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cloud.google.com·7h ago
Data Science
Towards Data Science·Jul 24·5 min read
Loop Engineering for RAG Generation: An LLM Cascade from a Cheap Local Model Up to a Hosted Flagship
TL;DR
A model selection strategy for retrieval-augmented generation (RAG) is proposed, emphasizing the importance of verifying outputs before escalating to larger models. This approach balances cost and performance by using local models effectively.
✦ Why It Matters
Implement a model verification process to ensure output accuracy before escalating to larger models.
Key Takeaways
- Start with the best local model based on defined criteria.
- Validate outputs before escalating to more expensive models.
- Incorporate task-specific vocabulary to improve model accuracy.
- Use a bounded loop to manage model escalations effectively.
How It Works
The LLM cascade operates by selecting a starting model based on criteria such as confidentiality and output reliability. It validates the output of the chosen model and only escalates to a stronger model if the validation fails.
This process allows for cost savings while ensuring that the output meets the required standards.