RAG challenges

Software Engineer (L2) | AI Developer | Building products leveraging human-centric AI Software Engineer (L2) | AI Developer | Building products leveraging human-centric AI

Subham Kundu 2y Edited Report this post Practical challenges I encountered while developing Retrieval Augmented Generation systems (RAGs) for various clients, particularly in the context of the hot topics within the generative AI ecosystem, where blogs and companies continually explore RAG integration. In simple terms, RAG is used to query and retrieve answers from a company's internal data. To simplify, documents are first transformed into embeddings and stored in a vector database. When a user poses a question, the top documents and the query are sent to the Language Model (LLM), and the use

related reading

• Better RAG 3: The text is your friendolickel.com
• Towards Data Sciencetowardsdatascience.com
• Code a simple RAG from scratchhuggingface.co
• What is Retrieval Augmented Generation (RAG)? | Databricksdatabricks.com
• RAG Architecture Deep Divelinkedin.com
• Building Enterprise AI: Hard-Won Lessons from 1200+ Hours of RAG Developmentbytevagabond.com
• Advanced RAG Techniques: What They Are & How to Use Themfalkordb.com
• Retrieval-Augmented Generation for Large Language Models: A Surveyarxiv.org
• Advanced Retriever Techniques to Improve Your RAGs | Towards Data Sciencetowardsdatascience.com
• Vinija's Notes • NLP • Retrieval Augmented Generationvinija.ai
• Aman's AI Journal • Primers • Retrieval Augmented Generationaman.ai
• Beyond embeddings: Navigating the shift to completions-only RAGsensible.so