Multi-turn Conversations and Prompts for Cold Chain

Data Characteristics

Cold chain logistics product data originates from IoT sensors, Warehouse Management Systems (WMS), Transportation Management Systems (TMS), and compliance documents. Sensor data (e.g., temperature, humidity, location) updates in real-time as streaming data, potentially every minute. WMS and TMS data updates in batches, typically daily or hourly. Documents include product specifications, operating procedures, and regulatory compliance reports, often in PDF or structured XML formats. Fields cover product batch numbers, storage conditions (e.g., temperature range 2°C to 8°C), transportation routes, and anomaly event records. Units commonly include Celsius (°C), percentage (%RH), kilometers (km), and timestamps.

Constraints from Data Characteristics on Multi-turn Conversations and Prompts

The real-time and distributed nature of cold chain logistics product data requires multi-turn dialogue systems to quickly integrate information from various sources. For example, when a user queries the transportation status of a specific drug batch, the system must simultaneously access real-time sensor data and historical transportation records. The coexistence of structured and unstructured documents challenges prompt engineering, requiring prompts designed to effectively parse complex documents and extract key information. Strict compliance requirements make the accuracy and traceability of dialogue results critical; prompts need to guide the model to prioritize authoritative data sources. The multi-unit and multi-field characteristics require prompts to accurately identify and associate correct values and units when understanding user queries, avoiding confusion, such as distinguishing between storage temperature and transportation temperature.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`maxContext`	`8` turns	Ensures the system can track complex query contexts while limiting memory consumption.
`Chunk size` (Segment Length)	`500–800 characters`	Accommodates longer operating procedures and batch reports in cold chain documents, ensuring semantic completeness.
`Recall count` (Recall Count)	`Top 10`	Covers real-time sensor data, WMS/TMS records, and relevant documents, improving information coverage.
`Similarity threshold` (Similarity Threshold)	`0.75`	Balances recall and precision, reducing interference from irrelevant data, especially for numerical queries.
`Rerank result count` (Reranked Return Count)	`3-5 items`	Selects the most relevant key information, particularly for anomaly events or compliance clauses.
`SYSTEM_PROMPT`	Explicitly requests citation of key fields like batch number, temperature range, and timestamp	Ensures model output includes core information specific to cold chain logistics for verification.

Common Pitfalls

Symptom: When a user queries the real-time temperature of a specific batch, the system returns empty or incomplete data. Reason: The prompt failed to clearly guide the model to prioritize information from real-time sensor data sources, or the data source interface API_TIMEOUT was set too short, causing data retrieval failure.
Symptom: During a conversation, the user repeatedly asks the same question, and the system fails to provide consistent or progressive answers. Reason: The maxContext parameter was set too low, preventing the system from effectively maintaining multi-turn conversation context and forgetting earlier dialogue content.
Symptom: The transportation recommendations provided by the system do not align with actual regulatory requirements. Reason: The prompt's weighting for document data sources was insufficient, causing the model to prioritize general knowledge over authoritative compliance documents when generating answers.

Verification Steps

For a specific batch product, simulate user queries for its real-time temperature, historical transportation records, and storage conditions. Verify if the system accurately and completely returns information from different data sources.
Conduct multi-turn dialogue tests. After 5-8 turns, verify if the system still understands user intent and answers based on previous context, for example, by asking for details about a specific anomaly event.
Randomly select questions from cold chain logistics product specifications or operating procedures. Test if the system can accurately cite the original document and provide compliance advice, while also checking if the cited batch number and temperature range are correct.
Check the system's understanding and output of units like temperature and humidity. Ensure it correctly identifies units like °C and %RH in conversations and can perform simple unit conversions or range judgments.

The values provided 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 2026-09-21.