What the data for this category looks like
The data used for film theater financial report analysis mainly comes from quarterly and annual public financial reports issued by theater companies, as well as daily box office and theater operation data from third-party film data platforms. Data update rhythms are split into scheduled and real-time: financial reports are updated on a fixed quarterly and annual schedule, while box office and operation data are updated daily. The document structure includes revenue modules (box office revenue, advertising sponsorship, derivative sales), cost modules (venue rental, labor costs, copyright procurement), and individual theater operation metrics such as average per-session audience count, box office per screen, and number of screenings. Units are person-times, ten thousand yuan, and sessions respectively.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
The multi-source update rhythms and segmented field characteristics of film theater data create multiple constraints for multi-turn dialogue and prompt engineering workflows. Scenarios for real-time box office queries and scheduled financial report analysis must be clearly distinguished. Prompts must define clear parameter type boundaries to prevent confusion between real-time and historical data. Multi-turn dialogue must gradually prompt users to supplement key parameters, including specified theater scope, data time cycle, and statistical dimension. Predefined mapping rules for professional fields must be implemented to ensure that extracted fields such as average per-session audience count and box office per screen align with industry standard definitions, and avoid parameter extraction errors.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–16000 characters | Film theater financial reports and box office data contain multiple detailed segments. A longer context can retain complete historical dialogue and data fragments, and avoid truncation of key information |
systemPromptTemplate | "Act as a professional film theater financial report analyst. Extract three core parameters from user questions: theater scope, time cycle, and data type. If any parameter is missing, ask the user to supplement it one by one. Only answer based on public film theater data" | Clarify roles and parameter extraction rules to meet the needs of industry-specific scenarios |
speechToTextModel | Use the platform's built-in general speech-to-text model | Voice input in film theater scenarios mostly consists of colloquial questions. General models can accurately identify industry terminology and parameter keywords |
ragRecallTopK | Top 6 entries | Film theater data has multiple associated dimensions. Retrieving an appropriate number of data sources can cover multiple types of information including box office, financial reports, and operations, and avoid information overload or missing content |
promptVariableExtractSwitch | Enabled | Automatically extract variables such as time and theater names from user questions, reduce manual input steps, and adapt to the parameter supplement logic of multi-turn dialogue |
fileParseChunkSize | 1000–1500 characters | Film financial report documents have long paragraphs. An appropriate segment length can retain field relevance, and improve the accuracy of subsequent prompt parsing |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Phenomenon: Parameters such as theater names and time cycles extracted during multi-turn dialogue are empty or inconsistent with the actual questions. Cause: The exclusive parameter rules for film theater scenarios are not clearly defined in the prompt. General parameter extraction logic cannot adapt to industry-specific fields, leading to missing or incorrect key information.
- Phenomenon: No text conversion result is generated after voice input, or the recognized content deviates from the actual question. Cause: A speech-to-text model adapted to the scenario is not configured, or the corresponding function switch is not enabled. General models have insufficient recognition accuracy for film industry-specific terminology.
- Phenomenon: Dialogue processing times out or returns an abnormal number of results. Cause: Reasonable
maxContextandragRecallTopKparameters are not set. Excessively long context or too many retrieved data entries cause processing timeouts, or excessively narrow parameter ranges miss key business information.
How to Confirm Proper Configuration
- Initiate a test question that includes a theater name and time cycle, verify that the system can accurately extract core parameters, and automatically ask for supplements if any parameters are missing.
- Upload a film theater financial report document, test the document segmentation and parsing effect, and confirm that core fields are not incorrectly split or missed.
- Enable the voice input function, use a colloquial question containing film industry terminology to test the transcription result, and confirm that the recognized content matches the actual question.
- Adjust the
ragRecallTopKparameter, test result completeness under different values, and confirm that the retrieval scope adapts to current business needs.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.