CLOUD LABS
Building a RAG Chatbot Using LangChain and Amazon Bedrock
In this Cloud Lab, you’ll learn to create a RAG chatbot using Bedrock Knowledge Bases and base models. You’ll also explore utilizing these resources to build a LangChain chatbot.
intermediate
Certificate of Completion
Learning Objectives
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.
Before you start...
Try these optional labs before starting this lab.
Relevant Courses
Use the following content to review prerequisites or explore specific concepts in detail.
Frequently Asked Questions
What is a RAG chatbot?
A RAG chatbot is a conversational assistant that retrieves relevant information from a document set (or knowledge source) and uses that retrieved context to generate an answer. This makes responses more grounded than those of a chatbot that relies solely on model memory.
Why use RAG instead of fine-tuning a model?
RAG is often faster and more maintainable. You can update knowledge by updating documents and re-indexing, rather than retraining. Fine-tuning can help with style or task behavior, but it doesn’t automatically keep the model up to date with changing knowledge.
What does LangChain actually do in a RAG chatbot?
LangChain helps orchestrate the pipeline: loading documents, splitting them into chunks, generating embeddings, retrieving relevant chunks, and assembling the prompt that the model uses to generate the response.
What kind of documents work best for RAG chatbots?
Well-structured, authoritative documents work best, including documentation, FAQs, policies, runbooks, product specifications, and internal wikis. Consistent headings and clear sections usually improve retrieval quality.
How do I reduce hallucinations in a RAG chatbot?
Improve retrieval quality (chunking, filters, reranking), and add prompt constraints that force the model to rely on provided context. It also helps to add “I don’t know” behavior and return citations or references to the retrieved chunks.
Do I need a vector database to build a RAG chatbot?
You need some kind of vector index for semantic retrieval, but it doesn’t have to be a standalone vector database. The requirement is to store embeddings and perform similarity searches with optional metadata filtering.
How do I handle follow-up questions in a RAG chatbot?
You can use conversational memory or rewrite follow-up questions into standalone queries before retrieval. The goal is to make retrieval context-aware without unnecessarily stuffing the entire conversation into the prompt.
Felipe Matheus
Software Engineer
Adina Ong
Senior Engineering Manager
Clifford Fajardo
Senior Software Engineer
Thomas Chang
Software Engineer
