Data Characteristics
Biopharmaceutical HR report data originates from internal HR systems, external recruitment platforms, specialized talent databases, and research project management systems. Data update frequencies vary. Internal system data may update in real-time. External data might synchronize weekly or monthly. Reports typically exist as structured or semi-structured documents, such as PDFs, Word files, or JSON. Key fields include name, education, major, work experience (company, position, start/end dates), project experience, skill tags, research achievements (papers, patents), and performance evaluation results. Some fields contain biopharmaceutical-specific terminology and abbreviations, such as compound names, mechanisms of action, disease targets, and experimental techniques (e.g., PCR, ELISA). Data volumes are often large; a single report can span several pages, and field value lengths vary significantly.
Constraints Imposed by Data Characteristics on Multi-turn Conversations and Prompts
The semi-structured nature and specialized terminology of HR reports demand strong entity recognition and intent understanding capabilities from multi-turn conversations. Frequently updated data sources require the conversation system to refresh its knowledge base regularly, ensuring query results are current. Varying field value lengths, such as a single line for work experience versus hundreds of words for project descriptions, challenge prompt design for effective context window utilization. Queries may also involve cross-referencing multiple reports, for example, "find all PhDs with gene editing experience who published in Nature within the last three years." This requires the system to handle complex multi-conditional queries and information integration. The accuracy of specialized terminology recognition directly impacts query precision. Prompt design must guide the model to focus on this critical information.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000 tokens | Accommodates complex queries and lengthy report segments, ensuring complete context. |
temperature | 0.3 | Reduces model generation randomness, improving query result stability and accuracy. |
Recall count | 15 entries | Covers enough potentially relevant report segments, enhancing recall for complex queries. |
Similarity threshold | 0.75 | Filters out irrelevant or weakly relevant documents, improving the precision of recall results. |
Chunk size | 500 characters | Balances segment completeness and retrieval efficiency, preventing overly long segments from diluting key information. |
Rerank result count | 5 entries | Ensures the most relevant report segments are presented to the user, reducing redundant information. |
Common Pitfalls
- Observation: User queries for specific skilled talent, but the results lack critical information. Reason: The prompt failed to effectively guide the model to focus on "skill tags" or "project experience" fields in the report. This led the model to overlook these specialized key data points during retrieval or summarization.
- Observation: The workflow includes web search, but after a user question, the model provides an answer directly without triggering the web search. Reason: The prompt's conditions for invoking the web search tool are unclear, or the model misidentified the user's intent as directly answerable knowledge, failing to meet the web search trigger threshold.
- Observation: In a multi-turn conversation, summary information from a previous turn is repeated in subsequent turns. Reason: The workflow design did not effectively trim or filter intermediate step outputs, causing unnecessary intermediate results to be passed to the final output stage.
How to Verify Configuration
- Prepare a set of test questions covering typical talent query scenarios, including specialized terminology and complex conditions. Observe if the model accurately identifies intent, invokes the correct tools, and provides expected results.
- Check the knowledge base update strategy. Ensure newly onboarded or updated HR reports are indexed by the system and available for query within the specified timeframe.
- Analyze multi-turn conversation history. Verify if the model correctly maintains context across different turns and adjusts subsequent query strategies based on prior conversations.
- Perform statistical analysis on frequently queried fields. Confirm that the recall rate and accuracy for these fields meet the expected thresholds. Thresholds can be set based on business needs and data characteristics.
The values provided above are common starting points. They should be measured 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.