Reference only; verify against the target FastGPT release and deployment documentation.
What the data for this category looks like
The base data for party building learning quiz generation comes from official public party building learning standard texts, intra-party policy documents, Party history learning materials, and designated reading materials for thematic education. The update rhythm adjusts with the latest intra-party learning requirements, with no fixed cycle. Each update covers knowledge point entries for the corresponding theme. Documents are categorized by theme into structured text blocks, with each entry containing four core fields: theme category, original knowledge point text, quiz question type requirements, and standard answer basis. The data unit is entries, where each entry corresponds to a basic knowledge point unit that can generate quiz questions.
Constraints Imposed on Multi-turn Dialogue and Prompt Engineering
Official authoritative data sources require prompts to strictly align with official expressions, to avoid content inconsistent with intra-party policies. No fixed update cycle requires multi-turn dialogue to dynamically load the latest knowledge point data, ensuring generated quiz questions meet current learning requirements. The theme-classified document structure requires multi-turn dialogue to associate the theme context of historical conversations, to avoid quiz generation deviating from the current learning theme. Fields containing question type requirements require prompts to accurately extract field information, generate quiz questions that meet specified question types, and handle continuation or adjustment needs for question types during multi-turn dialogue.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Maximum Context | 8000–12000 characters | Party building learning materials have long text, need to cover context memory for multi-turn dialogue and avoid knowledge point breaks |
| Question Optimization | Enabled | Quiz generation requires precise matching of knowledge points; optimization reduces off-topic question generation |
messages Parameter Configuration | Keep the first 20 conversation messages | Party building quiz generation needs to associate conversation theme history; excessive turns add unnecessary computational load |
variables Parameter Configuration | Pass 1–3 party building theme variables | Accurately match corresponding knowledge points to generate theme-aligned quiz questions |
chatId Parameter Configuration | Bind unique identifier per user session | Enable context memory for multi-turn dialogue, ensure consistent quiz generation for the same user |
Three Common Mistakes
- Phenomenon: Cannot directly output quiz questions when calling the simple application interface, results can only be generated through dialogue interaction. Cause: Failed to correctly configure the output mode of the dialogue interface, confused the calling logic of single generation and multi-turn dialogue.
- Phenomenon: Questions generated when referencing the party building knowledge base use non-Chinese expressions, which do not fit the usage scenario. Cause: Did not explicitly specify Chinese language requirements in the prompt, or failed to correctly bind the Chinese data source of the knowledge base.
- Phenomenon: Context is lost after multi-turn dialogue, unable to continue the previous quiz generation theme. Cause: Did not bind the unique
chatIdparameter for each session, causing session context to not be saved correctly.
How to Confirm Configuration is Correct
- Initiate continuous dialogue, sequentially submit quiz generation requests for different party building themes, verify that generated questions match the current conversation theme.
- After passing the
variablesparameter, verify that generated questions cover the specified party building knowledge points. - Maintain multiple rounds of conversation, verify that generated questions continue the previous conversation context without theme shifting.
- Check the session data returned by the interface, confirm that the
chatIdparameter is correctly bound and the context has not been reset.
The values provided here are common starting points, and should be evaluated against the user'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.