​
Introduction

Retrieval Augmented Generation (RAG) is a technique designed to enhance LLMs by integrating external knowledge bases into the response generation process. While LLMs are trained on large datasets, they often lack domain-specific or real-time knowledge about certain topics. RAG addresses these gaps by retrieving relevant information from external sources and incorporating it into the model’s responses.

At Wetrocloud, we leverage RAG behind the scenes to enrich responses with contextually relevant data from external knowledge bases. This approach enables the system to adapt to diverse use cases, making it a good alternative for scenarios requiring specialized information.

​
RAG: A Mental 🧠 Model to Understand

Imagine you’re writing an essay, but instead of relying on just your memory, you have a library next to you. When you come across a question or topic you’re unsure about, you pause, walk over to the library, find the most relevant book, and use the information to continue writing your essay.

In this analogy:

  • You are the language model (LLM), trained on a lot of knowledge but limited to what you already know.
  • The library is the external knowledge base, full of updated and domain-specific information.
  • RAG is the process of going to the library, picking the right book, and using its content to create a more informed essay.

​
Usecases of RAG

RAG has become incredibly popular these days, Some common use cases of RAG include:

  • Customer service chatbots
  • Virtual assistants
  • Enterprise search engines
  • Document summarization
  • Conversational chatbots

​
Further Reading

​
Introduction

Retrieval Augmented Generation (RAG) is a technique designed to enhance LLMs by integrating external knowledge bases into the response generation process. While LLMs are trained on large datasets, they often lack domain-specific or real-time knowledge about certain topics. RAG addresses these gaps by retrieving relevant information from external sources and incorporating it into the model’s responses.

At Wetrocloud, we leverage RAG behind the scenes to enrich responses with contextually relevant data from external knowledge bases. This approach enables the system to adapt to diverse use cases, making it a good alternative for scenarios requiring specialized information.

​
RAG: A Mental 🧠 Model to Understand

Imagine you’re writing an essay, but instead of relying on just your memory, you have a library next to you. When you come across a question or topic you’re unsure about, you pause, walk over to the library, find the most relevant book, and use the information to continue writing your essay.

In this analogy:

  • You are the language model (LLM), trained on a lot of knowledge but limited to what you already know.
  • The library is the external knowledge base, full of updated and domain-specific information.
  • RAG is the process of going to the library, picking the right book, and using its content to create a more informed essay.

​
Usecases of RAG

RAG has become incredibly popular these days, Some common use cases of RAG include:

  • Customer service chatbots
  • Virtual assistants
  • Enterprise search engines
  • Document summarization
  • Conversational chatbots

​
Further Reading