What I found interesting was the fifth section that describes the strategies to improve RAG performance. Basically:
1. Quality control. What documents to include?
2. Timing. When to query?
3. Pre & post processing. Improve LLM outputs based on retrieved data.
4. End to end training. Expensive, and data intensive but possibly the best long-term approach.
5. Controller. An interesting idea with similarities to Reinforcement Learning.
I wonder what are your thoughts. Which one is most promising? What has been your experience when building RAG apps? Also, is RAG the leading architecture for building applications?
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[ 3.3 ms ] story [ 18.8 ms ] thread1. Quality control. What documents to include?
2. Timing. When to query?
3. Pre & post processing. Improve LLM outputs based on retrieved data.
4. End to end training. Expensive, and data intensive but possibly the best long-term approach.
5. Controller. An interesting idea with similarities to Reinforcement Learning.
I wonder what are your thoughts. Which one is most promising? What has been your experience when building RAG apps? Also, is RAG the leading architecture for building applications?