Multi-turn Conversation and Prompts for Peptide Drug

Data Characteristics

Peptide drug registration data comes from various sources. These include R&D lab records, clinical trial reports, pharmaceutical research data, quality control reports, and regulatory documents. Data update frequencies vary. R&D data may update in real-time, while clinical reports or regulatory changes are periodic. Document structures typically follow the CTD (Common Technical Document) format from regulatory bodies like NMPA, FDA, or EMA, covering Modules 1 to 5. For peptide drugs, the pharmaceutical section details peptide sequences, modification types, synthesis processes, purification methods, structural confirmation, impurity profiles, and stability data. Fields include amino acid sequences, molecular weight, isoelectric point, chromatographic purity, impurity content, residual solvents, and endotoxin levels. Units include Dalton (Da), % (percentage), ppm (parts per million), and EU/mg (endotoxin units/milligram). These are specific to biopharmaceuticals.

Constraints on Multi-turn Conversation and Prompts

The diverse and complex structure of peptide drug registration data demands advanced context understanding in multi-turn conversations. For example, minor peptide sequence variations can significantly alter pharmacological activity. The AI must accurately identify and differentiate these details during conversations. The hierarchical CTD format requires conversations to support information correlation and traceability across sections and modules. This addresses user queries about specific quality attributes across different experimental stages. Peptide drug-specific terminology and units require prompt design to include precise domain glossaries and unit parsing rules. This prevents incorrect answers due to terminology misunderstandings. For time-series data like stability data, the conversation system must handle time-dimension queries, such as "What was the purity of a certain peptide at month 6 under accelerated stability conditions?"

Configuration Settings

Configuration Item	Suggested Value	Rationale
`maxContext`	`8` turns	Balances multi-turn conversation coherence with computational resource consumption. Covers typical follow-up questions.
`Chunk size` (Segment Length)	`500–700 characters`	Adapts to paragraph lengths in CTD documents. Ensures semantic integrity and prevents truncation of key information.
`Recall count` (Recall Count)	`7` items	Improves accuracy when retrieving relevant information from vast submission documents. Covers more potential related documents.
`Similarity threshold` (Similarity Threshold)	`0.75`	Balances recall and precision. Ensures retrieved documents are highly relevant to peptide drug-specific questions.
`Rerank result count` (Reranked Return Count)	`3` items	Selects a small number of the most relevant documents for in-depth analysis. Improves the accuracy of the final answer.
`ENABLE_IMAGE_RECOGNITION`	`true`	Registration documents often contain critical images like chromatograms and mass spectra. Image recognition enhances information acquisition.

Common Mistakes

The AI responds with "Cannot provide image content, as I am a text-based artificial intelligence." This occurs when the AI conversation node is not correctly enabled or the model does not fully support image recognition.
The AI cites irrelevant documents in its answer. This happens when the Similarity threshold (Similarity Threshold) is set too low, causing low-relevance information to be recalled.
After exceeding the expected number of conversation turns, the AI "forgets" or its answers become incoherent. This manifests as an inability to correctly understand peptide sequences or experimental conditions mentioned in previous turns. This is due to a small maxContext parameter, leading to truncation of historical conversation information.

How to Confirm Configuration

Conduct multi-turn conversation tests. Ensure the AI accurately traces and references peptide names, sequences, or experimental parameters mentioned in previous turns within 5-8 turns.
Upload submission documents containing images like chromatograms and mass spectra. Ask questions about the image content. Verify if the AI can correctly identify and describe key information in the images.
Query a specific quality attribute of a peptide drug (e.g., "purity of a certain batch at month 3 under accelerated stability conditions"). Observe if the AI can extract precise values and units from relevant documents.
Intentionally introduce variations or abbreviations of specialized terms in questions. Check if the AI can correctly understand and provide expected answers. This reflects the coverage of the domain glossary.

Note: The values provided are common starting points. Measure them against your 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.