Multi-Turn Conversations and Prompts for Structured Analysis

Data Characteristics

Peptide drug R&D documents primarily include experiment reports, patent literature, clinical trial data, synthesis batch records, and quality control files. Data sources are diverse, covering internal lab systems, public databases (e.g., PubChem, PDB), and regulatory agency filings. Update frequency varies: experiment reports and batch records may update daily, while patent and clinical data are released quarterly or annually. Document structures are complex, often containing extensive unstructured text, tables, graphs, and images. Fields are highly specific, such as peptide sequences, modification types, purity, yield, biological activity units (e.g., IC50, Ki, typically in nM or μM), stability data, synthesis parameters (e.g., temperature °C, time h, pH), and mass spectrometry (m/z) or NMR spectra.

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

The high precision required for peptide sequences and modification information demands accurate extraction and comparison of these critical structural details during multi-turn conversations, preventing misinterpretation due to ambiguity. The large volume of spectral and image data necessitates multi-modal parsing capabilities. Prompt design must guide the model to extract structural, activity, or purity information from images. Standardized unit handling is another key constraint; different literature or experiments may use varying concentration or activity units, requiring identification and conversion within conversations. Rapid document updates require the knowledge base to quickly synchronize new data, and the multi-turn conversation system needs real-time or near real-time data querying capabilities. Furthermore, the legal and compliance requirements for patent and clinical data make verifying information sources and citation accuracy crucial. Prompts must guide the model to provide sources in its responses.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`maxContext`	`8192` or higher tokens	Peptide sequences and experimental data often have long contexts, requiring more token capacity to maintain completeness.
`Chunk size` (Segment Length)	`800–1200 characters`	Balances semantic completeness with efficient chunk retrieval, preventing truncation of long sequences or complex tables.
`Similarity threshold` (Similarity Threshold)	`0.75–0.85`	Ensures high-precision retrieval of specialized terms and experimental details related to peptide sequences and structures.
`Recall count` (Recall Count)	`8–12 entries`	Increases coverage of relevant document segments, addressing cases where peptide information is scattered across documents.
`Rerank result count` (Reranked Return Count)	`4–6 entries`	Selects the most relevant results from a high recall set, improving the specificity and accuracy of the final answer.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Parsing peptide R&D documents, which contain complex tables, graphs, and large amounts of text, can be time-consuming.

Three Common Mistakes

Symptom: Peptide sequences or activity data in responses are incorrect or missing (e.g., IC50 values do not match the original text or units are absent). Reason: Prompts fail to explicitly instruct the model to focus on and precisely extract data in specific formats, or critical data is truncated during knowledge chunking.
Symptom: When a user asks about mass spectrometry data in an image, the model provides no effective answer or states "cannot process image information." Reason: The system is not correctly configured with a multi-modal model, or the image preprocessing step fails to convert key data from spectra into text or structured information that the model can understand.
Symptom: The model "forgets" previously mentioned peptide modification types or synthesis conditions in a multi-turn conversation, requiring the user to repeat information. Reason: The maxContext parameter is set too low, leading to truncation of historical conversation context and preventing the model from maintaining coherent understanding over long dialogues.

How to Confirm Correct Configuration

Select a long document containing typical peptide sequences, modifications, activity units, and synthesis conditions. Conduct multi-turn questioning and verify that the model's extraction of key information, especially numerical values and units, matches the original text.
Upload documents containing critical information such as mass spectrometry or NMR spectra. Ask questions to verify if the model can identify and describe relevant data points or structural features from the images (e.g., "What is the m/z peak value in the graph?").
Simulate a prolonged R&D discussion, engaging in multi-turn, in-depth questioning about a peptide project. Observe whether the model consistently tracks and utilizes historical conversation context, avoiding repetitive questions or loss of critical details.

Note: The values provided are common starting points. Measure them against your own samples for optimal performance.

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.