RAG vs CAG: A Technical Comparison

Large Language Models (LLMs) have redefined what machines can understand and generate. But as impressive as they are, they can’t memorize or reason over every possible fact. That’s why we turn to augmentation strategies like RAG and CAG.

While both aim to improve LLM performance using external information, they differ in their retrieval mechanisms, architecture, and applications. Let’s break down what they are, how they differ, and when to use one over the other.

What is RAG (Retrieval-Augmented Generation)?

RAG augments the model’s generation process by retrieving external documents based on the user query and feeding them as context to the language model.

Workflow:

1. Embed the query
   
2. Retrieve top-k chunks from a vector database
   
3. Concatenate context + query
   
4. Generate output using an LLM

Key Characteristics:

• Retrieval is query-driven
• Retrieval happens once per query
• Popular for question-answering, summarization, chatbots

Tech Stack:

• Vector DBs: FAISS, Qdrant, Weaviate
• LLMs: GPT, LLaMA, Falcon
• Frameworks: LangChain, Haystack, LlamaIndex

What is CAG (Context-Augmented Generation)?

CAG doesn’t rely on a separate retrieval step. Instead, it augments input using predefined context such as metadata, schema, examples, or domain-specific background knowledge.

Workflow:

1. Identify task and context template
2. Inject static or dynamic context into the prompt (e.g., instruction + schema + examples)
3. Generate using an LLM

Key Characteristics:

• No vector store or retriever required
• Input can be structured (JSON schema, instructions)
• Useful in zero/few-shot learning, schema enforcement, and rule-based prompts

Tech Stack:

• Tools: Prompt templates, Pydantic, structured datasets
• LLMs: OpenAI, Claude, Mistral

RAG vs CAG: Feature-by-Feature Comparison

Use Case Scenarios

Use RAG When:

• You have large corpora of unstructured text
• You want real-time, context-aware generation
• You need search-like behavior in LLMs

Use CAG When:

• You have fixed schemas (e.g., Pydantic models)
• You’re building few-shot examples or rule-guided prompts
• You’re enforcing structure in LLM outputs

Code Snippet: RAG in Action

from langchain.chains import RetrievalQA
from langchain.vectorstores import FAISS
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.llms import OpenAI

# Load vector DB
vectorstore = FAISS.load_local("./index", HuggingFaceEmbeddings())
retriever = vectorstore.as_retriever()

# Setup QA chain
qa_chain = RetrievalQA.from_chain_type(llm=OpenAI(), retriever=retriever)
response = qa_chain.run("What are the benefits of LoRA for LLMs?")

Code Snippet: CAG with Pydantic Schema

from pydantic import BaseModel

class LegalClause(BaseModel):
    party: str
    contract_duration: str
    penalty_clause: str

schema = LegalClause.schema_json(indent=2)
prompt = f"""Extract the following fields in JSON format matching this schema:
{schema}

Text: The contract between ABC and XYZ will last two years and includes a 5% penalty if either party withdraws early."""

Hybrid Patterns

You can combine RAG and CAG:

• RAG retrieves documents
• CAG uses a structured prompt with schema templates

This is powerful in domains like healthcare, legal, and financial NLP where both retrieval and validation are needed.

Final Thoughts

RAG and CAG are not competitors — they’re tools in your LLM toolkit. Use RAG for scalable knowledge access, and CAG for schema-constrained or logic-guided reasoning.

Planning to develop an AI software application? We’d be delighted to assist. Connect with Jellyfish Technologies to explore tailored, innovative solutions.