Jun 6 • 🗣️General Discussion
CAG or RAG AI Strategy?
CAG (Cache-Augmented Generation) and RAG (Retrieval-Augmented Generation) are two methods for augmenting large language models (LLMs) with external knowledge to improve the accuracy and relevance of their responses. CAG preloads relevant data into the model's context, while RAG dynamically retrieves external knowledge at runtime.
Context:
I am building a Tour Package Recommendation Agent, wherein the AI Agent needs to search through the database (travel agent owned database of tours, activities, events, tickets, transfers etc) and help bundle and recomend the most suitable package as per the customer's request.
System Capabilities:
- Store large volume of Tours and Activities related content
- Accurately match contents with destination and the type of activity (kids friendly, adventure etc..)
- Build itineraries based on activities selected (smart routing and scheduling)
What AI strategy would you choose?
RAG
CAG
Both
None
Other
1
3 comments
CAG or RAG AI Strategy?
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