Multi-turn Conversation and Prompts for Structured Analysis

Data Characteristics in this Domain

Hospital operations data originates from various sources. These include hospital management policies, medical service process specifications, cost accounting details, performance appraisal standards, department work manuals, equipment procurement and maintenance records, and medical record management regulations. Documents are typically in PDF, Word, or Excel formats. Some data may reside in internal business systems. Document update frequencies vary. Management policies and process specifications might update quarterly or annually, while equipment maintenance records could update in real-time or daily. Document structure varies significantly. Some documents strictly follow templates, while others, like meeting minutes or analysis reports, contain extensive free-form text. Fields and units are common. Examples include "bed turnover rate" (times/month), "average length of stay" (days), "consumable costs" (Yuan), "diagnosis and treatment project code" (unitless), and "staffing" (people). This domain involves numerous specialized terms and industry-specific metrics.

Constraints Imposed by Data Characteristics on Multi-turn Conversation and Prompts

The complexity and diversity of hospital operations documents impose specific requirements on multi-turn conversation and prompt design. First, documents contain specialized terminology and abbreviations. Prompts must guide the model to accurately understand context and avoid misinterpretations due to semantic ambiguity. For example, concepts like "DRG grouping" or "medical insurance payment reform" require clear definitions or pointers to relevant document sections. Second, data sources with varying update frequencies mean information timeliness must be considered in multi-turn conversations. Prompts should distinguish between the latest policies and historical data, guiding users to confirm the information's time range when necessary. Third, the mix of unstructured text and structured data requires prompt design to effectively guide the model in extracting and integrating key information from different formats. An example is extracting specific performance indicators from a free-form analysis report and comparing them with structured table data. Additionally, the presence of numerous numerical fields and units requires prompts to clearly define numerical ranges, unit conversions, or trend analysis when asking questions. Examples include querying "the growth rate of outpatient visits in the last year" or "the change in average length of stay for a specific department."

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`maxContext`	`8`	Accommodates multi-concept queries common in hospital operations documents, ensuring sufficient conversational context.
`Chunk size` (Segment Length)	`800–1200 characters` (characters)	Balances the integrity of long paragraphs in documents with model processing efficiency, preventing critical information truncation.
`Recall count` (Recall Count)	`Top 8–12 entries` (top 8–12)	Accounts for multiple relevant but not identical passages in document content, increasing recall to improve coverage.
`Similarity threshold` (Similarity Threshold)	`0.75`	Balances recall precision and recall rate, filtering document segments highly relevant to hospital operations queries.
`Rerank result count` (Reranked Return Count)	`Top 5 entries` (top 5)	Focuses on the most relevant few document segments, reducing noise for the model and improving answer quality.
`Model Temperature (temperature)`	`0.3–0.5`	Ensures the accuracy and stability of model responses, reducing hallucinations or divergent content in specialized domains.

Common Pitfalls

The model responds with "Unable to find relevant data" or "Please provide more specific query conditions." This indicates improper knowledge base association or incomplete indexing, preventing the model from effectively retrieving information in multi-turn conversations.
The model provides incorrect results for numerical calculations or time-series questions. This occurs when prompts fail to clearly guide the model for numerical extraction, unit conversion, or time range limitation.
API calls frequently report invalid token or authentication failed. This means the API_KEY is misconfigured or permissions are insufficient, preventing FastGPT from correctly calling backend model services.

Verification Steps

Conduct multi-turn conversation tests against different types of hospital operations documents (e.g., management policies, cost reports, performance reports). Check if the model accurately understands query intent and extracts key information from relevant documents.
Verify queries involving numbers, units, and time periods. Check if the model's answers are logically and numerically consistent with the original documents, and confirm correct unit conversions.
Simulate multi-turn follow-up questions of varying complexity. Evaluate whether the model maintains contextual coherence and adjusts its information retrieval strategy based on previous conversations.

Note: 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.