When building AI systems, we often need to enhance language models with external information. Two popular approaches are RAG and CAG.
What is RAG?
Retrieval-Augmented Generation (RAG) is like giving an AI assistant access to a library. When you ask a question, the system:
- Searches through documents to find relevant information
- Passes that information to the AI
- The AI generates an answer based on what it found
# Simple RAG flow
query = "What is quantum computing?"
relevant_docs = search_database(query)
response = generate_answer(query, relevant_docs)What is CAG?
Corrective-Augmented Generation (CAG) is smarter—it checks if the retrieved information is actually good enough. The system:
- Retrieves documents like RAG
- Evaluates if the documents are relevant and accurate
- If not good enough, it searches again or uses alternative sources
- Generates an answer only when confident
# Simple CAG flow
query = "What is quantum computing?"
docs = search_database(query)
if not is_relevant(docs):
docs = web_search(query) # Try another source
response = generate_answer(query, docs)Key Differences
| Aspect | RAG | CAG |
|---|---|---|
| Approach | Retrieve and generate | Retrieve, verify, then generate |
| Quality Check | No validation | Validates retrieved content |
| Fallback | Uses whatever it finds | Can trigger alternative searches |
| Complexity | Simpler | More sophisticated |
| Best For | Clean, reliable datasets | Noisy or uncertain data sources |
When to Use Each?
Use RAG when:
- Your knowledge base is well-curated and trustworthy
- Speed is more important than perfect accuracy
- You have a controlled document set
Use CAG when:
- Working with web searches or uncertain sources
- Accuracy is critical
- Your data might contain irrelevant or outdated information
Both approaches make AI systems more reliable by grounding them in real information rather than just relying on what they learned during training!