Multi-Turn Conversations and Prompts for Cold Chain

Data Characteristics

Cold chain logistics quality documents include transportation validation reports, temperature monitoring records, equipment calibration certificates, emergency plans, Standard Operating Procedures (SOPs), and deviation reports. Data sources are diverse, encompassing temperature and humidity sensors, GPS trackers, in-vehicle systems, and manual records. Document update frequency varies by type; SOPs and emergency plans may update quarterly or annually, while temperature records and transportation reports update in real-time or by batch. Document structures are often semi-structured, containing extensive tabular data, free-text descriptions, and standardized numbers. Specific fields include Temperature Range, Humidity Threshold, Batch Number, and Expiration Date. Units commonly involve Celsius (℃), Fahrenheit (℉), percentage (%RH), and time units (hours, days).

Constraints Imposed by These Characteristics on Multi-Turn Conversations and Prompts

The semi-structured nature of cold chain logistics quality documents requires multi-turn conversation systems to flexibly handle mixed queries involving tabular data and free text. For example, a user might ask, "Did drug batch number XYZ123 exceed the 2-8℃ temperature range during transport?" This requires the system to extract batch information from tables, understand temperature ranges, and compare them against time-series data. High-frequency updates of temperature monitoring records necessitate efficient knowledge base update strategies to ensure the timeliness of conversation results. The specificity of fields and the rigor of units constrain prompt design, requiring explicit unit specification to avoid ambiguity. For instance, "temperature" could refer to a set temperature or an actual recorded temperature. Furthermore, for inspection scenarios, the conversation system must quickly locate specific regulations or SOP clauses and cite original text, demanding precise knowledge recall.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`Chunk size` (Chunk Size)	`500-800 characters`	Balances tabular data integrity and textual semantic coherence, preventing truncation of critical information.
`Recall count` (Recall Count)	`Top 5 entries`	Ensures coverage of multiple relevant document segments in complex queries while avoiding interference from irrelevant information.
`Similarity threshold` (Similarity Threshold)	Calibrate based on actual measurements	Evaluate `cosine` or `dot product` similarity using a test set to ensure recall accuracy.
`maxContext`	`32k tokens`	Accommodates the context requirements of lengthy SOPs or validation reports, reducing information loss in multi-turn conversations.
`Rerank result count` (Reranked Return Count)	`Top 3 entries`	Further filters the most relevant segments from the recalled results, improving the precision of the final answer.
`prompt`	Explicitly request original text citation and specific field units	Ensures authoritative answers and data accuracy, meeting inspection and compliance requirements.

Common Pitfalls

The conversation displays "No relevant knowledge found" even when the document contains the information. This may be due to a Similarity threshold (Similarity Threshold) set too high or an inappropriate Chunk size (Chunk Size) that splits critical information, preventing a complete match.
When a user asks for a specific batch report, the system returns other batches or general SOPs. This may be because the prompt insufficiently emphasizes key fields like Batch Number, or the knowledge base index does not fully utilize these fields.
The conversation system cannot understand vague user queries about "temperature" or "humidity," requiring users to repeatedly clarify units or specific values. This may be because the prompt does not explicitly instruct the model to include units in its answers, nor does it effectively associate numbers with units during knowledge base processing.

Validation Steps

Select a complex query containing key fields (e.g., Batch Number, Temperature Range). Observe if the system's Recall count (Recall Count) and content fully cover all relevant document segments. Compare with expected recall results.
Choose a representative temperature monitoring record and ask if there was an "over-temperature event." Verify if the system accurately identifies and cites specific timestamps and temperature values from the record. Compare with manual analysis results to ensure numerical and unit accuracy.
Simulate an inspection scenario by asking a question about a specific SOP clause (e.g., "the handling process for cold chain interruptions in the emergency plan"). Check if the system precisely locates the chapter or page number of the original SOP text and cites the corresponding text.

The values provided are common starting points and should be measured against your 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.