Multiturn Conversation and Prompts for siRNA Nucleic Acid

Data Characteristics

siRNA nucleic acid drug quality documents include production batch records, inspection reports, stability study data, raw and auxiliary material quality inspection reports, and validation reports. These documents are typically in PDF, Word, or structured database formats. Data update frequency correlates with drug development stages and production batches. For example, clinical batches might update monthly, while commercial production batches generate data per batch. Document structures are complex, containing extensive specialized terminology, charts, and tables. Fields include batch number, production date, expiration date, inspection items, results, limits, and equipment numbers. Units involve concentration (nM), purity (%), endotoxin (EU/mg), and various chromatographic parameters.

Constraints on Multiturn Conversation and Prompts

The specialized and complex nature of siRNA nucleic acid drug quality documents demands higher accuracy for multiturn conversations and prompt construction. Documents contain numerous abbreviations and specific terms, requiring strong semantic understanding from the model to avoid misinterpretation. For instance, "ASO" could mean antisense oligonucleotide or have other meanings in specific contexts. Unstructured data in charts and tables requires efficient parsing and extraction to accurately cite specific data points during conversations. Furthermore, the traceability and correlation of data across batches necessitate that the model maintains context in multiturn conversations and can query and integrate information across documents based on user commands. High update frequency means the knowledge base must quickly synchronize the latest data to ensure real-time content in conversations.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`maxContext`	`8`	Ensures the model remembers enough conversation turns to handle complex traceability queries.
`Chunk size` (Segment Length)	`800-1000 characters` (characters)	Accommodates longer professional descriptions and experimental methods in documents, ensuring contextual completeness.
`Recall count` (Recall Count)	`8-12 entries` (items)	Increases the recall range to cover more potentially relevant batch or inspection item data.
`Similarity threshold` (Similarity Threshold)	`Calibrate by actual measurement` (Calibrated by actual measurement)	Optimizes for specialized terms and abbreviations, preventing recall of irrelevant content due to semantic similarity.
`Rerank result count` (Reranked Return Count)	`5 entries` (items)	Filters the most relevant content from recalled data, improving conversation quality.
`PROMPT_TEMPLATE`	`Specify query intent and required data units in detail`	Guides the model to precisely extract numerical values and units, avoiding ambiguity, e.g., "siRNA purity (%) for batch XXX".

Common Pitfalls

"Request Time out" errors or empty results in conversations. This typically occurs when processing siRNA nucleic acid drug documents, where individual files are too large or contain many images, leading to a PARSE_FILE_TIMEOUT_SECONDS parameter set too low and causing a parsing timeout.
The model fails to accurately associate data for the same inspection item across different batches in multiturn conversations. This may be because the prompt does not explicitly guide the model to perform cross-document or cross-field associative queries, leading to context loss.
Unit errors or inaccurate numerical values in conversation results. This usually happens when unit representation in original documents is inconsistent, or the prompt does not explicitly request the model to return values with specific units, causing the model to misidentify during extraction.

Validation Steps

Upload multiple representative siRNA nucleic acid drug quality documents (e.g., batch records, inspection reports). Check if all files are successfully parsed by verifying their status as "Completed" (Completed) on the FastGPT "Knowledge Base" (Knowledge Base) page.
Simulate multiturn conversations. Ask comparative questions about the same metric across different batches. Check if the model accurately cites specific batch numbers and inspection results and maintains conversation context.
Ask questions about specific numerical values and units in the document, such as "What is the purity of siRNA for batch ABC-20230101?" or "What is the endotoxin limit in EU/mg?". Verify if the model's returned values and units match the original text.
Attempt queries containing specialized abbreviations and synonyms. Observe if the model correctly understands and recalls relevant information to evaluate the effectiveness of the Similarity threshold (Similarity Threshold).

Note: The values provided are common starting points. Measure performance against your own samples to determine optimal configurations.

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.