Agentic data science with the Data Agent Kit
cloud.google.com·6h ago
Data Science
Towards Data Science·Jul 22·5 min read
Loop Engineering for RAG Generation: iterate top-k one at a time
TL;DR
A new approach to Retrieval-Augmented Generation (RAG) is introduced, focusing on iterating through top-k candidates one at a time. This method enhances efficiency in document intelligence tasks by optimizing candidate selection.
✦ Why It Matters
Implement this top-k iteration strategy to enhance your document intelligence systems today.
Key Takeaways
- Implement sequential processing for factual queries to save tokens.
- Use batch processing for complex questions requiring multiple candidates.
- Leverage the dispatcher to determine the best processing method.
- Monitor token usage to optimize costs in enterprise applications.
How It Works
The sequential approach treats top-K candidates as an ordered list, querying the language model with each candidate until a sufficient answer is found. This method utilizes a typed contract that includes two key signals: answer_found and complete_answer_found, allowing the model to determine when to stop processing further candidates.