Multi-turn Conversation and Prompts for CMC Research

Data Characteristics

CMC (Chemistry, Manufacturing, and Control) research registration and declaration documents in the biopharmaceutical field primarily originate from laboratory records, production batch reports, quality control reports, and stability study reports during drug development. This data updates infrequently, typically when submitting phased development results or when manufacturing processes change. Documents come in various forms: structured experimental data tables (e.g., Excel, CSV), semi-structured analysis reports (e.g., graphs, data in Word, PDF), and unstructured text descriptions (e.g., research logs, deviation reports). Fields often involve chemical structures, spectral data, chromatograms, process parameters (e.g., temperature, pressure, time), quality attributes (e.g., purity, content, impurities), and stability data (e.g., degradation products, shelf life). The unit system is complex, frequently including both SI units and industry-specific units (e.g., ppm, ppb, IU).

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

The complexity and diversity of CMC data impose specific requirements on the accuracy of multi-turn conversations and prompt construction. Unstructured text and semi-structured reports contain extensive specialized terminology, abbreviations, and graphical data, demanding strong semantic understanding and multimodal information processing capabilities from the model. Low update frequency means historical data stability and consistency are critical; the conversation system must accurately trace information for specific batches or research phases to avoid confusion. The complex unit system and field structure require prompt design to explicitly specify data extraction formats and unit conversion rules, for example, clearly requesting "purity, unit in %" or "impurity A content, unit in ppm." Additionally, multi-turn conversations frequently need to reference compound names, batch numbers, or experiment IDs mentioned in previous turns to maintain context coherence. This makes maxContext settings and session_id management important considerations.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`maxContext`	`8000 Tokens`	Ensures coverage of key information from multiple experimental batches and analysis reports across multi-turn conversations, maintaining context coherence.
`Chunk size`	`500 characters`	Balances the granularity of knowledge base recall with information completeness, adapting to varying paragraph lengths in CMC documents.
`Recall count`	`10 entries`	Increases the breadth of relevant information retrieved from the knowledge base, addressing potential data dispersion in CMC documents.
`Similarity threshold`	`0.75`	Balances recall accuracy and recall rate, filtering for highly relevant professional document segments.
`Rerank result count`	`5 entries`	Re-ranks recall results to prioritize the most relevant core information.
`temperature`	`0.3`	Reduces the randomness of model-generated content, improving answer accuracy and consistency, aligning with the stringent requirements of registration and declaration.

Common Pitfalls

A "cannot read this file" message appears in the conversation. This occurs because the knowledge base fails to correctly parse an uploaded xlsx file, preventing the model from extracting structured data.
During multi-turn conversations, the model repeatedly mentions issues already resolved in previous turns or confuses data from different batches. This results from insufficient maxContext settings, leading to loss of contextual information.
Model output for variables shows overlapping content, for example, variable B includes content from variable A. This typically happens when prompt design fails to clearly define the boundaries of different variables, causing the model to merge content from multiple variables during generation.

Verification Steps

Conduct multi-turn conversation tests for typical CMC registration and declaration questions. Check if the model accurately references and distinguishes specific data and parameters from different batches and experiments. Verify the context retention capability of session_id.
Upload PDF analysis reports containing complex charts and specialized terminology. In the conversation, ask the model to summarize key conclusions or data points from the report. Verify the accuracy of information extraction.
For queries involving specific units (e.g., µg/mL or kPa), check if the model's output data includes the correct units and verify numerical accuracy. This confirms the effectiveness of prompt constraints on unit conversion and field extraction.

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.