LLM (Large Language Model)
An LLM like GPT-4 or Claude is:
A pretrained model on massive text data
Generates answers based on what it has learned during training
Doesn’t know your private or real-time data unless provided in the prompt
Limitation:
Can hallucinate
Knowledge is static (cutoff-based)
RAG (Retrieval-Augmented Generation)
RAG is a system design pattern, not a model.
It works like this:
User asks a question
System retrieves relevant data (docs, DB, APIs, vector search)
That data is injected into the prompt
LLM generates an answer using that context
LLM can be seen as a generator
RAG is a combination of retriever and LLM
Core Differences
| Aspect | LLM | RAG |
|---|---|---|
| Type | Model | Architecture / Pattern |
| Knowledge Source | Training data | External + Real-time data |
| Accuracy | Can hallucinate | More grounded |
| Updates | Requires retraining | Just update data source |
| Use Case | General tasks | Domain-specific, factual Q&A |
Without RAG:
User: “What’s the latest interest rate?”
LLM: Might guess or give outdated info
With RAG:
System fetches latest rates from DB/API
LLM answers using that data
Accurate and up-to-date
Usage
Use LLM alone when:
Creative writing
General coding help
Brainstorming
Use RAG when:
You need company data / internal docs
Accuracy matters (finance, legal, healthcare)
Data changes frequently
