Enhancing Text Generation with RAG | Course and Power Point for Bots

The article discusses Retrieval Augmented Generation (RAG) and Language Models (LLM), explaining how RAG combines information retrieval with text generation and how LLM predicts word probabilities. It also details the setup of RAG, including the need for embeddings, a Vector Database, and tokenization, and highlights the benefits of combining RAG/Vector DB with a Large Language Model like GPT-3 for enhanced text generation.
SLIDE25
SLIDE25
        
SLIDE26
SLIDE26
        
SLIDE27
SLIDE27
        
SLIDE28
SLIDE28
        
SLIDE29
SLIDE29
        
SLIDE30
SLIDE30
        
SLIDE31
SLIDE31
        
        

Concept Description
Retrieval Augmented Generation (RAG) RAG is a technique that combines information retrieval with text generation. It involves retrieving relevant information from a database or knowledge source and using it to enhance the generation of text responses.
Language Model (LLM) A Language Model is a statistical model that is able to predict the probability of a word or sequence of words in a given context. Large Language Models like GPT-3 from OpenAI are capable of generating human-like text.
Setting Up RAG To set up RAG, you would need to create or have access to a Vector Database that contains embeddings of the information you want to retrieve. This involves tokenizing the text data and converting it into numerical vectors that represent the semantic meaning of the text.
Components Needed Components needed for setting up RAG include:
  • Embeddings: Numerical representations of text data.
  • Vector Database: Storage for the embeddings of the information.
  • Tokenization: Process of breaking down text into tokens or words.
Combining RAG/Vector DB with LLM To combine RAG/Vector DB with a Large Language Model like OpenAI's GPT-3, you would use the retrieved information from the Vector DB to augment the text generation process of the LLM. The retrieved information can provide context or additional details for the LLM to generate more accurate and relevant responses.


Topics

Related Links