It's a decent introduction. Could benefit from more diverse examples and a slightly better flow between modules.
Building Generative AI Applications with Python
Learn to build and deploy intelligent applications, chatbots, and language tools using Python, LangChain, and modern generative AI integration techniques.
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About this course
Generative artificial intelligence is transforming how we interact with technology, opening up incredible opportunities for software developers. If you want to move beyond using AI tools and start building your own intelligent applications, mastering the integration of large language models is the essential next step.
This text-based course guides you through the foundational concepts and practical steps of developing AI-powered software. You will learn how to connect Python applications to advanced language models, structure prompts effectively, and create functional user interfaces for your AI tools.
What you'll learn:
- Understand the core concepts of large language models and generative AI architecture
- Apply prompt engineering techniques to guide model behavior and improve output quality
- Build interactive conversational interfaces using Gradio and Flask
- Configure application workflows with LangChain to chain multiple LLM tasks together
- Implement Retrieval-Augmented Generation (RAG) patterns to connect models to custom data sources
- Manage API integrations securely and handle model responses in Python
The course begins with key terminology and foundational AI concepts before guiding you through written code implementations and practical application architecture. You will progress from simple text generation to building structured chatbots, summarizers, and translation tools through detailed explanations and code analysis.
This course is designed for beginner to intermediate Python developers who want to enter the world of AI development. No prior machine learning or AI experience is required.
Start reading today to begin building your own generative AI applications.
Course contents
6 modules · 28 lessons · 2h 59m
1
Foundations of Generative AI
Understand the basic terminology and set up a secure development environment.
- Demystifying LLMs and GenAI
Define key terminology and the difference between traditional AI and Generative AI.6 min - Understanding LLM Architecture
Explain how transformers and tokens work at a high level for developers.7 min - Setting Up Your Python AI Environment
Install necessary libraries and prepare a virtual environment for AI development.5 min - Secure API Management
Use environment variables and .env files to handle API keys securely in Python.6 min
2
Prompt Engineering & Response Handling
Master the art of communicating with models and processing their output.
- The Anatomy of a Perfect Prompt
Learn the structural components of an effective prompt: instruction, context, and data.7 min - Zero-shot vs. Few-shot Prompting
Improve model performance by providing examples within the prompt.6 min - Guiding Behavior with System Prompts
Use system roles to define the persona and constraints of the AI model.7 min - Parsing Structured AI Responses
Extract and validate JSON or specific data formats from raw LLM text outputs.8 min - Project: Simple Text Generator
Build a script that generates creative content based on user input parameters.6 min
3
Building Interactive User Interfaces
Create web-based frontends for your AI tools using Gradio and Flask.
- Rapid Prototyping with Gradio
Build a functional UI for an AI model in just a few lines of Python code.6 min - Creating a Chatbot UI
Implement a stateful chat interface using Gradio's ChatInterface component.7 min - Integrating AI with Flask
Set up a standard web server to handle AI requests via a REST API.7 min - Handling Async AI Requests in Flask
Manage long-running model calls without freezing the web interface.6 min - Project: AI Translation Tool
Build a web app that translates text between languages using a custom UI.7 min
4
Orchestration with LangChain
Chain multiple tasks together to create complex AI workflows.
- Introduction to LangChain
Understand why we need an orchestration framework for LLM applications.5 min - Building Your First Chain
Connect a prompt template to an LLM using the LangChain Expression Language (LCEL).7 min - Dynamic Prompt Templates
Create reusable and parameterizable prompts for different application tasks.6 min - Project: Intelligent Document Summarizer
Use LangChain to process long texts and generate concise summaries.8 min - Adding Conversational Memory
Enable your application to remember previous interactions in a session.7 min
5
Retrieval-Augmented Generation (RAG)
Connect your AI models to external, custom data sources.
- The Concept of RAG
Explain how RAG solves the problem of model knowledge cutoffs and hallucinations.6 min - Embeddings and Vector Databases
Learn how to turn text into numbers and store them for semantic search.7 min - Loading and Splitting Documents
Prepare PDFs or text files for the RAG pipeline by breaking them into chunks.6 min - Building a 'Chat with Your Data' App
Implement a full RAG pipeline to answer questions based on a specific document.8 min - Evaluating RAG Quality
Techniques to ensure the retrieved context is relevant and the answer is accurate.6 min
6
Deployment and Best Practices
Finalize your applications with error handling and cost management.
- Error Handling and Rate Limits
Write resilient code that handles API timeouts and usage quotas gracefully.6 min - Monitoring Token Usage and Costs
Implement logging to track how much each AI request costs.5 min - AI Application Architecture Patterns
Review the best practices for structuring large-scale AI Python projects.6 min - Course Wrap-up and Next Steps
Review the journey and explore how to keep up with the evolving AI landscape.5 min
Final project
Personal AI Knowledge Assistant
Build a comprehensive web application that allows users to upload a document, summarizes it, and then provides a chatbot interface to ask questions about that document's content.
What you'll get
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Short & focused
2h 59m of practical content
Certificate of completion
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Certificate of Mastery
This certifies that
Name Surname
has successfully demonstrated mastery of
Building Generative AI Applications with Python
Skills demonstrated
✓
Behavioral pattern analysis
Foundational
1.2 hrs
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Decision-architecture frameworks
Proficient
1.4 hrs
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A/B test design
Proficient
1.7 hrs
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Building Generative AI Applications with Python
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Performance detail
Coursework summary
Lessons completed
14 / 14
Practice questions
26 / 28
Assignments submitted
4 (avg 4.5 / 5)
Capstone project
Reviewed — 4.6 / 5
Total practice
6.2 hrs
Performance benchmark
Cohort rank
Top 12% of 1,625
Time to completion
11 days (median: 22)
Mastery score
91 / 100
Practice-question score
94%
Skill verification
Verified Skill Path
Reviews (10)
Fantastic course! The material was presented in a very digestible way, and the real-world applications made it super valuable. Highly recommend this one.
Decent material and presentation. The flow was mostly intuitive, and the applicability is there. Could be improved with more varied exercises.
Found it useful. The flow was logical, and the illustrative examples helped solidify the ideas. Could have used a bit more depth.
This course exceeded my expectations. The real-world applications discussed are incredibly useful. Great job!
Really enjoyed the flow of this. The practical applications discussed were spot on. Great course!
Hmm, I'm not sure this is for absolute beginners. It assumes a bit of prior knowledge that wasn't explicitly taught. Some examples were confusing.
Fantastic learning experience. The structure was logical, and the instructor's energy kept me hooked. Definitely got great value.
Good introduction. I appreciated the clear steps, although some of the later modules could have used more examples.
Learned a good amount here. The examples were relevant, though I wished there were a few more practical application tasks. Still, a worthwhile experience.
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