Multi-turn Conversation and Prompt Engineering for Financial Report Analysis on Investment Platforms

Financial report data for investment platforms primarily comes from public disclosure documents of domestic and overseas stock exchanges, official

What the Data for This Category Looks Like

Financial report data for investment platforms primarily comes from public disclosure documents of domestic and overseas stock exchanges, official announcements of listed companies, and compliant financial report databases. The update rhythm follows financial reporting cycles: quarterly reports are updated 1 to 2 months after the end of the quarter, annual reports are updated concentratedly during the annual report disclosure season, and temporary announcements are synced immediately with major events. Each single financial report document includes two parts: structured financial statements and note disclosures. Structured fields cover revenue, attributable net profit, operating cash flow, and others, with units mostly in yuan, ten thousand yuan, or hundred million yuan. Notes include written content such as accounting policy adjustments and non-recurring profit and loss explanations.

The large volume and multi-dimensional structure of financial report data require that multi-turn conversations limit the number of document fragments recalled per round, to avoid exceeding the model's context limit. For scenarios comparing financial reports of multiple companies, prompts must clearly specify comparison dimensions and corresponding reporting cycles to prevent field confusion. Unit differences between financial report fields must be agreed in advance in prompts to avoid calculation errors. The real-time update feature of temporary announcements requires that the conversation flow can trigger data source refreshes to ensure returned financial report data is the latest version. The mixed format of structured statements and note text requires distinguishing parsing rules for the two types of content in prompts to ensure accurate data extraction.

How to Configure

Configuration ItemRecommended ValueRationale
maxContext8000–16000 charactersThe core analysis fragment of a single financial report is approximately 2000 characters. Multi-turn conversations need to retain 3-4 rounds of history to avoid exceeding the model's context limit
recall_top_kTop 3–5 entriesFinancial report data fields are concentrated. Excessive recall will introduce irrelevant content and affect analysis accuracy
PROMPT_TEMPLATEStructured prompt that explicitly specifies reporting cycles, units, and comparison rulesFinancial report data must strictly match fields and units. Clear comparison dimensions can avoid confusion of cross-company data
UPLOAD_FILE_MAX_SIZE50–100 MBSingle annual report PDF or Excel files typically do not exceed 50 MB. Reserve expansion space to support batch uploads
PARSE_FILE_TIMEOUT_SECONDS600 secondsStructured parsing and field extraction for large financial report documents require longer processing time
markdown_image_base_urlhttps://your-platform-domain.com/static/Ensure imported markdown-format images load correctly, matching platform domain rules

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 targeted analysis, and it is recommended to test on your own samples before finalizing.

Three Common Errors

  • Phenomenon: Request logs and parameters from the tool call step are directly displayed in conversation results. Cause: The output switch for tool call results is not disabled, and rules for returning only AI analysis content are not configured.
  • Phenomenon: Image links in imported markdown-format financial report attachments fail to load normally, showing invalid addresses. Cause: The base domain parameter for markdown images is not configured. The platform domain is only automatically bound during Word import, and domain information is lost after conversion to markdown.
  • Phenomenon: When a global API key is used to initiate cross-application conversations, returned results are associated with session data from non-target applications. Cause: Independent session isolation parameters are not configured for different applications, and the global key does not limit the application context bound to the conversation.

How to Verify Successful Configuration

  • A single quarterly financial report document is uploaded, a query including field extraction and unit verification is initiated, and the field units in the returned results are verified to match those marked in the document.
  • A query comparing the financial reports of two listed companies is initiated, and the returned results are checked to only include financial data for the specified two companies, with no irrelevant entity information included.
  • After tool call configuration is complete, a query requiring use of the financial report parsing tool is initiated, and the conversation results are verified to only display analysis conclusions, with no original tool call requests or parameters shown.
  • A Word-format financial report document including images is imported, converted to markdown format, and the domain of the image link is checked to match the platform configuration.

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.