Retrieval Augmented Generation (RAG) Explained

Jun 17, 2025

Large language models unlocked modern AI agents, yet they rely on static training data. When that data ages, answers drift. Retrieval-Augmented Generation (RAG) solves the drift by letting a model pull fresh, domain-specific facts at inference time.

In this blog, we’ll explain RAG and show you how to implement it.

Techniques to improve LLMs

There are four ways to improve LLMs: zero-shot prompting, few-shot prompting, RAG, and fine tuning.

Zero-Shot Prompting is simply describing the task to LLM without examples in the prompt.
Few-Shot Prompting improves on zero-shot by including a handful of examples in the prompt.
Retrieval-Augmented Generation (RAG) combines retrieval from external knowledge sources with language model generation.
Fine-Tuning retains a LLM further on a task-specific dataset.

A study below shows model accuracy improves as technique complexity increases. Big base models (e.g. GPT models) consistently achieve higher performance increase at each stage when compared with small base models (e.g. Mistral-7B).

So, why is RAG so special?

Benefits of RAG

RAG offers three advantages over the other techniques when it comes to enterprise applications.

RAG is faster to deploy than fine-tuning. Fine-tuning requires large volume of labeled data, significant compute resources, and ongoing maintenance.
RAG is easier to maintain than fine-tuning. RAG decouples knowledge from the model parameters, so teams can update the retrieval layer independently without re-training the entire model.
RAG systems can cite sources and snippets to build trust in the model's responses. The ability to verify output information is critical for use cases including customer support, legal research, or healthcare.

The Architecture of RAG

RAG has two steps: retrieving information and generating response.

There are five components in a typical RAG architecture:

External Documents: Company documents, such as financial data, contracts, customer contacts, are transformed from their original form into embeddings. Embeddings are numerical representations (i.e. vectors) that capture semantic meanings of texts, images, and videos. These vectors allow for efficient similarity comparison between texts, therefore enabling fast search across documents to find the relevant piece.
Embedding Store: The embedding store is a vector database, which simply means a database that contains the embedding vectors. These vectors are organized into a structure that is optimized for semantic search, and they are also chunked into smaller, manageable pieces for LLMs to process later.
Retriever: A retriever is a function created to accept the user message.
Querying: The retriever initiates the search for external documents, and the embedding store returns chunks of relevant information.
Context: The relevant information is incorporated into the LLM context window. The context window now contains user messages (i.e. prompt), retrieved information, and the rest of the conversation.

Whit this architecture, RAG addresses LLM’s limitation and informs problem solving.

Implement RAG with Knowledge Base

At Stack AI, we serve customers in healthcare, operations, education, and finance by building AI agents with RAG. All components of the RAG architecture are neatly packaged into the Knowledge Base in our platform.

External documents can be uploaded into a central cloud storage or individual projects.