Multi-turn Conversation and Prompts for Cold Chain Logistics

Data Characteristics

Cold chain logistics pharmacovigilance data originates from temperature and humidity monitoring systems, transport management systems, warehouse management systems, and adverse event reporting systems. Temperature and humidity data are typically time-series, with high update frequencies, potentially minute-level or even second-level. Transport records include structured information such as batch numbers, drug names, origin, destination, transport routes, and start/end times. Warehouse data covers inventory, inbound/outbound movements, and expiration dates. Adverse event reports are often unstructured text, describing event time, location, drug information, adverse reaction symptoms, and handling measures. These are usually manually entered or imported from external systems, with lower update frequencies. Some data may exist as PDF transport documents or Excel statistical reports.

Constraints from Data Characteristics on Multi-turn Conversations and Prompts

High-frequency temperature and humidity time-series data require precise timestamp queries and range filtering in multi-turn conversations. Prompt design must guide the model to recognize timestamps and numerical intervals. Unstructured adverse event reports require prompts to effectively extract key entities (e.g., drug names, symptoms, batch numbers) and handle user follow-up questions about report details. The complexity of multi-source data integration necessitates the conversation system's ability to query information across systems; prompts should explicitly specify data sources. Additionally, cold chain logistics involves numerous specialized terms and acronyms. Prompts must consider vocabulary and entity recognition accuracy to avoid conversation breakdowns or misunderstandings due to unfamiliar terminology. Precise matching of critical identifiers like drug batch numbers and serial numbers places high demands on multi-turn conversation context understanding and entity resolution.

Configuration Settings

Configuration Item	Suggested Value	Rationale for this Value
`maxContext`	`8000 tokens`	Balances long conversation history and complex queries, handling multi-turn follow-ups and contextual dependencies.
`Chunk size` (Segment Length)	`512 characters` (characters)	Suitable for scenarios mixing structured data and unstructured text, balancing recall accuracy and processing efficiency.
`Recall count` (Recall Count)	`Top 10 entries` (top 10 items)	Ensures coverage of potentially relevant information across large volumes of temperature/humidity data and adverse event reports.
`Similarity threshold` (Similarity Threshold)	`0.75`	Improves query result precision and reduces interference from irrelevant information, especially for critical batch number matching.
`Rerank result count` (Rerank Return Count)	`Top 5 entries` (top 5 items)	Further refines recall results, prioritizing the display of cold chain anomalies or adverse reaction reports most relevant to user intent.
`Temperature Unit`	`Celsius`	Standard unit in the cold chain logistics industry, ensuring accuracy in data parsing and conversational feedback.

Three Common Mistakes

Symptom: The AI cannot accurately answer questions about temperature anomalies for specific drug batches during a conversation. Reason: Temperature and humidity data in the knowledge base are not correctly associated with drug batches, or prompts do not effectively guide the model to perform multi-field joint queries.
Symptom: When a user asks for detailed content of an adverse event report, the AI only returns summary information. Reason: When processing unstructured adverse event text, the Chunk size (Segment Length) in the knowledge base is too short, leading to truncation of critical details, or the Recall count (Recall Count) is insufficient to cover the complete report.
Symptom: Input guidance is enabled, but the user does not see the expected guiding questions in the conversation interface. Reason: The trigger conditions for Input Guidance are misconfigured, failing to match common query patterns in cold chain logistics pharmacovigilance, or the guidance vocabulary does not include common questions in this domain.

How to Confirm Proper Configuration

Simulate various cold chain anomaly scenarios (e.g., temperature excursions, transport delays) to verify if the AI can accurately identify and provide detailed information and handling suggestions for relevant batches.
Input key information from multiple adverse event reports (e.g., drug name, symptoms) to check if the AI's returned report content is complete and without omissions.
Test queries containing specialized terms and acronyms to confirm if the AI can correctly understand and provide accurate responses, evaluating the impact of Similarity threshold (Similarity Threshold) on term matching.
Conduct multi-turn conversation tests to observe the AI's performance in context understanding, historical information referencing, and follow-up question handling, evaluating the practical effect of maxContext.

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.