Systematically Improving Your RAG

Systematically Improving Your RAG - jxnl.co

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Here is everything you need for your RAG pipeline: 1. @llama_index parser 2. @firecrawl_dev for web pages 3. Process CSVs and Tables to a SQL table and use text-to-sql as a tool for your LLM 4. Use a multimodal model to describe... See more

Yasser x.com

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.

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Low-Hanging Fruit for RAG Search - jxnl.co

RAG (Retrieval-Augmented Generation) is the most common pattern for building chatbots that answer questions about specific documents.

The Problem: Models like ChatGPT don't know about your company's internal documents or events that happened after their 2023 training cut-off. Asking them will lead to hallucinations.
The Architecture:
1. Retrieve: When a