RAG Fundamentals and Advanced Techniques freeCodeCamp

• What does RAG stand for?

  Retrieval Augmented Generation
• What is the main idea of RAG?

  To enhance large language models with specific data
• What problem does RAG address?

  It allows models to access user-specific information
• How does RAG improve responses from large language models?

  By injecting user-specific data into the model
• What are the two main components of RAG?

  Retriever and Generator
• What does the retriever do in RAG?

  It identifies and retrieves relevant documents
• What is the role of the generator in RAG?

  To create coherent and contextually relevant responses
• How does RAG define itself?

  A framework combining retrieval and generation strengths
• What is the goal of RAG?

  To produce accurate and contextually relevant responses
• How does RAG customize a large language model?

  By injecting user-specific data into the model
• What happens to documents in RAG?

  They are cut into small chunks for processing
• What is created from document chunks in RAG?

  Embeddings for vector representation
• What is the purpose of the embedding model in RAG?

  To transform data into vector representations
• What does the vector database do in RAG?

  Stores vectorized document chunks for retrieval
• What is the augmentation phase in RAG?

  Adding relevant documents to the query
• What is the final step in the RAG process?

  Generating a response using a large language model
• What is the purpose of the virtual environment in Python?

  To manage dependencies for projects
• What is Chroma DB used for in RAG?

  To store vectorized document data
• What is the embedding function used for?

  To create embeddings from document data
• What is the first step in using the OpenAI client?

  Pass the API key to the client
• What does the client allow you to do?

  Interact with the OpenAI API for responses
• What is the purpose of the 'chat.open' function?

  To create chat completions with the model
• What is the expected output when querying the model?

  A coherent response based on the input
• What is the significance of the 'choices' in the response?

  It contains the generated message from the model
• What do we create to interact with the OpenAI API?

  A client
• What is the purpose of the API key in the client creation?

  It authenticates the client with OpenAI
• How do you access the content of the response from the client?

  By accessing res.choices.message.content
• What type of files are loaded from the directory?

  Text files
• Why do we split documents into chunks?

  To maintain contextual meaning
• What is the chunk size set to when splitting documents?

  1,000 characters
• What does the overlap parameter do when splitting documents?

  It ensures context is preserved
• What is the purpose of generating embeddings?

  To save them in the vector database
• How are embeddings created from text?

  Using OpenAI's embedding model
• What function is called to generate embeddings for chunked documents?

  get_openai_embeddings
• What does the query_documents function do?

  It retrieves documents based on a query
• What is passed to the query_documents function?

  A question and expected results count
• What happens during the similarity search in the database?

  It finds documents relevant to the query
• How does the generate_response function utilize the relevant chunks?

  It combines them with the question for context
• What is the role of the prompt in the generate_response function?

  It guides the model's answer generation
• What is the expected output when querying about AI replacing TV writers?

  Relevant information from the documents