Multi-turn Conversation and Prompts for Batch Record Audit

Data Characteristics for This Category

Batch records are comprehensive document sets detailing the entire drug production process in biopharmaceutical manufacturing. Data sources include manual entries by production floor personnel, data exports from automated equipment, and quality control laboratory test reports. Update frequency typically aligns with production batches; a complete set of batch records is generated after each batch, with update cycles ranging from several days to several weeks. Document structure is highly standardized, usually comprising sections such as batch information, material requisition, manufacturing process parameters, in-process control, equipment cleaning, deviation records, product yield, and quality inspection results. Fields and units adhere to strict industry standards; for example, temperature units are ℃, pressure units are MPa or bar, time units are min or h, and material batch numbers and equipment IDs follow specific naming conventions and checksums. Documents often include unstructured information like tables, chromatograms, handwritten annotations, and signatures.

Constraints Imposed by These Characteristics on "Multi-turn Conversation and Prompts"

The highly standardized structure and strict field unit specifications of batch records require multi-turn conversation systems to possess high-precision entity recognition capabilities to avoid unit confusion or misinterpretation of values. Their relatively low update frequency means model training and knowledge base construction can use periodic batch updates, ensuring data consistency. The unstructured information within documents, such as handwritten annotations and chromatograms, necessitates multimodal processing; the conversation system must be able to recognize and associate image content. Multi-turn conversation context management must be robust enough to handle cross-section and cross-document information traceability requirements that may arise during the audit process. For example, when a user asks about the reason for a deviation record, the system may need to trace back to material requisition records or equipment cleaning records. Additionally, strict compliance requirements mandate that the conversation system clearly indicates data sources when providing information and assesses the credibility of results.

Configuration Guidelines

Configuration Item	Suggested Value	Rationale for This Value
`maxContext`	`20 turns`	Batch record audits may involve tracing information across multiple stages, requiring a longer conversation history to maintain context.
`Chunk size` (Segment Length)	`800–1200 characters`	Batch record sections are often lengthy; overly short segments can split critical information, while overly long ones increase recall noise.
`Recall count` (Recall Count)	`top 5`	Ensures enough relevant document segments are recalled to cover multiple evidence points potentially involved in an audit.
`Similarity threshold` (Similarity Threshold)	Calibrate by actual measurement	Batch record content is highly specialized; repeated adjustments with test datasets are needed to ensure precise recall.
`Model Temperature`	`0.1–0.3`	Batch record audits demand accurate and objective results; a low model temperature helps reduce hallucinations and divergent answers.
`Support Image Recognition`	Enable	Batch records often contain image information such as chromatograms, signatures, and handwritten annotations, requiring multimodal capability.

Three Common Pitfalls

During a conversation, the system fails to retrieve key data for a specified batch. This may be due to the knowledge base index not including the latest batch data, or the query command lacking critical qualifiers like the batch number.
After enabling the multimodal model, uploading batch record images results in "cannot provide image content." This typically occurs because the AI Conversation Node does not have the Image Recognition option configured or uses a model that does not support multimodal capabilities.
FastGPT's response references irrelevant batch record files. This may be because the Similarity threshold (Similarity Threshold) is set too low, leading to the recall of irrelevant documents, or Rerank result count (Reranked Return Count) is improperly configured, failing to effectively filter low-quality results.

How to Confirm Proper Configuration

Select a typical batch record audit scenario. Simulate a multi-turn conversation to confirm the system can accurately answer questions involving different sections and fields.
Upload batch record images containing critical chromatograms or handwritten annotations. Verify the system can correctly identify and associate information within the images.
Construct a test batch record containing known erroneous information. Ask the system relevant questions to confirm it can identify and pinpoint the source of the erroneous information.

Note: The values provided are common starting points. Measure performance against your own samples to determine optimal configurations.

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.