Multi-turn Conversation and Prompts for Peptide Drug

Data Characteristics

Peptide drug regulation and Standard Operating Procedure (SOP) documents originate from regulatory authority guidelines, internal Quality Management System (QMS) files, and specific R&D, production, and quality control procedures. These documents are typically in PDF, Word, or scanned image formats. They feature a rigorous structure, extensive technical terminology, parameters, and flowcharts. Update frequency is relatively stable; regulations may update annually or every few years, while internal SOPs revise periodically based on R&D progress and production process optimization. Documents often cover peptide sequences, synthesis processes, purification methods, quality control standards (e.g., purity, impurities, content), and stability studies. Units commonly include percentages (%), milligrams (mg), microliters (µL), moles (mol), and degrees Celsius (°C). High precision and compliance are critical for numerical values.

Constraints on Multi-turn Conversation and Prompts

The specialized, rigorous, and infrequently updated nature of peptide drug documents imposes specific constraints on multi-turn conversations. The model must precisely understand user intent and retrieve the most relevant, original-text-level snippets from the knowledge base. The extensive technical terms and abbreviations in the documents require prompt design to guide the model in correctly identifying and contextualizing these terms. Infrequent content updates allow for a knowledge base built with an emphasis on deep analysis and cross-referencing, reducing frequent data synchronization overhead. For questions involving specific values, units, and operating procedures, the model must accurately extract and reiterate the original content, avoiding generalization or free-form responses. This requires prompts to explicitly emphasize that answers must be "based on the knowledge base original text." Additionally, interpreting regulatory clauses requires the model to understand the rigor of legal texts, avoiding misinterpretation or over-interpretation.

Configuration Settings

Configuration Item	Suggested Value	Rationale
`maxContext`	`2000 characters`	Ensures sufficient context retention in multi-turn conversations for complex peptide synthesis processes or quality control standards, based on empirical `token` limits.
`chunkLength`	`400 characters`	Peptide regulation document paragraphs are often long, containing multiple steps or parameters. This length helps maintain semantic completeness and prevents critical information from being split.
`recallCount`	`top 5`	Given the specialized and precise nature of peptide regulations, recalling more items increases the coverage of relevant information and reduces missed retrievals.
`similarityThreshold`	`0.78`	Ensures retrieved snippets are highly relevant to the user's query, filtering out semantically ambiguous or irrelevant content. This value is calibrated through actual testing.
`citationTemplate`	`The following is relevant knowledge: {{context}}. Please answer the question based on the above knowledge: {{question}}.`	Clearly informs the model about the cited content and question, ensuring it understands that its answer must be based on the provided knowledge snippets, reducing hallucinations.
`rerankCount`	`top 3`	Further filters the recalled results to improve the precision and conciseness of the final answer, avoiding interference from redundant information.

Common Mistakes

Symptom: API call responses differ significantly from online chat results, with much irrelevant information. Reason: When making API calls, the stream parameter is set to false, and the detail parameter is true, but the prompt does not explicitly instruct the model to strictly answer based on cited content. This leads the model, in non-streaming mode, to generate longer answers that may deviate from the knowledge base.
Symptom: When a user asks about "the purification steps of a certain peptide," the model provides only general descriptions instead of specific process flows. Reason: The knowledge base chunking strategy is unreasonable, splitting a complete purification process into multiple small paragraphs. This prevents the retrieval from obtaining complete contextual information.
Symptom: The model cannot correctly understand or explain abbreviations in the document (e.g., "HPLC," "GMP"). Reason: The knowledge base lacks a unified explanation or glossary for these specialized abbreviations, and the prompts do not guide the model to interpret terminology.

Verification Steps

Ask multi-turn questions about core issues such as peptide drug synthesis steps and quality control indicators. Check if the model can consistently and accurately cite the original text from the knowledge base, and track whether the citation content includes critical information.
Randomly select professional terms or regulatory clauses from the knowledge base. Ask the model about their meaning or application scenarios. Verify the professionalism and accuracy of the model's answers, ensuring consistency with the original text.
Simulate user queries containing typos or synonyms. Check if the model can still recall relevant documents and adjust the similarityThreshold based on the similarity score of the recalled results.

Note: The values provided are common starting points and should be measured 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.