Multi-turn Conversations and Prompts for Structured Analysis

Data Characteristics for This Category

Quality documents are core assets in biopharmaceutical R&D. These include Standard Operating Procedures (SOPs), batch production records, inspection reports, validation protocols and reports, quality standards, and deviation and change control documents. Data typically originates from enterprise document management systems (EDMS) or quality management systems (QMS), stored as PDFs, Word files, or scanned images. Updates are relatively stable, usually occurring after regulatory changes, process modifications, or periodic reviews. Documents have a strict structure, containing numerous tables, graphs, and specialized terminology. Examples include pharmacopoeia names, CAS numbers, batch numbers, production dates, expiration dates, inspection items, limits, analytical methods, and instrument numbers. Fields often include clear units such as mg/mL, ppm, °C, pH values, and AU values.

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

The strict structure and specialized terminology of quality documents require multi-turn dialogue systems to accurately identify and link to specific fields and data within documents when understanding user intent. For example, if a user asks, "What is the content of component X in batch XYZ?", the system must differentiate between the batch number and general descriptions, then accurately extract the numerical value from tables. Document update frequency is relatively fixed, but each update may involve extensive content revisions. This demands that the knowledge base's indexing update mechanism effectively handles content changes, avoiding references to outdated information. The presence of numerous specialized fields and units places higher demands on prompt engineering. Clear instructions and examples are needed to guide the model in unit conversions or range judgments. Furthermore, due to regulatory compliance requirements for quality documents, dialogue accuracy and traceability are critical. The system must cite original sources and avoid generating fabricated content.

Configuration Settings

Configuration Item	Recommended Value	Rationale for This Value
`maxContext`	`32000 token`	Ensures the ability to handle multi-turn context in complex queries and long retrieved document snippets, preventing information loss.
`Chunk size` (Segment Length)	`800–1200 characters` (characters)	Accommodates long sentences and detailed descriptions that may appear in quality documents, while retaining sufficient contextual information.
`Recall count` (Recall Count)	`Top 5–8 entries` (top 5–8 items)	Improves hit rate for complex queries, covering relevant information that may be distributed across different paragraphs in quality documents.
`Similarity threshold` (Similarity Threshold)	`0.78–0.85`	Appropriately relaxes the threshold to capture a wider range of specialized term matches while ensuring recall relevance.
`Rerank result count` (Reranked Return Count)	`Top 3 entries` (top 3 items)	Focuses on the most relevant document snippets, reducing the model's burden of processing irrelevant information.
`Custom System Instruction`	Includes "As a Quality Management Expert，Answer Rigorously" (As a quality management expert, answer rigorously)	Guides the model to respond with a professional and rigorous tone and logic, meeting the requirements of the quality management domain.

Three Common Mistakes

The chat interface shows no knowledge base selected, but it works correctly in debug preview. This usually happens if the "Enable Knowledge Base" parameter in the application's "Knowledge Base" module is unchecked, or if the "Knowledge Base" dropdown is not bound to the correct knowledge base ID.
The model produces factual errors or "hallucinations" in its answers and cannot provide original document sources. This may be due to a Similarity threshold (Similarity Threshold) set too low, leading to the recall of irrelevant document snippets, or too few Rerank result count (Reranked Return Count), meaning the model did not receive enough context.
For queries involving units or numerical ranges, the model's answer is inaccurate or it cannot make a judgment. This typically occurs because the prompt does not explicitly instruct the model to focus on numerical values and units, or it lacks targeted few-shot examples to guide the model in processing this type of information.

How to Confirm Correct Configuration

For core quality document types, such as SOPs or batch production records, create a set of complex queries involving numerical values, units, and specialized terminology. Verify if the model can accurately extract information and cite original sources.
After a simulated document update, submit relevant queries. Check if the model can identify and cite the latest version of the content, or if it indicates that old version information is invalid.
Use the API interface to query historical chat records for a specific chatId. Verify if the system can accurately retrieve and display the multi-turn interaction process between the user and the application.

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.