What Data Looks Like for This Category
Documents and materials in the coal-electric power integration field mainly come from internal production ledgers, project approval documents, safety inspection reports, purchase and sales contracts, and unit operation reports. Data update rhythms fall into two categories: production operation data is updated per shift or daily, while project and contract data is updated as needed. Most document structures include fixed fields: project number, coal calorific value, power generation load, safety hazard level, and approval process node. Units cover professional metering standards such as ton, large calorie/kilogram, megawatt-hour, and shift. The length of a single document mostly ranges from 500 to 3000 characters.
What Constraints These Characteristics Impose on Multi-turn Dialogue and Prompt Engineering
Professional fields and unit requirements for coal-electric power integration documents require clear communication of field definitions during multi-turn dialogue, to avoid deviations in generated content caused by ambiguous terminology. Frequently updated production data requires retaining the latest real-time parameters in the conversation context, to prevent generated content from being disconnected from current business status. The long document structure requires limiting the total length of the context window, to prevent token overflow that interrupts the dialogue. Mixed data from coal and power business requires prompts to clearly define the scenario scope of documents, to avoid confusion between professional rules of the two types of business.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Maximum context length | 8000–12000 token | Coal-electric power integration documents contain long paragraphs and professional data, requiring sufficient context to avoid loss of key information |
variables binding | Bind preset variables such as daily coal consumption and unit load | Coal-electric business data is updated in real time, and passing data via variables ensures generated content matches the latest business status |
messages history truncation | Retain the last 10 rounds of dialogue | Long dialogues easily accumulate redundant information; truncation controls token consumption and avoids triggering timeouts |
| Single search limit | Top 6 results | Coal-electric documents need to match accurate historical ledgers and specifications; excessive recall will interfere with generation logic |
| Question optimization | Enabled | There are many professional terms; optimization clarifies the core needs of user questions and reduces generation deviations |
chatId identification | Bind unique session identifier | Ensures correct context association for multi-turn dialogue, avoiding information confusion across different sessions |
Three Common Mistakes
- Phenomenon: API calls return content that does not match the preset JSON structure, with an error message showing "invalid schema". Cause: The specified JSON Schema is not bound via the
variablesparameter, resulting in generated content that does not match the required format. - Phenomenon: Long periods of no response from the dialogue, with timeout records in backend logs. Cause: No reasonable maximum context length is set, and coal-electric ledger data accumulated in long dialogues exceeds the token limit, triggering system timeout.
- Phenomenon: Generated documents mix professional units for coal and power, such as marking the calorific value of coal as megawatt-hours. Cause: The prompt does not clearly define the usage rules for professional units, and no scenario-specific field verification logic is bound.
How to Verify Proper Configuration
- Submit a query containing coal-electric professional terms to the dialogue, and verify that the returned content includes data corresponding to the preset
variablesfields. - Initiate 10 consecutive dialogues, and confirm that the context is correctly truncated without any error prompts related to token overflow.
- Call the API for testing, and confirm that the returned
messagesparameter contains the latest conversation history with no redundant old session data. - Submit a query containing professional units, and verify that the generated content follows the correct metering rules.
The values provided here are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.