Atlas AI agents answer questions about your industrial data by querying the CDF knowledge graph. The quality of your prompts directly affects the accuracy and usefulness of agent responses. Specific prompts that include context and define what you need help the agent retrieve the right data and return a useful answer the first time.
Be specific and clear
Specific prompts produce better results than vague ones. Use action verbs to clarify what you want the agent to do.
Vague
Specific
Highly specific
Tell me about Pump P-101.
What is the current operational status of Pump P-101?
List all open high-priority work orders for Compressor C-205 created in the last 30 days.
Provide context
Context helps agents focus on relevant data by narrowing the scope of your request.
Without context
With basic context
With detailed context
Show me the temperature trends.
Show me the temperature trend for Reactor R-301, specifically for sensor TI-301A, over the past 7 days.
Considering the recent maintenance on Heat Exchanger HE-502, are there any anomalous pressure
readings in the connected lines from the past 48 hours?
Define the desired output
Specify the format, structure, and level of detail you need in the response.
Undefined
Defined format
Defined precision
Structured output
Give me maintenance data for Valve V-789.
Summarize the maintenance history for Control Valve CV-789 for the last 12 months, focusing
on repair actions taken and parts replaced. Present it as a bulleted list.
What were the maximum and minimum vibration levels for Motor M-607 on January 15th, 2024?
List all work orders for Pump P-101 in a table showing work order ID, status, description,
and date.
Use schema awareness
When your agent uses the Query tool, it can discover the structure of your data model at runtime. You can ask questions that rely on relationships between assets, equipment, and data without knowing the underlying field names or schema.
This table shows examples of questions that use schema awareness effectively.
| Question | What the agent does |
|---|
| How many maintenance orders are there per equipment type? | Queries the knowledge graph and groups results by equipment type |
| What sensors are connected to Compressor C-205, and which ones have readings outside the expected range? | Traverses the relationship between the asset and its linked time series, then checks current values |
| Show me all heat exchangers with overdue inspections | Filters on asset type and inspection status without requiring you to specify field names |
Ask effective follow-up questions
Agents retain context within a conversation, so you can build on a previous response without repeating the original question. If the agent returned a list of work orders, you can follow up with “of those, which ones are high priority?” or “show those results as a table” without re-specifying the equipment or time range.
Follow-up questions work well when they narrow, reformat, or extend the previous result. If you need to ask about a different piece of equipment or a completely different workflow, starting a new conversation resets the agent’s context.
When a response is not what you expected, check the reasoning field to see how the agent interpreted your question and which data it used. If the agent queried the wrong equipment or applied the wrong filter, use that information to refine your next prompt with more specific criteria.
Last modified on June 18, 2026
