Which questions should natural-language equipment queries answer first?
Start with equipment status, energy trends, and event records. Map everyday questions to available data, access rights, and traceable answers, then validate the AI query function against real work questions.
Finding devices that stopped updating yesterday may involve an equipment list, trend charts, and event records. Natural-language queries can bring those lookups into one question interface, using the language people use at work.
Choose initial questions that available data can answer and people can readily verify. Establish a clear retrieval scope before adding explanations and analysis.
Ask about specific equipment, periods, and states
The first questions can cover equipment status, period values, and event lists. These examples illustrate possible functions; the required data depends on the actual integration scope.
| Example question | Required data | Information to include in the answer |
|---|---|---|
| Which devices have exceeded their update threshold? | Equipment list, source and receipt times, and staleness rules | Device names, source and receipt times, and applied thresholds |
| How much energy did this circuit use yesterday? | Circuit mapping, energy records, and calculation rules | Period, unit, calculation method, and gaps |
| Which alerts occurred on this device this week? | Equipment identity, alerts, and handling status | Event times, details, and source links |
Resolve yesterday to an explicit period in the agreed time zone. Map abnormal to defined events or thresholds. If an alias identifies several devices, ask the user to select the intended equipment before querying. Check both source and receipt times when assessing freshness to identify old readings that have just been backfilled.
Turn questions into bounded data queries
A useful design extracts equipment, fields, periods, and filters from the question, then calls approved query functions. The data service retrieves records and performs calculations. The LLM helps interpret wording and present the result.
Calculate energy consumption through defined rules and retain the query conditions. If a requested field is unavailable, identify the missing data and offer access to existing records. Questions about causes can first return trends and related events, with inferences clearly labeled.
Link answers to their sources
Include the query period, data update time, units, and sources in the answer. Links to equipment pages or event records let users compare the AI summary with the underlying data.
Generative AI can produce incorrect content. The NIST Generative AI Profile recommends reviewing sources and citations in AI outputs. Put that practice into the interface through accessible evidence for values and events, and explicit gaps when data is insufficient.
Apply the user’s work permissions
The data service checks identity and access scope for every query. AI retrieval follows the same site, equipment, and document permissions as the platform. Apply these checks to source records, attachments, and exports so summaries stay within the user’s work scope.
Start with read-only retrieval. Creating work orders, changing settings, or controlling equipment requires separate action interfaces, permissions, and approval steps, with recorded execution results.
Validate with questions from daily work
Ask management staff to assemble everyday questions. Specify the source, correct period, and expected result for each question. Compare manual lookups with AI answers, then test ambiguous names, missing or stale data, and requests beyond the user’s permissions.
Record error types and results after changes. This shows whether to improve data coverage, query conditions, or question interpretation. Expand to more equipment and questions once the initial scope is stable.
lvsota’s AI and LLM implementation service works with equipment and data integration to plan platform queries. Bring common management questions and available records to discuss the first query functions.
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