Improving RAG: Strategies

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

1. Synthetic Data for Baseline Metrics¶

Synthetic data can be used to establish baseline precision and recall metrics for your reverse search. The simplest kind of synthetic data is to take existing text chunks, generate synthetic questions, and verify that when we query our synthetic questions, the sourced text chunk is retrieved correctly.

Benefi... 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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RAG, which stands for "Retrieval Augmented Generation," is a strategy in artificial intelligence where a large language model (LLM) retrieves relevant information from an external knowledge base (like a database or document collection) before generating a response to a user query, ensuring the response is more accurate and contextually relevant by ... See more