RAG Explained: Pros and Cons

This blog contains a few advantages as well as disadvantages of RAG applications.

Disclaimer

This blog reflects my learnings from Augment your LLM Using Retrieval Augmented Generation by NVIDIA. If you are not familiar with RAG, I would suggest you to check out my following blog.

RAG Explained: Introduction, improving LLMs

Ďēv Šhãh 🥑 ・ Oct 1

#rag #langchain #llm #vectordatabase

Pros

1. Domain-Specific Knowledge

RAG empowers LLMs by enabling real-time access to domain-specific knowledge. This is particularly beneficial for industries like legal or medical fields, where precise, up-to-date information is essential. By retrieving relevant documents before response generation, RAG allows the LLM to address specialized queries more accurately.

2. Reduced Hallucinations

One of the primary issues with traditional LLMs is the potential for "hallucinations", where the model generates responses that lack factual grounding. RAG solves this issue by grounding each generated response in retrieved evidence. This ensures the LLM identifies facts from internal or external knowledge sources before generating a response, leading to more reliable outputs.

3. Source Citing

RAG enhances the transparency of AI-generated responses by citing the sources of retrieved knowledge. This feature is particularly valuable in contexts requiring verifiability, such as legal or academic fields.

4. Data Privacy

Since RAG-enabled models do not rely on pre-training with extensive external datasets, the sensitive data can be preserved within the organization’s infrastructure. This feature addresses privacy concerns and is especially valuable for applications in sectors with strict data governance requirements.

Cons

1. Complexity

Implementing a RAG pipeline demands significant technical proficiency. Setting up and maintaining retrieval mechanisms requires knowledge of vector databases, document embeddings, and search infrastructure. Moreover, it is necessary to ensure data freshness so that the model can rely on the most current information.

2. Potential for Slower Response Times

Vector similarity search, a core component of the RAG approach, can be a computationally intensive process. This can slow down response times, especially when querying large knowledge bases.

Citation
I would like to acknowledge that I took help from ChatGPT to structure my blog and simplify content.