Implementation Logic and Scenario Adaptation for Dynamic Prompt Construction
LLManager's dynamic prompt technology is implemented based on the LangChain framework and comprises the following core technical components:
1. Architectural design
- Context Extractor: Retrieve relevant historical cases through RAG technology
- Rule EngineAnalyzing Approval Criteria in config.json
- Variable Injector: Replace specific parameters in real time
2. Workflow
- Receive original approval request
- Perform multi-path semantic search (compliance clauses/similar cases/reflection records)
- Assembly Base Tip Template
- Inject dynamic variables to form the final prompt
3. Scenario Adaptation Strategy
| Scene Type | Adaptation Method |
|---|---|
| Budget Approval | Enhanced Digital Verification Template |
| Contract Review | Enable Terms Comparison Module |
| Permission Request | Load RBAC rule set |
Typical Case: When processing cross-border procurement approvals, the system automatically: 1) Detects involved countries 2) Loads local tax law modules 3) Adjusts currency conversion logic 4) Injects compliance checkpoints The entire process completes within 200 milliseconds.
This answer comes from the articleLLManager: a management tool that combines intelligent automated process approvals with human reviewsThe
May not be reproduced without permission:AI productivity tools " How is the dynamic prompt building technology implemented in LLManager? How does it adapt to different approval scenarios?
