Multi-turn Conversation and Prompting for IT Service

What the data for this category looks like

Data for IT service research reports comes from public research reports on the financial sector released by professional IT consulting institutions, quarterly monitoring reports from financial industry associations, and official technical documents from leading financial IT service providers. Updates follow a quarterly regular rhythm, with special research reports added within 7-14 days after the release of core fintech iterations. Document structures uniformly include core summary, financial IT architecture breakdown, market competition landscape, implementation path suggestions, and appendix data list. Data fields include vertical segment revenue scale (unit: 100 million yuan), number of technology implementation cases, vendor technology iteration cycle, and monitoring sample coverage. There are no standardized fields with a unified fixed format.

What constraints these characteristics impose on multi-turn conversation and prompting

Research reports are generally long and contain multi-chapter segmented content. In multi-turn conversations, contextual association of historical interactions must be retained to avoid repeatedly recalling irrelevant chapters. Research reports are updated quarterly with temporary supplements, so prompts must explicitly specify prioritizing retrieval of the latest released report versions. Research reports contain quantified fields with clear units, so prompts must require attaching corresponding units when returning fields. Research report content focuses on financial IT implementation and market landscape. In multi-turn conversations, users can ask follow-up questions about specific vertical segments, and the system must retain semantic association of conversation context to avoid deviating from the current research report topic.

How to set configurations

Configuration Item	Recommended Value	Rationale
`maxContext`	`8000–12000 characters`	Adapts to the length of single IT service research reports (5000-10000 characters) and retains complete context for multi-turn interactions
`recall_topk`	`Top 6–10 results`	Research reports have multiple vertical segments, so sufficient content from relevant chapters must be retrieved to avoid missing key information
`similarity_threshold`	`0.75–0.85`	Research report content has high professionality, so lowly relevant non-target segment content must be filtered to improve retrieval accuracy
`prompt_template`	`Prepend the requirement "Prioritize using content from special research reports released in the last 3 months, and attach corresponding units when returning fields"`	Matches the update frequency of research reports and the unit requirement for quantified fields, ensuring returned content complies with business specifications
`conversation_history_max_length`	`Last 3–5 turns of conversation`	The reference value of early content in multi-turn interactions for current research report retrieval gradually decreases, controlling context redundancy
`file_parse_chunk_size`	`1000–1500 characters`	Balances semantic integrity and retrieval accuracy for long research report documents, avoiding breaking the coherence of professional terminology when segmenting

The parameter values provided on this page are conventional recommendations used as a 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

Symptom: Returns an empty string or 500 Internal Server Error after calling the conversation node, with no valid prompt displayed on the frontend. Cause: No fallback return logic is configured for the error_handling parameter, and no handling is implemented for empty recall or model exception scenarios.
Symptom: After calling the POST interface to delete conversation records, the records remain in the system. Cause: The request does not carry a valid Authorization authentication header, or the incoming conversation_id parameter does not match the target conversation.
Symptom: After enabling the guess_you_ask configuration, no associated questions are generated after the conversation ends. Cause: The guess_you_ask_auto_trigger switch is not enabled, or the configured prompt does not include a requirement to generate associated questions.

How to confirm the configuration is correct

Upload a single IT service research report, initiate two consecutive follow-up questions about different vertical segments, and verify that the system retains the context from the first round and recalls the corresponding chapters.
Simulate model errors or empty recall scenarios, and verify that the system returns preset fallback prompt text to avoid returning no content.
Configure a Feishu online document link as a data source, retrieve specific quantified fields in the document, and verify that the corresponding content can be recalled correctly.
Enable the guess_you_ask configuration, initiate a complete conversation, and verify that associated follow-up questions are generated after the conversation ends.

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.