Build a Chat With Your Documents AI App Using Amazon Bedrock
Learn how to build an AI assistant that answers questions from your own documents using Amazon S3, Amazon Bedrock, and Retrieval-Augmented Generation (RAG).
# Build a Chat With Your Documents AI App Using Amazon Bedrock
Imagine uploading a PDF containing hundreds of pages and asking a simple question:
"What are the three most important points in this document?"
Instead of searching manually, an AI assistant finds relevant information and generates an answer based on the available content.
This is one of the practical applications of Generative AI, and you can build it using AWS services.
In this article, we'll explore how to design a document question-answering assistant using Amazon S3, Amazon Bedrock, and Retrieval-Augmented Generation.
## What Are We Building?
We'll build an application that allows users to ask questions about a collection of documents.
For example, you could upload technical documentation, project reports, company policies, or study materials.
The application retrieves relevant content and uses an AI model to generate a contextual answer.
The architecture looks like this:
Documents
↓
Amazon S3
↓
Document Processing and Chunking
↓
Embeddings and Retrieval
↓
Amazon Bedrock Foundation Model
↓
Answer Based on Retrieved Content
↓
Amazon S3
↓
Document Processing and Chunking
↓
Embeddings and Retrieval
↓
Amazon Bedrock Foundation Model
↓
Answer Based on Retrieved Content
Amazon Bedrock Knowledge Bases can simplify much of the retrieval and knowledge-base workflow, depending on the configuration you choose.
## Why Not Just Send the Entire Document to an AI Model?
A common first idea is to send the entire document to an AI model whenever someone asks a question.
However, large document collections can exceed context limits, increase processing costs, and include lots of irrelevant information.
A better approach is to retrieve the sections that are most relevant to the question.
This is the basic idea behind Retrieval-Augmented Generation, commonly called RAG.
## What Is RAG?
RAG combines information retrieval with text generation.
Instead of relying only on information learned during model training, the application retrieves relevant content from an external knowledge source.
The process has two main stages.
During ingestion, documents are processed, split into smaller chunks, and prepared for retrieval.
During a query, the system searches for relevant chunks and supplies them to the model as context for generating an answer.
This can help the application answer questions about specific documents without training a new foundation model.
## Step 1: Store Documents in Amazon S3
Amazon S3 provides scalable object storage for the source documents.
Create a bucket and upload a small collection of sample files.
For example, you could start with product manuals, technical guides, or your own project documentation.
Keep the bucket private unless public access is genuinely required.
Choose an appropriate access policy and avoid storing sensitive documents without understanding their security requirements.
## Step 2: Prepare the Documents for Retrieval
Documents need to be processed before a retrieval system can find relevant information effectively.
A typical ingestion process includes extracting text, splitting it into manageable chunks, and generating embeddings.
An embedding represents content as a numerical vector that can be used to measure semantic similarity.
For example, a question about password protection may retrieve a document section discussing account security, even if the wording differs.
With Amazon Bedrock Knowledge Bases, supported ingestion and retrieval workflows can handle many of these tasks for you.
## Step 3: Use Amazon Bedrock to Generate Answers
Amazon Bedrock provides access to foundation models through managed APIs.
When a user asks a question, the application retrieves relevant content and supplies it to the selected model.
For example:
Question: How should AWS credentials be protected?
Retrieved context: Documentation explaining IAM roles, temporary credentials, and credential management.
Generated answer: A concise response based on the retrieved information.
The quality of the result depends on the retrieved content, model choice, instructions, and application design.
## Step 4: Reduce Unsupported Answers
An AI assistant should not confidently invent information when its documents do not contain the answer.
You can improve reliability by instructing the model to answer using retrieved context, acknowledge when evidence is insufficient, and provide source references where supported.
However, these instructions alone cannot guarantee correctness.
Test the application with questions whose answers are present in the documents and questions whose answers are missing.
## Step 5: Protect the Knowledge Base
Document access is an important part of the architecture.
Use IAM permissions to restrict access to the required AWS resources.
Keep private documents in appropriately secured storage, and ensure that users can retrieve only information they are authorized to access.
If different users have different document permissions, enforce those restrictions in the retrieval and application layers.
Never rely on the model itself to enforce access control.
## Step 6: Measure the Quality of Answers
A working chatbot is not necessarily a reliable chatbot.
Create a small evaluation set containing realistic questions and expected answers.
Check whether the system retrieves the correct document sections, answers accurately, acknowledges missing information, and respects access restrictions.
Also measure response latency and cost.
Testing these aspects helps you understand where the application needs improvement.
## What Can You Build With This Architecture?
The same approach can support many applications:
A chatbot for technical documentation.
A university study assistant.
An internal company knowledge assistant.
A product-support assistant.
A searchable project documentation system.
The architecture can be extended with an application interface, authentication, monitoring, and additional data sources.
## Final Thoughts
Generative AI becomes more useful when it can work with relevant information instead of relying entirely on a general-purpose model.
Amazon S3 can store the source documents, Amazon Bedrock can support the retrieval and generation workflow, and a RAG architecture can connect users' questions to relevant information.
Start with a small document collection, test the answers carefully, and improve retrieval quality before adding complexity.
You do not need to train a foundation model from scratch to build a useful AI application.
You need the right data, a well-designed retrieval workflow, and appropriate security controls.
## Your Turn
If you were building this application, what would you choose first: a personal document assistant, a college study assistant, or a company knowledge chatbot?
Share your choice and explain why. It would be interesting to see which use case other AWS builders find most useful.
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