Multi-turn Dialogue and Prompt Engineering for Urban

What the Target Data Entails

Urban commercial bank investment research data originates from five main sources: the institution’s own credit ledgers, regional economic statistical bulletins, public annual reports of listed urban commercial banks, peer credit reports, and regulatory notifications from local banking and insurance regulatory bureaus.

Credit ledgers are structured data, with fields including customer identification, credit limit, maturity date, risk classification, and others.

Regional economic bulletins and annual reports typically combine paragraph text with statistical tables, covering metrics such as regional enterprise counts and industry proportion shares.

Peer reports include multi-chapter data on business scale and risk exposure.

Update cycles vary across data types:

Credit ledgers update on a T+1 basis
Public annual reports release annually or semi-annually
Regional economic bulletins update quarterly
Regulatory notifications are sent irregularly

Field units use standard business units such as ten thousand yuan, hundred million yuan, and enterprise count.

Constraints for Multi-turn Dialogue and Prompt Engineering

Structured fields from credit ledgers must be retained across conversation turns.

Multi-turn dialogue systems must distinguish storage logic between structured fields and unstructured text to avoid context confusion caused by mixed storage.

Data with different update frequencies requires matching retrieval strategies.

T+1 updated credit data must be linked to the latest record in each conversation turn.

Annual report data can be cached and reused across turns.

The diversity of document structures requires prompts to adapt to retrieved results of different formats.

Structured data requires extraction of specified fields.

Unstructured text requires summarization of core viewpoints.

Fine-grained field units require prompts to clearly define unified unit rules, to avoid confusion around currency units.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`maxHistoryTurns`	`10`	Urban commercial bank investment research conversations mostly revolve around credit and regional policies. 10 turns covers complete business logic while avoiding context overload.
`similarityThreshold`	`0.72–0.78`	Investment research data contains numerous fine-grained structured fields. This range filters irrelevant matches while retaining valid business-related data.
`splitChunkSize`	`1200–1500 characters`	Regional economic bulletins and peer reports are mostly long paragraphs. This length preserves complete policy statements and business data units.
`reRankCount`	`Top 3 retrieved results`	Urban commercial bank investment research needs to balance regional policies and in-house credit data. Re-ranking prioritizes frequently associated business fields.
`enableStructuredMemory`	`Enabled`	Fields such as customer ID and credit limit from credit ledgers must be retained across conversation turns to avoid repeated questions and information loss.
`contextWindowSize`	`8000 characters`	Balances structured field retrieval and unstructured text context, adapting to the multi-source data mixed scenario of urban commercial bank investment research.

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. It is recommended to test on your own samples before finalizing settings.

Three Common Misconfigurations

Scenario: Setting maxHistoryTurns to 6 causes the model to fail to associate the credit limit field from the previous conversation turn. Cause: The enableStructuredMemory switch is not enabled. Only unstructured conversation text is retained, and context for credit fields is not stored synchronously.
Scenario: Garbled characters appear in credit fields extracted from conversations. Cause: No validation is performed on the encoding format of structured data. Imported ledger files have mixed GBK and UTF-8 encoding.
Scenario: No like-related fields are returned after calling the v4.8.10 version dialogue interface. Cause: The like function in this version requires additional front-end interaction configuration. The back-end interface does not integrate this feature by default.

How to Verify Successful Configuration

Launch a conversation with multiple rounds of investment research queries, verify that the model can associate the credit customer ID and credit limit mentioned in the previous turn.
Import a regional economic bulletin document, trigger a multi-turn dialogue, verify that retrieved results include policy statements from the corresponding chapter.
Adjust the similarityThreshold value, verify that the number of matching results meets business requirements.
Check backend logs, confirm that the number of stored historical conversation turns matches the set maxHistoryTurns.

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.