Multi-turn Conversation and Prompts for Structured Analysis

Data Characteristics in This Category

Tender and bidding documents in the biopharmaceutical industry primarily originate from drug procurement platforms, medical device procurement centers, and scientific research project bidding websites. These documents update frequently, often with new tender announcements weekly or monthly, accompanied by supplementary notices and winning bid results. Document formats vary, primarily PDF and Word, with some platforms offering HTML pages. The degree of content structuring is inconsistent. Documents typically include key fields such as project name, tender number, procuring entity, budget amount, qualification requirements, technical specifications, evaluation criteria, and contract terms. Technical specifications often involve drug ingredients, dosage forms, specifications, production processes, and clinical trial data. These fields frequently contain specific units like mg, ml, μg/kg, IU, and sometimes abbreviations or aliases for units, increasing parsing difficulty.

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

The data characteristics of tender and bidding documents impose specific requirements on the design of multi-turn conversations and prompts. First, diverse and frequently updated document sources necessitate continuous knowledge base maintenance and incremental updates. The conversation system must handle new or modified document content and avoid interference from old information. Second, varied document formats and inconsistent structuring demand robust information extraction capabilities during structured parsing. The system must accurately identify and extract key fields and their values from unstructured text. In multi-turn conversations, users may inquire about qualification requirements for different tender batches or procuring entities for a specific drug. This requires the system to identify and link relevant information across different documents. Finally, complex biopharmaceutical terminology and measurement units in technical specifications require prompt design to guide the model in correct understanding and generation, preventing information deviation due to unit confusion or misinterpretation of professional terms.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`Chunk size`	`500–800 characters`	Tender document paragraphs are typically long, containing multiple pieces of information. Shorter segments risk losing context; longer segments introduce irrelevant information, affecting recall precision.
`Recall count`	`Top 8 entries`	Tender documents have high information density. More relevant paragraphs are usually needed to support complex queries, ensuring coverage of all potentially relevant information points.
`Similarity threshold`	`0.78–0.85`	Tender documents are highly specialized with fixed terminology. A higher threshold helps exclude semantically similar but irrelevant content, improving accuracy.
`maxContext`	`4096 tokens`	Complex multi-turn follow-up questions and comparisons require maintaining a longer conversation history to prevent loss of critical information and conversation interruption.
`temperature`	`0.3–0.5`	Tender information requires accurate presentation. A lower temperature value reduces the model's freedom to generate, ensuring objectivity and accuracy of responses.
`systemPrompt`	Detail tender document structure and query intent	Precisely guide the model to understand the specific structure of tender documents, such as project number and budget amount, and focus on extracting key business information.

Three Common Mistakes

Incorrect budget amounts or qualification requirements for a tender project appear in the conversation. This occurs because numbers and units were not correctly identified during document parsing, or the knowledge base contains outdated data.
When a user asks about clinical trial data for a specific drug, the system repeatedly provides irrelevant production process information. This happens because the prompt did not clearly distinguish between different types of technical specification fields, leading to model confusion.
When an AI conversation node in a workflow is followed by a code execution node, the conversation output still includes the model's thought process. This is because the thinking tag was not correctly removed or processed, leading to redundant output.

How to Confirm Correct Configuration

Conduct multi-turn conversation tests with typical tender announcements. Verify the model's accuracy in extracting key fields like project name, budget amount, and procuring entity, and check for unit matching.
Submit queries containing specialized terminology and measurement units. Verify the model's ability to correctly understand and provide industry-standard answers, for example, querying recombinant human interferon α2b for injection with 1 million IU specifications.
Simulate document update scenarios. Upload new versions of tender announcements or supplementary notices. Test whether the conversation system prioritizes the latest information and provides updated results for historical queries.

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.