What is RAG: Definition, use cases, and how to implement it
Retrieval-Augmented Generation is a transformative approach to building smarter, dynamic AI systems. A practical guide along with an example implementation.
Jan 16, 2025 • 7 Minute Read
Picture this: You’re at a sprawling library filled with countless books, documents, and journals. You have a question that needs answering, but you don’t have the time to sift through the shelves. Luckily, the librarian is a genius—they quickly fetch the most relevant books and even summarize the answer for you. Efficient, right?
This is how Retrieval-Augmented Generation (RAG) works. Think of RAG as the AI equivalent of that brilliant librarian who doesn’t just know where to look for answers but also crafts a coherent response tailored to your needs. RAG combines two powerful processes: retrieving relevant information from external sources and generating responses based on that information.
What’s the big deal about RAG?
Let’s say you’re using a chatbot powered by a large language model (LLM). While the model is highly intelligent and creative, it has a limitation: it only knows what it was trained on. Its knowledge is static and limited to data that existed up to a specific point in time. If you ask it about recent developments, niche topics, or highly specific information, it might fumble.
RAG overcomes this by dynamically fetching relevant, up-to-date information from external knowledge sources, such as databases, documents, or APIs. This makes the system both more accurate and adaptable.
Have you ever used a chatbot or virtual assistant that gave outdated or incomplete information? Imagine how much better it could be if it could search for and incorporate current, relevant data in real-time!
Breaking Down the Magic of RAG
To understand RAG, we need to look at its two core components: retrieval and generation.
High-Level Overview
At a basic level, RAG works like this:
Retrieval: When you ask a question, the system searches through a connected knowledge base—like a collection of documents, articles, or a database—and fetches the most relevant pieces of information.
Generation: The retrieved information is passed to a language model, which uses it to generate a detailed, coherent response.
This combination enables RAG to provide contextually rich answers that are informed by the latest or domain-specific knowledge.
Deeper Dive
From a technical perspective, RAG combines two types of AI models:
Retriever: A system that identifies the most relevant pieces of information based on your query. It uses techniques like vector embeddings, where text is represented as numerical vectors to measure similarity.
Generator: A language model (such as GPT) that takes the retrieved information and crafts a fluent response tailored to your query.
For example:
The retriever might identify two documents about "RAG in AI" from a database of thousands.
The generator then uses these documents as context to answer your question: “What is RAG?”
Why RAG matters
RAG isn’t just a cool concept—it’s solving real-world challenges in AI.
High-Level Benefits
Up-to-Date Answers: Unlike static LLMs, RAG dynamically integrates new knowledge, ensuring answers are accurate and timely.
Domain-Specific Expertise: By connecting to a domain-specific knowledge base, RAG can act as an expert in fields like healthcare, law, or finance.
Reduced Model Size: Instead of training massive models on all possible knowledge, RAG offloads much of the data to external storage, reducing computational requirements.
Deeper Impact
Traditional LLMs are like encyclopedias: they’re great for general knowledge but fall short when dealing with time-sensitive or niche topics. By augmenting LLMs with retrieval, RAG transforms them into dynamic systems capable of:
Customer Support: Providing precise, informed responses based on up-to-date internal documentation.
Healthcare Applications: Assisting doctors with the latest research and treatment guidelines.
Personalized Learning: Creating AI tutors that adapt to students' knowledge gaps using external resources.
RAG bridges the gap between static training and real-time knowledge, making it an invaluable tool for building smarter, more useful AI systems.
How Does RAG Work?
Let’s break down the process of RAG into clear, actionable steps.
Step 1: Data Preparation
Before anything, we need a knowledge base. This is where the retriever will search for relevant information. The knowledge base can include:
A collection of unstructured documents (PDFs, text files, web pages).
A structured database with organized information.
Real-time APIs that fetch live data.
To make this data usable, we process it into embeddings—numerical representations of text that capture semantic meaning. Think of embeddings as a unique fingerprint for each piece of information, enabling the retriever to measure how closely a document matches the user’s query.
Step 2: Retrieval
When a query is made, the retriever searches the knowledge base for the most relevant pieces of information. It uses vector similarity search to compare the query’s embedding with those of the documents.
For example, if you ask, “What is RAG?” the retriever might fetch two documents with high similarity scores:
Document 1: “RAG combines retrieval of external data with language model generation.”
Document 2: “It is a system for answering questions using real-time data.”
Step 3: Generation
The retrieved documents are passed to the generator, which integrates them into its response. A language model (like GPT) reads the documents and crafts an answer that feels natural and coherent.
For instance, it might combine the above documents into a response like:
“RAG, or Retrieval-Augmented Generation, is a system that enhances language models by integrating real-time data retrieval from external sources.”
Example Implementation
Here’s how to build a simple RAG system using Python.
Install Required Libraries
pip install faiss-cpu openai langchain
Step 1: Index Documents
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import FAISS
# Define your documents
documents = [
"RAG stands for Retrieval-Augmented Generation.",
"It combines retrieval of external data with language model generation.",
"FAISS is a popular tool for similarity search and dense vector indexing."
]
# Generate embeddings
embedding_model = OpenAIEmbeddings()
doc_embeddings = [embedding_model.embed(text) for text in documents]
# Create FAISS index
index = FAISS.from_documents(documents, embedding_model)
Step 2: Retrieve Relevant Data
query = "What is RAG?"
retrieved_docs = index.similarity_search(query, k=2) # Retrieve top 2 documents
Step 3: Generate an Answer
from langchain.chains import RetrievalQA
from langchain.llms import OpenAI
llm = OpenAI()
qa_chain = RetrievalQA(llm, retriever=index)
response = qa_chain.run(query)
print("AI Response:", response)
Implementing RAG at Organizations
RAG is ready for implementation in organizations today. Here's a step-by-step guide:
1. Identify Use Cases:
- Knowledge retrieval for customer support teams.
- Internal knowledge management for employees.
- Personalized user experiences in healthcare, retail, or education.
2. Prepare Your Knowledge Base
Gather domain-specific documents, FAQs, or data logs. Use a tool like Hugging Face’s Sentence Transformers or OpenAI Embeddings API to convert these into vector embeddings.
3. Choose a Retriever and Generator
- Retriever: Use FAISS for fast similarity searches or ElasticSearch for larger datasets.
- Generator: Use pre-trained models from Hugging Face (e.g., facebook/bart-large-cnn or t5-base).
4. Deploy as a Microservice:
- Use Flask to create an API that combines retrieval and generation.
- Integrate this microservice into existing workflows or applications.
Challenges and Best Practices
High-Level Considerations
Data Quality: The AI’s accuracy depends heavily on the quality of the knowledge base. Poorly curated or outdated data can lead to incorrect answers.
Latency: Combining retrieval and generation can take time, especially with large datasets or complex queries.
Deeper Challenges
Embedding Accuracy: Using high-quality embedding models (like OpenAI or Sentence Transformers) is critical for effective retrieval.
Scaling Issues: As the knowledge base grows, efficient indexing tools like FAISS or ElasticSearch become essential.
- Fine-Tuning: While RAG reduces the need for frequent retraining, fine-tuning the generator on your domain can significantly improve performance.
Full Implementation with Hugging Face and Flask
Here’s how to deploy a RAG-based microservice.
Step 1: Install Required Libraries
pip install flask transformers sentence-transformers faiss-cpu
Step 2: Create and Index the Knowledge Base
from sentence_transformers import SentenceTransformer
import faiss
# Sample documents
documents = [
"RAG stands for Retrieval-Augmented Generation.",
"It combines retrieval of external data with language model generation.",
"FAISS is a library for fast similarity search and dense vector indexing."
]
# Load sentence transformer model
embedding_model = SentenceTransformer('all-MiniLM-L6-v2')
doc_embeddings = embedding_model.encode(documents)
# Create FAISS index
dimension = doc_embeddings.shape[1]
index = faiss.IndexFlatL2(dimension)
index.add(doc_embeddings)
Step 3: Set Up Flask Microservice
from flask import Flask, request, jsonify
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
# Load Hugging Face generator model
tokenizer = AutoTokenizer.from_pretrained("t5-small")
generator_model = AutoModelForSeq2SeqLM.from_pretrained("t5-small")
app = Flask(__name__)
@app.route("/rag", methods=["POST"])
def rag():
query = request.json.get("query")
if not query:
return jsonify({"error": "Query is required"}), 400
# Encode query and perform retrieval
query_embedding = embedding_model.encode([query])
distances, indices = index.search(query_embedding, k=2) # Top 2 results
# Retrieve documents
retrieved_docs = [documents[idx] for idx in indices[0]]
# Generate response
input_text = " ".join(retrieved_docs) + " " + query
inputs = tokenizer.encode("summarize: " + input_text, return_tensors="pt", max_length=512, truncation=True)
outputs = generator_model.generate(inputs, max_length=50, num_beams=4, early_stopping=True)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
return jsonify({"response": response})
if __name__ == "__main__":
app.run(port=5000)
Step 4: Test the API
Start the Flask server and send a POST request:
curl -X POST -H "Content-Type: application/json" \
-d '{"query": "What is RAG?"}' http://127.0.0.1:5000/rag
Conclusion
RAG is a transformative approach to building smarter, more dynamic AI systems. By combining the strengths of retrieval and generation, it allows AI to provide accurate, timely, and context-aware responses.
What You Can Do Next:
If you’re curious, think about how RAG could enhance your daily interactions with AI. What problems could it solve in your industry or personal life?
If you’re a developer, challenge yourself to build your own RAG system using the code provided. Customize it for a domain you care about—whether it’s healthcare, education, or customer service.
With RAG, we’re moving closer to creating AI systems that not only think creatively but also know exactly where to find the information we need.
Dedicated RAG learning resources
Liked this article? Make sure to check out Axel Sirota's course, "Vector Space Models and Embeddings in RAGs," which covers how to implement a RAG-based chatbot using Python and TensorFlow, focusing on text embeddings and retrieval techniques. It's part of Pluralsight's learning path, Retrieval Augmented Generation (RAG) for Developers, which covers everything you need to know from RAG deployment, maintenance, fine-tuning, scaling, and more.
You can try the learning path out with Pluralsight's 10-day free trial, and also explore Pluralsight's full 7,000+ course library. Dive into our expert-led courses and establish the foundation you need to make the most of AI technology.
