Practical Considerations in RAG Application Design

This is the second part of the RAG analysis:

• part 1: Disadvantages of RAG
• part 2: Practical Considerations in RAG Application Design

The RAG (Retrieval Augmented Generation) architecture has been proven to be efficient in overcoming the LLM input length limit and the knowledge cutoff problem. In today’s LLM technical stack, RAG is among the bedstones for grounding the application on local knowledge, mitigating hallucinations, and making LLM applications auditable. There are plenty of …

analysis application architecture design disadvantages generative ai tools generative-ai-use-cases knowledge llm machine learning machine learning & ai part photo practical rag retrieval retrieval augmented generation stack technical