Data Profile for This Category
Usually, data for this category comes primarily from historical traffic data, service topology, monitoring metrics, and business growth plans. Daily deliverable documents include technical solutions, architecture diagrams, capacity assessment reports, and technical review minutes. Technical solutions note constraints, cross-service boundary rules, and long-term evolution directions. Capacity assessments cover resource usage-related metrics. Data is updated once monthly or when adjusted for major requirements. Document fields focus on technical decisions and resource planning, with no unified standardized numerical units. Matching must be done based on specific business scenarios. Unlike backend service development, design decisions for this category span service boundaries and long-term evolution roadmaps.
Constraints for Multi-turn Dialogue and Prompt Engineering
Data for this category is scattered and formatted diversely. Multi-turn dialogue must support integration of information across data sources. Prompt engineering must explicitly specify association with different types of input data such as historical traffic data and service topology. Since data update frequency is not fixed, dialogue context must support dynamic loading of the latest technical review minutes or business growth plan content. Daily document structures vary widely. Prompt engineering must adapt to inputs in different formats such as architecture diagram descriptions and capacity assessment metrics. Additionally, the most time-consuming constraint collection and solution demonstration links for this category require multi-turn dialogue to gradually guide the sorting of technical constraints and avoid missing key information.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Context Window Size | 10000–15000 characters | Matches the typical content volume of architecture design and technical solution documents, prevents truncation of critical technical details, and aligns with the context storage limit of FastGPT v4.17.0 |
| Session Context Refresh Strategy | Triggered by major requirements | Matches the update frequency of data for this category, ensures dialogue always uses the latest business growth plans or review minutes |
| Historical Dialogue Retention Turns | Last 8–12 turns | Filters out early non-core discussions, focuses on effective communication content during the current requirements drafting phase, and reduces model inference load |
| Variable Passing Configuration | Bind business identifiers and project IDs | Used to associate historical traffic data and service topology information for the corresponding project, enabling precise calling of dedicated data sources |
| Prompt Template Update Timing | Triggered upon closure of review comments | Aligns with the most time-consuming review comment closure link for this category, ensures prompts sync with the latest review consensus |
Three Common Misconfigurations
- Symptom: Dialogue history cannot be queried in MongoDB. Cause: The binding rules for the
chatIdparameter are not configured correctly, resulting in dialogue data not being stored by project dimension, making it impossible to retrieve via project identifiers. - Symptom: After local deployment, data processing is empty in the second step of knowledge base training. Cause: Data source paths are not correctly associated, resulting in uploaded documents such as architecture diagrams and traffic data not being properly parsed and stored, and the subsequent data processing workflow not being triggered.
- Symptom: Inconsistencies appear between solution content and historical review comments in multi-turn dialogue. Cause: The business identifier parameter passed via
variablesis not updated correctly, resulting in dialogue calling expired technical review minute content without syncing the latest review consensus.
How to Verify Correct Configuration
- Check the content scope of the session context, confirm that core documents such as the current project’s technical solutions and capacity assessments are included. This can be verified by viewing the context preview in the dialogue input.
- Validate the configuration of
variablespassing parameters, confirm that each dialogue carries the business identifier and data source association information for the corresponding project. This can be verified by viewing the parameter content in the request body via an interface debugging tool. - Cross-check the historical dialogue retention turns, confirm that only recent discussions related to the current requirements drafting phase are retained. This can be verified by viewing the number of
messageshistorical records in the session details. - Check the prompt template update trigger logic, confirm that updates are triggered upon closure of review comments. This can be verified by simulating a review comment submission action to check if the template is adjusted synchronously.
The values provided are common starting points and should be measured against samples specific to the relevant deployment scenario.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-10-19.