"I don't use RAG, I just retrieve documents"

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

if your job is legible enough that people can make a dataset clearly pointing out what is right and what is wrong, you are at the highest risk for an AI model being “superhuman” at your job. It is even more risky if it is possible to articulate your thought process in a way that is verifiable.

Looking at this perspective, it makes more sense that c... 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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