Unverified50% confidenceSolutionExact time
The project uses a three-stage architecture: the LLM parses intent and converts it into hard constraints, the McDonald's MCP fetches store data, the local script optimize_meal.py enumerates combinations and performs optimal coupon matching, and then the LLM translates the result back into natural language
1
Sources
50%
Confidence
Long-term
Relevance
10/12/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedThe product uses a multi-LLM routing architecture that distributes requests to the most suitable model based on user intent, with different models excelling at tasks such as storytelling, mathematics, and translation.69% similarUnverifiedLLM-based information extraction solutions can quickly adapt to different business scenarios through prompt engineering, requiring only modifications to field definitions and examples in the prompt, without retraining the model.67% similarUnverifiedThe LlamaIndex framework provides infrastructure for building orchestration systems with multi-model collaboration, conditional branching, and result validation.66% similarUnverifiedThird-party AI tools such as Doubao and Kimi can be used to generate system prompts; after clearly describing the requirements, a structurally complete prompt can be generated in about one minute.65% similarUnverifiedLLM 工具支持模板和系统提示词功能65% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1003270API
curl https://kongchang.com/api/v1/knowledge/claims/1003270MCP
get_claim(id=1003270)