Multiturn Conversation and Prompts for Structured Analysis

Data Characteristics

mRNA vaccine R&D documents cover various stages. These stages include basic research, preclinical trials, clinical trials, manufacturing processes, and quality control. Data sources are diverse. They include research papers, patent applications, experimental reports, batch production records, regulatory documents, and clinical trial protocols. Documents are often in PDF, Word, or Excel formats. Their structure is complex and varied. Update frequency is high, especially in early R&D. Experimental data and results iterate rapidly. Documents contain extensive specialized terminology, biochemical formulas, charts, and tabular data. Fields and units are highly specific. Examples include nucleotide sequences, modified base types, lipid nanoparticle (LNP) component ratios, antigen expression levels (ng/mL), and immunogenicity indicators (ELISA titers, T-cell activation percentage).

Constraints on Multiturn Conversation and Prompts

The complexity of mRNA vaccine R&D documents imposes specific requirements on multiturn conversation and prompt design. Extensive specialized vocabulary and biochemical symbols demand high-precision language model comprehension. This prevents semantic drift. Key data in charts and tables, such as LNP encapsulation efficiency or antibody titers, require structured parsing for effective retrieval and citation. This directly impacts the accuracy of data references in conversations. High update frequency of experimental data, such as new cell line transfection results or animal model immune response data, requires rapid knowledge base synchronization. This ensures the timeliness and authority of conversation content. In multiturn conversations, users may need to track a specific molecule's performance under different experimental conditions. Prompts must guide the model to establish connections between multiple document segments and perform logical reasoning. An example is tracing preclinical cell experiment data back to specific batch humanized antibody production records.

Configuration Guidelines

Configuration Item	Suggested Value	Rationale
`Chunk size` (Chunk Size)	`500–800 characters`	Balances contextual completeness with information density per chunk, reducing noise.
`Overlap Length`	`50–100 characters`	Ensures semantic continuity across chunk boundaries, aiding RAG recall.
`Similarity threshold` (Similarity Threshold)	`0.75–0.85`	Improves retrieval precision for highly similar texts in specialized domains, reducing irrelevant information.
`Recall count` (Recall Count)	`5–8 items`	Covers information from multiple angles, preventing omission of critical experimental data or regulatory clauses.
`maxContext`	`8192 tokens`	Accommodates long documents and complex reasoning scenarios, ensuring complete context in multiturn conversations.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Addresses parsing time for large experimental reports and clinical trial protocols.

Common Pitfalls

Missing key data or incorrect citations in conversations may occur. This can happen if numerical data in charts or tables are not extracted correctly during document parsing.
Knowledge base retrieval results may not align semantically with user queries, or they may return many irrelevant snippets. This could be due to the vector indexing model's insufficient understanding of specialized mRNA vaccine terminology and biochemical entities, leading to semantic embedding bias.
The model may fail to maintain long-term memory of specific molecules or experimental conditions in multiturn conversations, leading to context breaks. This relates to a maxContext parameter set too low or prompts failing to effectively guide the model to recall historical conversations.

Verification Steps

Upload mRNA vaccine R&D documents containing critical numerical charts and tables. Verify the accuracy of corresponding field data in the parsed knowledge base.
Ask multiple questions about a specific mRNA vaccine molecule (e.g., an mRNA sequence encoding the Spike protein). Observe if the model consistently tracks information across different experimental stages for that molecule.
Test knowledge base recall results for specific technical terms (e.g., pseudo-uridine, LNP, adjuvant). Manually evaluate relevance and coverage to calibrate the Similarity Threshold.

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.