Data Characteristics in this Category
Cold chain logistics data primarily comes from internal company regulations, operational manuals, compliance documents, emergency plans, and technical standards. These documents are typically in PDF, Word, or scanned image formats. Updates are driven by policy changes, technological advancements, and internal management requirements, usually occurring quarterly or annually. Some emergency plans may be revised quickly after an incident. Document structures include nested section headings, numbered lists, flow chart descriptions, and tabular data. Fields and units involve temperature (Celsius, Fahrenheit), humidity (percentage), time (hours, days), weight (kilograms, tons), specific equipment models, batch numbers, and serial numbers. Strict requirements exist for numerical precision and unit consistency.
Constraints Imposed by These Characteristics on Multiturn Conversation and Prompts
The characteristics of cold chain logistics regulatory documents impose specific requirements on multiturn conversation and prompt design. First, the moderate update frequency of documents requires the knowledge base to have version management capabilities. This ensures the AI always answers based on the latest regulations. Second, complex document structures mean the AI must accurately understand context, especially when dealing with process steps or multi-condition judgments. Multiturn conversations are needed to progressively clarify user intent, avoiding misunderstandings due to insufficient information in a single query. The large number of specialized terms and units requires prompt design to guide the model in correctly identifying and processing this information, for example, distinguishing between "minus 18 degrees Celsius" and "18 degrees Celsius." The presence of flowcharts and tabular data requires the Q&A system to extract structured information from unstructured text and present it clearly in conversations.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 6 | Covers the contextual depth of common process Q&A, preventing information loss. |
Chunk size (Segment Length) | 800–1000 characters | Adapts to the paragraph length of regulatory documents, ensuring semantic completeness. |
Recall count (Recall Count) | Top 8 entries | Increases coverage of relevant segments, addressing complex cross-references in regulations. |
Similarity threshold (Similarity Threshold) | 0.78 | Balances recall precision and completeness, reducing interference from irrelevant information. |
Rerank result count (Reranked Return Count) | Top 3 entries | Focuses on the most relevant regulatory clauses, improving answer accuracy. |
prompt_template | Calibrated by actual measurements | Guides the model to focus on critical fields like temperature and batch numbers, and to handle multi-condition queries. |
Three Common Mistakes
- Symptom: AI answers include outdated regulatory clauses or fail to mention the latest revisions. Reason: The knowledge base was not updated promptly, or the version management mechanism was misconfigured, leading the model to cite old document versions.
- Symptom: When a user asks about "cold storage temperature standards," the AI cannot distinguish temperature requirements for different categories of goods and provides a general answer. Reason: The prompt failed to effectively guide the model to identify and differentiate specific regulations for various categories within the document, or category information was lost during document segmentation.
- Symptom: In a conversation, a user asks about "transportation records for a specific batch of vaccines," and the AI responds "no relevant information found," even though the information exists in the document. Reason: The prompt failed to effectively guide the model to identify specific fields like "batch," or failed to correctly parse user-provided query conditions in a multiturn conversation.
How to Verify Configuration
- Select typical cold chain logistics operational procedures. Simulate user questions and check if the AI's answers accurately cite the latest regulatory provisions.
- For critical parameters like temperature and humidity, design questions containing specific values and units. Verify if the AI can correctly identify them and provide ranges or standards compliant with regulations.
- For complex questions involving multiple conditions (e.g., "specific cargo storage requirements below minus 18 degrees Celsius and above 70% humidity"), conduct multiturn conversation tests. Confirm that the AI can progressively understand user intent and provide accurate answers.
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.