Multi-turn Dialogue and Prompt Engineering for Integrated

What the data for this category looks like

Integrated services investment research data sources include public industry research reports, periodic announcements of listed companies, regulatory disclosure documents, and third-party industry databases. Update cadence varies by document type: announcements are updated in real time, research reports are updated per their release cycle, and database resources are synchronized in monthly batches. Document structure includes main body paragraphs, structured data tables, industry classification tags, publishing organization identifiers, and publishing time fields. Field units cover currency units, multiple units, and other types, with no unified fixed unit.

What constraints these characteristics impose on multi-turn dialogue and prompt engineering

Wide variation exists in document structures across different sources, including long text paragraphs and structured tables. Multi-turn dialogue must support contextual association parsing across different document types. Prompt engineering must clearly distinguish the calling logic between structured data and unstructured text. Real-time updated announcement data and periodically updated research report data coexist. Context recall for multi-turn dialogue must limit the time range to avoid introducing outdated information. There are no unified standards for fields and units. Prompt engineering must additionally specify field matching rules, and multi-turn dialogue must verify unit consistency within the context.

How to set the configurations

Configuration Item	Recommended Value	Rationale
`maxContext`	`First 12000 characters`	Integrated services investment research data includes long-text research reports and structured tables. 12000 characters can cover the effective context of a single core research report and avoid truncating key information
`history_window_size`	`First 6 dialogue turns`	Investment research conversations need to associate earlier industry tags and data calibers. A 6-turn window can cover the context of a complete research cycle while controlling token consumption
`system_prompt_template`	`Follow the format of "Document Type + Field Rules"`	There are no unified standards for investment research data fields and units. The template must clearly specify the calling priority of structured data and unit verification logic
`recall_top_k`	`Top 8 recall results`	Integrated services investment research data sources are diverse. 8 results can cover research reports and announcements from different dimensions and avoid missing key information
`token_limit_per_call`	`8000 tokens`	Investment research conversations need to process context from multiple documents. 8000 tokens balances response speed and information completeness

The parameter values provided on this page are all conventional recommendations used to determine the starting point for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing.

Three common misconfigurations

Phenomenon: Historical conversation context cannot be directly called during multi-turn dialogue. Cause: The history_window_enable configuration item is not enabled, or the number of window turns is set to 0.
Phenomenon: Redundant AI conversation text remains in the tool call flow. Cause: The system_prompt_template does not explicitly require hiding redundant conversation content after tool calls, or an incorrect output format switch is configured.
Phenomenon: The Markdown format in the prompt engineering is not rendered and is output directly as plain text. Cause: The markdown_render_enable configuration item is not enabled, or the prompt template does not specify the output format as Markdown rendering mode.

How to verify successful configuration

Initiate a test conversation that includes historical context, verify that the system can associate previously mentioned industry tags and data content, and check the accuracy of context recall.
Write a prompt that includes Markdown format, initiate a test request, verify that the output content is in rendered format rather than plain text, and check the configuration status of the rendering switch.
View the token statistics field in the conversation log, verify that the input and output token values are displayed normally, and confirm that the token statistics configuration is enabled.
Initiate a tool call test, verify that no redundant AI conversation text remains after the call is completed, and check the format requirement configuration of the system prompt.

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.