RAG: How AI Uses External Knowledge Without Retraining

RAG - a term you’ve likely come across amid the current wave of AI innovation. But before understanding what RAG is, it’s important to understand why it exists.

Large Language Models (LLMs) are transformer-based neural networks trained on massive datasets ranging from 1 billion to over 30 billion parameters. The knowledge they acquire during training is embedded within their parameters — known as parametric knowledge. When you query an LLM, it generates responses by drawing from this internal knowledge base.

However, this knowledge is static — frozen at the time of training. If new facts, events, or discoveries emerge afterward, the model has no awareness of them.

Worse, when faced with unfamiliar questions, these models tend to produce information that may appear convincing but is actually incorrect — this is called hallucination.

Through in-context learning, a model can adapt to a task by observing examples within the prompt — without changing its internal parameters. However, this adaptation is temporary and limited to a single interaction.

Example: Sentiment Analysis

I love this phone. → Positive
This app crashes a lot. → Negative
The camera is amazing. → Positive
Now classify: “I hate the battery life.”

The LLM infers learning contextually → outputs Negative.

The Need for RAG

These gaps led to the development of Retrieval-Augmented Generation (RAG).

RAG extends the power of LLMs by integrating them with external, up-to-date information sources.

In essence, RAG allows language models to “think with context” — grounding their responses in verifiable, current data rather than static, outdated memory.

What is Retrieval-Augmented Generation?

It means combining retrieval and generation in a single workflow.

Retrieval: The system searches an external knowledge source to find relevant information for a query.
Generation: The language model uses that retrieved information as context to produce an answer.

In short, RAG lets AI look up facts before answering, instead of relying solely on trained data.

Understanding RAG

Building a RAG-based system involves four key steps:
Indexing
Retrieval
Augmentation
Generation

Indexing

Preparation of a knowledge base for efficient searching and querying.

Document ingestion: Collect all source materials such as PDFs or website data.
Text chunking: Split large documents into smaller segments.
Embeddings creation: Convert each chunk into a vector embedding.
Vector store creation: Save embeddings in a vector database.

Retrieval

Upon user query, search the vector store to find relevant chunks.

Uses semantic similarity search.
Narrows down massive data to a few relevant segments.

Augmentation

Combine retrieved data (context) with user query to form a comprehensive prompt.

You are a helpful assistant. Use only the provided context to answer.

Context: <retrieved transcript text>

Question: <user query>

Prevents hallucination by limiting scope.

Generation

The LLM reads the combined prompt and produces a response using in-context learning.

Conclusion

RAG bridges the gap between static model memory and dynamic information. It enables AI systems to reason with real, up-to-date knowledge.