RAG on AWS: Connecting Your Own Data to AI with Amazon Bedrock
Generative AI models know a lot, but they do not automatically know your application’s private documents, business rules, or internal data. In this article, we will understand how RAG works and how Amazon Bedrock Knowledge Bases can connect your own data to AI models without requiring you to train a model from scratch.
The Problem with AI Models
Imagine that you created a chatbot for a company.
You can use a large language model to answer questions such as:
“What is cloud computing?”
No problem.
But what if the user asks:
“What is my company’s refund policy?”
The model does not automatically know that document.
One alternative would be to retrain the model using the company’s data, but that can be expensive, complex, and unnecessary.
This is exactly where RAG — Retrieval-Augmented Generation comes in.
What Is RAG?
RAG stands for Retrieval-Augmented Generation.
The idea is relatively simple.
When a user sends a question, the system searches for related information inside a knowledge base.
The most relevant pieces of information are retrieved and sent to the language model together with the original question.
The flow can be represented like this:
User → Context Retrieval → Relevant Documents → LLM → Response
Instead of expecting the model to know everything, we provide the information it needs at the moment the question is asked.
This makes it possible to build applications that answer questions using:
- internal documentation;
- manuals;
- contracts;
- FAQs;
- company policies;
- technical documents;
- knowledge bases.
Where Does Amazon Bedrock Come In?
Amazon Bedrock is an AWS service designed for building applications with Generative AI and Foundation Models.
Inside Bedrock, there is a feature called Knowledge Bases for Amazon Bedrock, which is specifically designed to simplify RAG-based architectures.
Imagine that our company has hundreds of documents stored in Amazon S3.
We can connect those documents to a Knowledge Base.
From there, the content goes through several important stages.
Documents → Chunking → Embeddings → Vector Store
Then, when a user asks a question, the application searches for the document chunks that are most semantically related to that question.
Those chunks are then sent to the model as context.
But What Are Embeddings?
Embeddings are numerical representations of information.
Consider these two sentences:
“How can I cancel my subscription?”
and
“I want to terminate my plan.”
The words are different, but the meaning is very similar.
An embedding model transforms these sentences into numerical vectors.
Sentences with similar meanings tend to produce vectors that are close to each other.
This allows us to perform semantic search.
Instead of searching only for exact keyword matches, the system can search for content with a similar meaning.
This technique is one of the foundations of RAG architectures.
RAG Architecture on AWS
A simplified architecture could look like this:
Amazon S3
↓
Application documents
↓
Amazon Bedrock Knowledge Bases
↓
Chunking
↓
Embeddings
↓
Vector Store
↓
Semantic Search
↓
Retrieved Context
↓
Foundation Model
↓
Response to the User
Amazon Bedrock Knowledge Bases helps manage several of these steps, reducing the amount of infrastructure we would need to build manually.
A Practical Example
Imagine a company that has 500 technical documents stored in Amazon S3.
A developer asks:
“How does our API authentication process work?”
In a traditional application, we would need to manually create logic to search through those documents.
With RAG, the process could work like this:
- The question is transformed into a vector representation.
- The system searches for semantically related document chunks.
- The most relevant content is retrieved.
- That content is sent to the model.
- The model uses that information to generate the answer.
This turns a collection of documents into a kind of intelligent knowledge base.
Possible Use Cases
Documentation Assistant
A developer could ask:
“How does the payments endpoint work?”
The system would search for the answer directly in the technical documentation.
Customer Support
A user could ask:
“Can I cancel my plan after 30 days?”
The application would consult the company’s policies before generating a response.
Document Analysis
It would also be possible to query contracts, reports, regulations, or internal documents using natural language.
Internal Assistant
Companies can build assistants capable of answering questions using private documents without making that information part of the public training data of a model.
Why Not Simply Send the Entire Document to the LLM?
This was one of the things that caught my attention the most when I started studying RAG.
At first, it may seem easier to send an entire PDF to the model.
But imagine doing that with thousands of documents.
Sending everything on every request would be extremely inefficient.
That is why retrieval is important.
The system first identifies which information is probably needed.
Then, it sends only the most relevant pieces of content to the model.
This helps reduce the amount of context used and makes the architecture more efficient.
RAG Does Not Solve Everything by Itself
Although RAG is a very powerful technique, adding it to an application does not automatically mean that every answer will be perfect.
There are still several important decisions to make.
For example:
- chunk size;
- chunk overlap;
- embedding model;
- number of retrieved documents;
- source quality;
- access permissions;
- response evaluation;
- latency;
- cost.
Retrieval quality is extremely important.
If the system retrieves poor information, the model will receive poor context.
We can think about it like this:
Good LLM + Bad Context = Potentially Bad Answer
That is why, in RAG applications, improving retrieval can be just as important as choosing the language model itself.
What I Found Most Interesting About RAG
When I started studying LLMs, one of the questions that caught my attention the most was:
How can an AI answer questions using data it has never seen during training?
RAG is one of the answers to that problem.
Instead of putting all the knowledge inside the model, we can create an architecture where knowledge is retrieved dynamically.
This also changes the way we think about AI applications.
The LLM is no longer the entire application.
It becomes one component inside a larger architecture.
And that is exactly where Backend, Cloud, and Software Architecture concepts start to connect with Artificial Intelligence.
Conclusion
RAG is one of the most interesting architectures for modern Generative AI applications.
With services such as Amazon Bedrock Knowledge Bases, we can connect language models to our own data without having to build the entire retrieval infrastructure from scratch.
The basic flow remains simple:
Question → Retrieval → Context → LLM → Response
But behind that flow, there are several important engineering concepts:
Cloud, storage, embeddings, vector databases, security, APIs, and Artificial Intelligence.
For anyone studying Backend + AWS + AI, understanding this type of architecture is a great next step.
Because many real-world AI applications do not depend only on asking a model a question.
They depend on giving the model:
the right information, at the right time.
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