What data for this category looks like
Coking coal-related data comes primarily from domestic coal trading center public ledgers, port loading and unloading records, downstream industrial enterprise purchase filing information, and industry association monitoring reports. Update rhythms vary across data types. Port inventory and real-time transaction prices update hourly. Monthly production capacity and industry supply and demand data update monthly.
Each single data entry includes fixed structured fields. These cover origin identifiers, dry ash content indexes, caking indexes, total moisture content, transaction unit prices, total inventory, transportation distances, and more. Transaction unit price uses yuan per ton as its unit. Total inventory uses tons as its unit. Transportation distance uses kilometers as its unit. Some fields use enumerated classification formats.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
For coking coal marketing content targeting financial institutions, the multi-source update rhythm of data requires multi-turn dialogue to distinguish calling logic for real-time and non-real-time indicators. Real-time transaction price dialogues must call the latest interface each time to retrieve data. Monthly supply and demand data can use caching to reduce repeated requests.
Fixed structured fields and enumerated origins require prompt engineering to clearly define valid value ranges for fields. This prevents generating origin information outside the enumerated set. The clear unit system requires prompt engineering to enforce binding of output units, to avoid unit confusion. The multi-field attribute set also requires multi-turn dialogue to gradually guide users to clarify required indicator dimensions. This prevents generating vague marketing content.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | Previous 8–12 entries | Coking coal marketing dialogues need to retain historical records of user requirements and indicator preferences. This range balances context completeness and response efficiency, and adapts to the context storage logic of FastGPT 4.10.0 |
systemPrompt | When generating coking coal marketing content for financial institutions, strictly use the given structured data fields, attach correct units to outputs, prioritize enumerated origin values, and do not fabricate indicators | Coking coal data has fixed fields and enumerated classifications. This constrains generated content to comply with data specifications and financial scenario requirements |
relevanceThreshold | 0.78–0.82 | There are many coking coal subdivision indicators. This threshold filters low-relevance historical dialogue fragments to avoid interfering with current generation logic |
rerankTopN | Previous 4–6 entries | There are approximately 5 core indicators for coking coal data. Retaining core data after reranking ensures marketing content focuses on key information |
loopMaxTimes | 2–4 times | Iterative adjustments for coking coal marketing content do not require excessive cycles. This range balances content optimization and generation efficiency |
apiSessionBind | Bind a unique session ID | API channel calls need to retain complete dialogue context to ensure demand continuity for multi-turn dialogues |
The parameter values provided on this page are all conventional recommendations used as starting points for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Phenomenon: Setting
maxContextto 30 entries, but dialogue details only display 2 context entries, and replies cannot associate historical requirements. Cause: Conversation context persistence configuration is not enabled, or the context cache threshold is incorrectly lowered. - Phenomenon: Prompt engineering contains coking coal field names with spaces (such as "dry ash content"), and generated content has field name splitting errors. Cause: The prompt does not clearly require retaining field spaces, or the space recognition switch for prompts is not enabled.
- Phenomenon: After configuring a cyclic workflow, only 1 execution terminates, and multi-turn generation and judgment are not completed as expected. Cause: The cyclic judgment logic is not correctly bound to the dialogue output result, or the judgment condition does not match the expected text classification result.
How to confirm successful configuration
- Launch a test dialogue, enter indicator requirements related to coking coal, and verify that the number of context entries displayed in the dialogue details matches the set
maxContext. - Write a test prompt that includes coking coal field names with spaces, launch a dialogue, and verify that generated content retains the field spaces.
- Configure a cyclic workflow, enter non-compliant test text, and verify that the cycle executes the specified number of times as expected.
- Use API calls to bind a session ID, launch two rounds of dialogue, and verify that the second round of reply associates the requirement information from the first round.
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.