The Blueprint for Trustworthy AI: Constructing Accurate Chatbots with Sophisticated Data Pipelines

AI Custom Chatbot with an Advanced Data Pipeline

To get started leveling up your custom AI chatbot, check out our GitHub repository here.

This tutorial shows you how to level-up your data pipeline when building a custom AI chatbot.

We recommend setting up a basic custom chatbot first. This tutorial will build on top of the quick start you can reference here.

Building AI chatbot apps is easy. Customizing is hard.

We are using our preferred tech stack.

• OpenAI
• Langchain
• Llama Hub Data Loaders
• Pinecone Vector Database
• Python (FastAPI web framework)

We are leveraging Retrieval Augmented Generation (RAG).

The goal of this tutorial is to help level-up your customized chatbot beyond the basics.

• Increase accuracy
• Grow dataset by integrating more disparate data sources
• Earning user trust

Technical Objectives

• Increase accuracy by implementing multiple data cleansing techniques
• Integrate Notion
• Earn user trust by providing the link to the original data source

Here’s a peak at the components of the AI custom chatbot.

For more resources: check out our other blog posts on AI.

Comments

[Austin Vance]

Where is it more important to tune - at the system message level or on the data input level?