Internal Office and Employee Services for Mining and Ore Processing: Multi-turn Dialogue and Prompt Engineering

What the Data for This Category Looks Like

Data related to internal office and employee services for mining and ore processing comes from underground IoT sensors, ore processing production logs, equipment maintenance logs, employee attendance and scheduling systems, and safety inspection records. Data update rhythms:

• Sensor data updates in real time based on collection frequency
• Production logs update per shift
• Scheduling data updates weekly
• Maintenance logs update after each single job is completed

The document structure of a single data entry includes equipment ID, collection timestamp, parameter values (such as tons per hour, MPa, rotational speed r/min), employee ID, job title, work period, safety inspection items, and more. Field units include tons, cubic meters, hours, MPa, r/min, person-times, and more.

Constraints Imposed on Multi-turn Dialogue and Prompt Engineering

Real-time collected sensor data requires limiting the length of retained dialogue context. Overly long history will make data outdated and reduce answer accuracy. Multi-field equipment data requires prompts to clearly specify exclusive fields for retrieval. Without this, responses will return information from unrelated equipment or employees. Differences exist between employee positions and job content. Precise binding of corresponding data is required to avoid confusion across dialogue contexts for different positions. Large data volume requires limiting the number of retained historical entries in multi-turn dialogue. Excessive historical data will occupy context resources and truncate key information.

Configuration Settings

This page uses FastGPT v4.17.0 for configuration.

Three Common Misconfigurations

• Dialogue responses do not match the query. For example, asking about the production capacity of a specific ore processing station returns parameters of other equipment. This occurs when the prompt fails to clearly limit matching to exclusive mining and ore processing equipment ID fields, leading to retrieval of unrelated data.
• The chat:LLM_model_response_empty error is triggered. This happens when the messages parameter does not pass the latest user questions in order, or when context length exceeds the configured limit, resulting in truncated key information.
• Garbled quotation marks appear at the end of output text. This stems from incorrect format configuration of the variables parameter, which fails to properly parse quotation marks generated by the model and causes mixed half-width and full-width characters.

How to Confirm Successful Configuration

• Submit a mining and ore processing-specific query, such as asking for real-time parameters of a specific underground equipment. Verify that the returned result includes the corresponding equipment parameters and units as expected.
• Test multi-turn dialogue using a session associated with the chatId parameter. Confirm that historical context is correctly retained and does not include unrelated data from other positions or equipment.
• When the chat:LLM_model_response_empty error occurs, check whether the order and length of the messages parameter align with the configured maximum context length.
• When reviewing global variable calls, confirm that employee data bound by the variables parameter matches the current dialogue employee’s job title.
• Check that output text has no garbled characters, and confirm that the variables parameter format is configured correctly so that quotation marks display normally.

The values given are common starting points and should be measured against the reader's own samples.

Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.