Build AI Apps with Python: Why RAG? — Give AI Your Own Data | Episode 12

Build AI Apps with Python: Why RAG? — Give AI Your Own Data

When interacting with AI models like Claude, they often lack knowledge of your specific data, leading to inaccurate or "hallucinated" answers. Retrieval-Augmented Generation (RAG) solves this by fetching relevant documents and injecting them into the prompt, enabling the AI to answer accurately based on your own data. This approach transforms how AI applications handle company handbooks, FAQs, or any proprietary information.

Code

from langchain.llms import OpenAI
from langchain.chains import RetrievalQA
from langchain.vectorstores import FAISS
from langchain.embeddings.openai import OpenAIEmbeddings

# Assume we have a vector store of company handbook documents
embeddings = OpenAIEmbeddings()
vectorstore = FAISS.load_local("company_handbook_faiss_index", embeddings)

# Create a retriever to fetch relevant docs based on user query
retriever = vectorstore.as_retriever()

# Initialize the LLM (Claude or OpenAI)
llm = OpenAI(model_name="claude-v1")

# Create a RetrievalQA chain that retrieves docs and generates answers
qa_chain = RetrievalQA.from_chain_type(llm=llm, retriever=retriever, return_source_documents=True)

# User question about the company handbook
query = "What is the company's remote work policy?"

# Run the chain: it retrieves relevant docs and passes them to the LLM prompt
result = qa_chain.run(query)

print("Answer:", result)

Key Points

• RAG stands for Retrieve, Augment, Generate — it retrieves documents, augments the prompt, then generates an answer.
• Without RAG, AI models hallucinate when asked about unknown or proprietary data.
• Injecting retrieved documents into the prompt provides context, enabling accurate, data-driven responses.
• Vector stores and embeddings enable efficient document retrieval relevant to user queries.
• RAG allows the same AI model to answer differently based on the context you provide, improving reliability.