Build End-to-End GenAI Project: AI Travel Agent with Python
Build End-to-End GenAI Project: AI Travel Agent with Python
Published 10/2025
Duration: 1h 23m | .MP4 1280x720 30 fps(r) | AAC, 44100 Hz, 2ch | 666.45 MB
Genre: eLearning | Language: English
Published 10/2025
Duration: 1h 23m | .MP4 1280x720 30 fps(r) | AAC, 44100 Hz, 2ch | 666.45 MB
Genre: eLearning | Language: English
Master GenAI by building an AI Travel Agent - from data ingestion to RAG pipeline and deployment, all in one course.
What you'll learn
- Build a complete end-to-end GenAI application from scratch using Python.
- Understand and implement document ingestion, text chunking, and embeddings generation
- mplement Retrieval-Augmented Generation (RAG) pipelines using OpenAI or Hugging Face models.
- Learn how to manage environment variables, configurations, and structure production-ready GenAI projects.
Requirements
- A computer with internet access and permission to install Python packages.
- Curiosity to build and deploy AI-powered applications from scratch.
Description
Do you want to build and deploy a real-world GenAI application from scratch?In this hands-on course, you’ll learn how to create your very ownAI Travel Agent- an intelligent assistant that can read PDF guides, store them as embeddings, and answer user queries usingRetrieval-Augmented Generation (RAG)techniques.
This course walks you through every stage of development, starting from project setup, building theStreamlit frontend, developing aFastAPI backend, connecting to avector database (Qdrant), and integratingOpenAI or Hugging Face LLMs. By the end, you’ll not only understand how modern GenAI apps work - you’ll have your own deployed AI assistant ready to use and extend.
What You’ll Build
A workingAI Travel Assistantthat can ingest PDFs and answer travel-related questions intelligently.
A clean and modularPython project structuresuitable for real-world deployments.
ARAG pipelinethat connects ingestion, embeddings, retrieval, and LLM generation seamlessly.
Fully deployed frontend and backend on cloud platforms such as Railway and Streamlit Cloud.
What You’ll Learn
How to set up and structure GenAI projects like a pro.
Building beautiful Streamlit UIs with file upload and query blocks.
Creating backend APIs using FastAPI with /upload and /ask endpoints.
Understanding document ingestion, embeddings, and vector databases.
Connecting to Qdrant to store and retrieve embeddings efficiently.
Implementing RAG techniques to combine retrieval and generation for smarter answers.
Integrating OpenAI and Hugging Face models with proper key management.
Deploying your application end-to-end to the cloud.
By the end of this course, you’ll havehands-on experience with the entire GenAI development lifecycle- from idea to a fully deployed product
Who this course is for:
- Students and beginners curious about AI applications, even with limited prior AI experience
- Aspiring GenAI Engineers looking to learn retrieval-augmented generation (RAG), embeddings, and vector databases through practical implementation.
- Developers and Python programmers who want to get hands-on with GenAI by building a real-world project.
- Indie hackers, startup founders, or no-code builders who want to create and deploy their own AI assistant or product idea.
More Info