprise customers in Q2. Use the approved account and fiscal-period definitions, exclude test accounts, compare the result with the Power BI executive measure, and return a table plus a short explanation of any mismatch. > Act as a senior finance analyst. Explain why enterprise ARR growth slowed in Q2 despite higher bookings. Compare plan versus actual by region and segment; separate volume, price, churn, timing, and FX effects; identify the five accounts driving the variance; validate every number against Snowflake and Power BI; and finish with an executive summary and recommended actions. > Investigate the checkout failures that began this morning. Correlate deployments, traces, logs, and SQL Server telemetry in parallel. Keep an investigation note, draw the dependency path, quantify impact, rule out plausible alternatives, and finish with the root cause and prevention actions. > Build an executive revenue and retention command center for the weekly operating review. Validate Snowflake against Power BI; show ARR, NRR, expansion, churn risk, trajectory, and regional mix; highlight actionable risks; share it with Finance; and schedule a Monday 08:00 refresh. ## Follow the agentic loop The run timeline shows how the agent moves from request to result: 1. It plans the investigation and selects scoped context. 2. It runs independent checks in parallel where possible. 3. It observes tool results, validates evidence, and records useful intermediate state. 4. It reflects, retries, or asks for clarification when evidence is incomplete. 5. It returns a conclusion, data result, or artifact with its work visible. For complex work, ask for a **note** to maintain hypotheses, open questions, and next actions. Ask for a **Mermaid** diagram to make dependencies, flows, or incident timelines explicit. ## Iterate without repeating context Follow up in the same report to narrow a period, add a comparison, challenge an assumption, or change the deliverable. Useful follow-ups include: * “Re-run this at constant currency and explain what changed.” * “Rule out the payment provider with trace evidence.” * “Turn the conclusion into an executive dashboard artifact.” * “Save the clarified service-start rule as a proposed instruction.” ## Select a model in the conversation The composer shows the active model. Choose another enabled model for the session when you want a different speed, cost, or reasoning profile; automatic routing can select for you when it is enabled by an administrator. For models that reason, the picker also has a **Faster ↔ Smarter** slider with four stops: **Fast**, **Medium**, **High**, and **Max**. Fast skips reasoning where the model allows it, for the quickest and cheapest answers; Max thinks longest, for hard multi-step analysis. Until you move it, the slider sits on your administrator's default effort for that model (or Fast if none is set). Your choice is saved with the conversation. The current in-chat model selector with configured reasoning and fast models Use business language and desired outcomes. The agent uses scoped schemas, semantic objects, files, APIs, tools, and [instructions](/agents/instructions) to determine the implementation details. This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.