What the data for this category looks like
Data sources for electric power marketing content include internal marketing management systems of electric power enterprises, customer service work orders, provincial electricity price policy announcements, and offline service records. For update rhythm, price policy documents are updated at policy release or quarterly. Work orders and customer behavior data are updated via daily summaries. Document structure contains structured fields such as customer electricity usage address, electricity capacity, and applied business type, plus unstructured consultation dialogues and policy interpretation texts. Unique fields include electricity capacity (unit: kilovolt-ampere), price tier (unit: yuan per kilowatt-hour), and service response time limit (unit: hours).
What Constraints These Characteristics Impose on Multi-turn Dialogue and Prompt Engineering
The mixed structured and unstructured nature of electric power marketing data requires that multi-turn dialogue context retrieval must match both business fields and text semantics. This prevents returning incorrect values such as electricity capacity and price tiers. The regularly updated attribute of policy data requires adding effective time verification logic in prompts, to avoid calling expired electricity price policies. Real-time customer work order data requires that the multi-turn dialogue context window supports filtering by conversation time, only recalling valid history within the current consultation cycle. This avoids interference from irrelevant cross-session information. Additionally, electric power marketing content involves compliance requirements, so prompts must explicitly restrict the model to only use officially authorized content within the knowledge base, and prohibit generating unfiled marketing copy.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 tokens | Electric power marketing dialogues often include electricity usage parameters and policy texts, requiring adaptation to long context needs to avoid truncating critical business information |
recall_top_k | Top 6 entries | Balances retrieval accuracy and context length, matches high-frequency consultation scenarios in electric power marketing, and avoids interference from excessive redundant information |
similarity_threshold | 0.75–0.85 | Electric business fields such as electricity capacity require strict matching. A threshold that is too low may return irrelevant policies, while a threshold that is too high may miss valid content |
context_time_window | Last 7 days | Filters expired historical work orders and policies, adapts to the update rhythm of electric power marketing data, and avoids calling outdated electricity price information |
llm_prompt_template | Calibrated based on actual testing | Must enforce the model to only use knowledge base content, and explicitly require business fields to match corresponding units, for example: "Only reply with official electricity prices from the knowledge base, and the unit for electricity capacity must be kilovolt-ampere" |
speech_file_max_size | 50 MB | Adapts to voice consultation files in electric power marketing scenarios, avoiding parsing timeouts caused by overly large files |
The parameter values provided on this page are common recommended starting points for configuration setup. Actual values are affected by material form, data volume and business rules. Specific issues require targeted analysis, and testing should be performed on internal samples before finalizing configuration.
Three Common Misconfigurations
- Phenomenon: Responses from custom models and simplified applications differ, with no clear error prompt. Cause: The
llm_prompt_templateandcontext_time_windowparameters are not unified, and rule settings across different workspaces do not match. - Phenomenon: The LLM generates electricity price or service content not present in the knowledge base, and prompt restrictions are ineffective. Cause: The prompt does not explicitly require only calling authorized knowledge base content, and does not verify the official source of business fields.
- Phenomenon: Voice consultation files fail to parse after upload, or multi-turn dialogue only retains single-session content. Cause: The
speech_recognition_enabledconfiguration is not enabled, orrecall_top_kis incorrectly set to an overly small value, failing to retain valid cross-turn context.
How to Verify Proper Configuration
- Access multiple associated workspaces, input the same electric power marketing consultation question, and check whether the model's response content is consistent.
- Input a business question not present in the knowledge base, and check whether the model only prompts that valid information cannot be obtained, and does not generate unauthorized content.
- Upload a voice file that meets the format requirements, and check whether the interface displays successful parsing and generates corresponding text.
- Input a consultation question that includes historical business fields, and check whether the model can associate valid cross-turn context information.
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.