Multi-turn Dialogue and Prompt Engineering for Livestock and

What This Category’s Data Looks Like

Data sources for livestock and poultry farming profitability and market trend daily reports include national livestock industry monitoring platforms, daily ledgers of regional breeding cooperatives, and real-time quotation systems of live poultry/live livestock trading markets. There are two data update schedules: core daily report data updates full statistics for the previous day at midnight every day, while real-time purchase prices update every 2 hours.

Document structure is layered by breeding category, such as pigs, white feather chickens, beef cattle, etc. Each document includes fields like breeding cycle, initial inventory, new slaughtered quantity, feed cost, epidemic prevention cost, average purchase price, and profit per head. Field units include head, yuan/kg, yuan/head, and others. Some PDF documents include regional distribution charts.

Constraints on Multi-turn Dialogue and Prompt Engineering

Data is split into structured ledgers and unstructured reports. Multi-turn dialogue must distinguish between different data sources to avoid confusing real-time and historical data. Fields use multiple units, so prompts must clearly specify fields and their corresponding units. Failure to do so may lead to unit deviations in profit calculations.

Field structures vary by breeding category. Multi-turn dialogue must first confirm the breeding category specified by the user before retrieving the corresponding data entries. Differences in update frequencies require the dialogue flow to clarify the user’s query time range, to avoid returning outdated or mismatched data.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`maxContext`	`8000–12000 characters`	Livestock and poultry farming multi-turn dialogue needs to associate historical questions about categories, time ranges and other information. Sufficient context prevents information loss
`rag_relevance_threshold`	`0.72–0.78`	Breeding data has many fields with low similarity differentiation. This threshold filters invalid entries and retains core monitoring data
`rag_top_k`	`Top 6–8 entries`	Valid data entries in a single breeding daily report are concentrated in 5-7 entries. This value covers core information without increasing retrieval load
`file_parse_chunk_size`	`1000–1500 characters`	The standard paragraph length of breeding daily reports is about 1200 characters. This chunk size fully preserves the logical unit of a single report
`rag_timeout`	`60 seconds`	Retrieving cross-regional breeding data requires certain processing time. This duration balances retrieval completeness and dialogue response speed
`enable_history_reference`	`Enabled`	Multi-turn dialogue needs to reuse parameters such as breeding category and time range previously specified by the user. Enabling this allows association with historical context

The parameter values provided on this page are general 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 settings.

Three Common Misconfigurations

Phenomenon: Some questions in the dialogue trigger knowledge base retrieval, while others generate answers directly without retrieval logs. Cause: rag_trigger_mode is not configured to "forced retrieval". In the default mode, retrieval is only triggered when the question involves unpreset information.
Phenomenon: Global variables are cleared after the dialogue ends, making it impossible to reuse historically configured breeding category parameters. Cause: The persist_global_variables switch is not enabled. Temporary variables are cleared by default after a session ends.
Phenomenon: As the number of questions in the same dialogue window increases, knowledge base retrieval time exceeds 60 seconds, while retrieval time for new windows is normal. Cause: The context length limit of maxContext is not set. Excessive accumulated historical dialogue leads to too large context data volume loaded during retrieval.

How to Verify Proper Configuration

Enter a profitability query for a specified category in the dialogue window, check the retrieval log on the right side of the interface to confirm that matching knowledge base entries are recalled, and verify that the number of recalled entries matches the configured rag_top_k value.
Create a test dialogue, set and save global variables, end the session, then reopen the dialogue to check if the variables are retained, and confirm that the persist_global_variables switch is correctly configured.
Launch multiple consecutive queries related to different breeding categories, record the retrieval time for each, and compare it with the retrieval time for new windows to confirm that the configuration does not cause excessive delay.
Include a field query without specified units in the question, check if the answer includes clear unit annotations, and confirm that the field constraints in the prompt are effective.

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.