---
id: introduction
title: Introduction to Chatbot Evaluation
sidebar_label: Introduction
---

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Learn how to build and evaluate a reliable **LLM-powered medical chatbot** using **OpenAI**, **LangChain**, **Qdrant**, and **DeepEval**—from development to deployment.

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:::note
If you are working with **multi-turn chatbots**, this tutorial will be helpful to you. We will go through the entire process of building a reliable _multi-turn chatbot_ and how to evaluate it using `deepeval`
:::

## Get Started

Jump ahead to any of the sections in the tutorial, or keep reading to go with the flow.

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        "LangChain for orchestration",
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      title: "Evaluate multi-turn conversations",
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        "Use datasets to setup LLM evals pipeline",
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## What Will You Be Evaluating?

In this tutorial, you'll learn to evaluate and test a **medical chatbot** using DeepEval on its ability to:

- Diagnose symptoms, and
- Book appointments

It's a **multi-turn conversational agent**—meaning it can remember previous messages, handle follow-up questions, and take action based on the full conversation. Here's a nice looking UI to give you a better idea of what your chatbot could look like in the real world:

![Medical Chatbot Overview](https://deepeval-docs.s3.us-east-1.amazonaws.com/tutorials:medical-chatbot:chatbot-overview.png)

In the next section, we'll begin by going through the chatbot implementation, built with OpenAI, Qdrant, and LangChain.

:::tip
You can also skip straight to the [Evaluation section](/tutorials/medical-chatbot/tutorial-medical-chatbot-evaluation) instead.
:::