1. What is RAG?
RAG (Retrieval-Augmented Generation) combines **LLMs** with a **retrieval system** (like a vector database). The AI retrieves relevant information from external sources, then **generates a response** grounded in that knowledge.
Example: A user asks: "What is the latest guideline on AI ethics?"
RAG retrieves current ethical frameworks from a knowledge base and generates a summarized answer.
2. How RAG Works
- Convert documents into embeddings (vectors).
- Store embeddings in a vector database.
- User query is converted to a vector.
- Retrieve top-k similar vectors from DB.
- Pass retrieved context to LLM for generation.
This approach makes AI answers **accurate, up-to-date, and domain-specific**.
3. Simple Python Example (LangChain + FAISS)
from langchain.vectorstores import FAISS
from langchain.embeddings import OpenAIEmbeddings
from langchain.chains import RetrievalQA
from langchain.llms import OpenAI
# Create embeddings
embeddings = OpenAIEmbeddings()
vectorstore = FAISS.load_local("my_faiss_index", embeddings)
# Create RAG QA chain
qa = RetrievalQA.from_chain_type(
llm=OpenAI(),
chain_type="stuff",
retriever=vectorstore.as_retriever()
)
# Ask a question
query = "Explain responsible AI guidelines."
answer = qa.run(query)
print(answer)
The system retrieves relevant knowledge and generates a precise answer.
This demonstrates the **power of combining retrieval + generation**.
4. Applications
- AI chatbots that answer with updated documents
- Summarization of technical manuals or research papers
- Domain-specific knowledge assistants
- Customer support knowledge bases
- AI tutors and education assistants
5. Try It Yourself
Take a set of documents (like articles or PDFs), create embeddings, store in FAISS or Milvus,
then build a RAG system with an LLM. Test queries and observe how retrieved context improves answers.
6. Inspirational Quote
"Knowledge is powerful, but context makes it wise." — RAG Philosophy