Prompting Guide
The way you phrase a question directly affects the quality of the answer. This page explains how the Data Agent interprets requests and how to write prompts that return precise, useful results.
how to get more accurate results
how to get faster results
requests should match actual measurements if possible (e.g. talking of utilization when you want people-count/occupancy metrics)
How the Agent Interprets Your Request
The agent parses every query for the following elements:
Locations
building, floor, room, zone, east wing, basement
Assets or asset types
sensors, meters, SmartSocket, EnergyMeter, rooms
Metrics
temperature, energy usage, occupancy, CO2, water consumption
Time ranges
last week, past quarter, between Jan 1 and Dec 31, today
The more of these you include, the more precisely the agent can scope and execute the query. Missing elements may trigger a clarification question.
Writing Effective Prompts
Be specific about what you want to measure
The agent maps for example measurement terms to concrete attributes in the database. Using the right vocabulary reduces the risk of ambiguity and disambiguation steps.
Show me the data for the building.
Show me the current temperature readings for all assets on the 3rd floor of Building A.
Any issues with the sensors?
Which sensors on the 2nd floor haven't reported data in the last 48 hours?
Reference specific entities when you know them
Mentioning asset names, asset types, or exact locations allows the agent to resolve scope without requiring clarification.
The more specific you are the faster responses will be, as the Assistant does not have to do vague searches first.
Which rooms are warm?
Which RoomController assets on the 2nd floor are recording temperatures above 24°C?
Check the sockets.
Retrieve the current power consumption of all assets with asset type SmartSocket in Building A.
Include a time range for historical queries
Time ranges are important for trend queries. Without one, the agent may request clarification or apply a short default window.
Show me energy consumption for Building B2F.
Show me the total energy consumption for Building B2F for the past quarter.
When was occupancy highest?
What was the peak daily occupancy in Building A during the last quarter?
Larger Time Ranges for historical data will slow down Database Queries and subsequent Result Analysis
Ask one clear question at a time
The agent retains context across a conversation. Splitting a complex request into a sequence of focused follow-ups produces more reliable results than combining everything into a single prompt.
Show me energy, occupancy, temperature, and alarms for all buildings for the last year.
Show me total energy consumption per building for the last quarter. → then: Now show me occupancy for the same period.
Positive vs. Negative Prompt Examples
"Which buildings have the biggest utilization gap versus energy intensity?"
"What is the average energy consumption per building for the last month?"
"Utilization gap" and "energy intensity" are not directly mapped metrics
"Which sites show the highest opportunity for improvement?"
"Which buildings consumed the most energy in the last quarter? Rank them."
"Opportunity" requires a defined metric to rank against
"Where do space, asset, and energy KPIs point to the biggest business impact?"
"Show me average CO₂ levels and occupancy side by side for all meeting rooms."
Multiple undefined KPIs without a target metric
"Are there anomalies?"
"Are there any assets in the server room that recorded temperatures above 80°C last week?"
No metric, scope, or threshold defined
"Show me everything."
"Show me the current occupancy readings for all rooms in Building A."
No scope or metric
The less-effective examples fail not because the question is unreasonable, but because they lack a defined metric, scope, or time range that the agent can translate into a database query.
Tips & Patterns
Use asset type names when you know them Referencing known asset types (e.g. SmartSocket, EnergyMeter, RoomController) makes scope resolution immediate and avoids disambiguation steps.
Provide units when relevant Adding "in kWh" or "in °C" helps the agent select the correct measurement attribute when multiple options exist.
Follow the suggested prompts Every response ends with grounded follow-up suggestions based on the data just retrieved. These are the fastest way to drill down progressively. You can also select them and then tune those to your specific needs.
Iterate rather than over-specify upfront A broad first question followed by focused follow-ups is more reliable than a single highly complex query.
Use the tool call trace when results look unexpected If a response does not match what you expected, expand the tool call trace. It shows exactly which assets and measurements were resolved, and where any ambiguity was encountered. Rephrasing with more specific terms usually resolves the issue.
Common Pitfalls
Using business terms with no direct database mapping
The agent asks for clarification or selects the closest available attribute
Use specific terms that exists as attributes on your assets e.g. "people-count" instead of "utilization"
Omitting a time range in historical queries
The agent asks for clarification or applies a short default window
Always specify: "last 7 days", "past quarter", "previous 2 months"
Asking multiple unrelated questions in one prompt
Results may be incomplete or one question may be deprioritized
Split into follow-up prompts or different conversations
Using an ambiguous location name
Multiple candidates may match; the agent asks you to choose
Provide more specific identifiers when available (e.g. concrete asset names, types or ids)
Expecting the agent to draw business conclusions
The agent retrieves and surfaces data; interpretation is mostly up to you
Use the results as input for your own analysis
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