Multi-Turn Conversations and Prompts for Structured Analysis

Data Characteristics

Lead compound screening data originates from High-Throughput Screening (HTS) reports, activity test reports, compound library information, and preliminary Structure-Activity Relationship (SAR) analysis documents. This data typically exists in structured formats (e.g., .csv, .xlsx) and semi-structured formats (e.g., experimental reports in .pdf, research logs in .docx). Update cycles usually align with experimental batches, potentially weekly or bi-weekly. Document structures are complex, containing fields such as experimental conditions, compound ID, target information, activity data (e.g., IC50, EC50), selectivity data, and toxicity data. Activity data units vary, including nM, µM, and % inhibition. Compound structures may be represented as SMILES or InChI strings.

Constraints on Multi-Turn Conversations and Prompts

The heterogeneous nature of lead compound screening data requires multi-turn conversation systems to flexibly handle various input formats. The complexity of fields and units, especially the multiple representations of activity data, challenges prompt accuracy and robustness. This necessitates explicitly specifying desired output units or performing unit conversions. The large number of compound IDs and experimental batch information in HTS reports makes tracking context and associating different experimental results critical in multi-turn conversations. Additionally, molecular structure information from preliminary SAR analysis requires prompts to guide the model in identifying and associating structure with activity data, potentially even performing simple structural feature extraction. These constraints collectively demand stronger structured guidance and context management capabilities in prompt design.

Configuration Settings

Configuration Item	Recommended Value	Rationale for Recommendation
`maxContext`	`8000 tokens`	Lead compound screening reports are often long. Sufficient context retention is needed to understand experimental backgrounds and multi-turn follow-up questions.
`Chunk size`	`500 characters`	Individual experiment descriptions or compound information blocks in experimental reports are of moderate length, preventing information loss or redundancy.
`Recall count`	`Top 10 entries`	This ensures coverage of multiple relevant experimental results or compound information, improving recall.
`Similarity threshold`	`0.75`	This filters for highly relevant document segments, excludes irrelevant noise, and balances precision and recall.
`Rerank result count`	`Top 5 entries`	This further refines the most relevant segments, reduces the model's processing burden, and improves final answer quality.
`Model Temperature`	`0.3-0.5`	This ensures accuracy and consistency in responses, avoiding creative deviations in critical data parsing.

Common Pitfalls

Model output language mismatch: The prompt does not explicitly specify the output language, or the model has a default language preference, leading to Chinese output even if the knowledge base is in English.
API input variables not taking effect: Variable names in the prompt template do not match the key names in the variables field of the API request body, preventing correct placeholder replacement.
File parsing errors: Uploaded experimental report files have incompatible encoding formats or complex internal structures, causing the parser to fail to correctly identify text content, for example, encountering an Unsupported file format error.

Verification Steps

Submit queries containing different activity units (e.g., nM and µM). Check if the model correctly identifies and unifies the reported units.
Conduct multi-turn follow-up questions for a specific compound ID. Verify if the model maintains contextual association with that compound across different turns.
Upload documents containing SMILES strings. Ask questions about related structural features. Confirm if the model correctly extracts and understands structural information from the document.

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.