Low-Hanging Fruit for RAG Search - jxnl.co

Sounds fancy. Why do we care? GAR involves taking the source documents and having an LLM enrich them, prior to indexing. For example, the LLM might... * Generate titles for documents that are missing them * Standardize author names/formats* Extract dates, URLs, citations and other elements that might be valuable to search as separate fields* Create... See more

The explosion of generative AI made us pause and consider what was possible now that wasn’t a year ago. We tried many ideas which didn’t really click, eventually discovering the power of turning every feed and job posting into a springboard to:

• Get information faster , e.g. takeaways from a post or learn about the latest from a company.


• Connect the

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I think the biggest mistake around improving the system is that most people are spending too much time on the actual synthesis without actually understanding whether or not the data is being retrieved correctly. To avoid this:

• Create synthetic questions for each text chunk in your database


• Use these questions to test your retrieval system


• Calculate p

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