Multi-turn Dialogue and Prompt Engineering for Aquaculture

What the Data for This Category Looks Like

Data sources for aquaculture due diligence include IoT monitoring devices at aquaculture ponds, farmers’ daily logs, fishery administration inspection reports, and feed supplier delivery records. Data update frequencies fall into three categories:

Real-time metrics such as water dissolved oxygen and pH update hourly
Log data such as feeding amounts and disease records update daily
Fishery administration inspection reports update weekly

The document structure uses pond as the unique identifier, and includes fields such as aquaculture species, breeding cycle, pond area, water quality metrics (dissolved oxygen, ammonia nitrogen, unit mg/L; pH has no unit), daily feeding amount (kg/mu), and disease occurrence records.

What Constraints These Characteristics Impose on Multi-turn Dialogue and Prompt Engineering

Since data sources are scattered and update frequencies vary, multi-turn dialogue must first confirm the target pond and aquaculture species for the current query. This prevents cross-pond data calls.

Thresholds for water quality metrics differ significantly across aquaculture species. Prompts must first obtain species information before matching corresponding threshold rules.

Real-time metrics and historical logs require distinct call priorities. In multi-turn dialogue, latest real-time data must be retrieved first, followed by historical log content. This ensures the timeliness of due diligence data.

Fields include specific units. Prompts must explicitly require all indicators to include standard units. This prevents ambiguous results without units.

Configuration Settings

Configuration Item	Recommended Approach	Rationale
`maxContext`	First 1200–1500 characters	Aquaculture data has many fields. Sufficient historical dialogue and metric context must be retained to avoid truncation of critical breeding parameters.
`ragRecallNum`	Top 8 entries	Must cover multiple data categories including water quality, feeding, and disease. Too many entries increases token consumption. Too few fails to cover all due diligence dimensions.
`similarityThreshold`	0.75–0.85	Aquaculture metric thresholds are strict. Low-match non-aquaculture data must be filtered to avoid incorrect calls of monitoring data from non-target ponds.
`responseFormat`	Follow specified JSON structure	Due diligence reports require structured output. This facilitates subsequent automated integration and report generation.
`apiTimeout`	600 seconds	Multiple scattered aquaculture data sources must be retrieved. This avoids dialogue interruptions caused by data pull timeouts.
`system_prompt_template`	Split prompts by pond species and metric dimensions	Adapts to threshold differences across aquaculture species. Explicitly prioritizes calling real-time IoT data before supplementing logs.

The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.

Three Common Mistakes

Phenomenon: After calling the API to start an intelligent due diligence dialogue, the interface prompts "session initialization failed". Cause: The exclusive data permission for the target aquaculture pond was not bound in the dialogue configuration, resulting in failure to pull the corresponding data source.
Phenomenon: After completing one dialogue, a duplicate user input entry appears in the history record. Cause: The automatic append historical context switch was not turned off, or the initial user question text was repeatedly passed when calling the API.
Phenomenon: The generated due diligence report reply does not include standard units for water quality indicators. Cause: The prompt did not explicitly require all aquaculture indicators to be marked with corresponding units, or the configured responseFormat did not include unit field verification rules.

How to Confirm the Configuration Is Correct

Initiate a query about water quality for a specified pond, and verify that the returned results include the latest dissolved oxygen and pH values for that pond.
Call the API to initiate two consecutive dialogues, and check that there are no duplicate user question entries in the history record.
After configuring responseFormat as JSON, confirm that the generated reply can be properly parsed by standard JSON parsing tools.
Adjust the similarityThreshold parameter, test recall results across different thresholds, and confirm that its value matches the precision requirements for aquaculture metrics.

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.