Building a RAG Chatbot Using LangChain and Amazon Bedrock

LangChain allows us to easily create LLM applications using a simple chain-like structure. We can integrate the capabilities of LangChain with AWS Bedrock Knowledge Bases and foundational models to create chatbots.

In this Cloud Lab, you’ll learn how to build a Retrieval-Augmented Generation (RAG) chatbot using LangChain and Amazon Bedrock. You’ll start by setting up an Amazon Bedrock Knowledge Base with an Aurora Serverless instance as its vector store. Also, you’ll create an S3 bucket to store the source files for the knowledge base. The knowledge base will access the S3 bucket through an IAM role. Then, you’ll use LangChain to create a retriever and generator chain. You’ll use the knowledge base as the retriever and the Anthropic Claude model as the generator. Finally, you’ll bring your application to life with a Streamlit frontend to test your RAG model.

By the end of this Cloud Lab, you’ll be well-equipped to use Bedrock Knowledge Bases and base models in your AI applications. The architecture diagram shows the infrastructure you’ll build in this Cloud Lab:

Why RAG chatbots are the most practical kind of “AI assistant”

Most real-world chatbots fail for one simple reason: they don’t have reliable access to your knowledge. A general-purpose model can write fluent answers, but it can’t be trusted to know your product docs, internal policies, or the latest information your users need.

Retrieval-augmented generation (RAG) addresses this issue by incorporating a retrieval step prior to generation. The chatbot pulls relevant chunks from your documents and then uses those chunks as grounding context for the model’s response. The result is an assistant that can sound natural while staying anchored to your content.

Where LangChain fits in a RAG system

LangChain is commonly used as an orchestration layer. It helps you wire together the moving pieces of a RAG pipeline:

• Document loaders and preprocessing

• Chunking strategies

• Embeddings generation

• Vector search and retrieval

• Prompt templates and response formatting

• Memory and conversational patterns (when appropriate)

The advantage is speed and clarity: you can prototype and iterate on your pipeline without rewriting glue code every time you change a component.

Why Amazon Bedrock is a good model layer for RAG

Amazon Bedrock provides managed access to foundation models and (depending on your setup) embeddings. In a RAG chatbot, Bedrock typically supplies the generation step, turning the user question and retrieved context into a final answer.

When teams build on AWS, the appeal is that Bedrock integrates naturally with the rest of the stack you already use (storage, serverless, IAM, observability). That makes it easier to move from “prototype” to “something you can actually operate.”

The key design decisions that shape chatbot quality

A RAG chatbot’s quality usually depends less on the model and more on the retrieval design choices:

• Chunking and document structure: Good chunk boundaries (based on headings/sections) often outperform arbitrary fixed-size splits.

• Retrieval strategy: Semantic retrieval is powerful, but hybrid retrieval (semantic + keyword) can be even better for technical documentation.

• Metadata filtering: If you have multiple products, versions, or audiences, filters reduce irrelevant results and improve precision.

• Prompt discipline: The prompt should explicitly instruct the model to rely on the provided context and avoid guessing. This is one of the highest ROI “guardrails” you can add.

• Fallback behavior: Define what happens when retrieval is weak: ask a clarifying question, respond with “I don’t know,” or route to a human/help article.

How to evaluate a RAG chatbot before shipping it

A practical evaluation approach includes:

• A test set of real user questions.

• Checks for retrieval relevance (did it fetch the right chunks?).

• Checks for faithfulness (did the answer stick to sources?).

• Latency and cost monitoring (RAG can get expensive if you retrieve too much).

• Safety and Privacy Review (What Data Can the Bot Access?).

The goal isn’t perfection, it’s predictable behavior under the queries your users will actually ask.