Building Generative Ai Apps Llama Syllabus
Full curriculum breakdown — modules, lessons, estimated time, and outcomes.
Overview (80-120 words) describing structure and time commitment.
Module 1: Introduction to Generative AI and the Llama Framework
Estimated time: 10 hours
- Understanding generative AI concepts and applications
- Overview of the Llama framework and its capabilities
- Setting up the development environment
- Exploring pre-trained Llama models
Module 2: Core Concepts in Building AI Applications
Estimated time: 12 hours
- Working with prompts and text generation
- Understanding model inputs and outputs
- Customizing model behavior through parameters
- Evaluating generated content quality
Module 3: Hands-On Development with Llama
Estimated time: 15 hours
- Building a basic text generation application
- Integrating Llama into a web interface
- Managing model inference and API usage
- Debugging common implementation issues
Module 4: Enhancing Application Functionality
Estimated time: 14 hours
- Adding user input handling and interaction features
- Implementing context memory and conversation flow
- Improving response relevance and coherence
- Optimizing performance for real-time use
Module 5: Deployment and Real-World Considerations
Estimated time: 16 hours
- Preparing applications for deployment
- Understanding ethical implications of generative AI
- Addressing bias and safety in model outputs
- Monitoring and maintaining AI applications
Module 6: Final Project
Estimated time: 20 hours
- Design and build a complete generative AI application using Llama
- Document development process and decision-making
- Present functionality, limitations, and potential improvements
Prerequisites
- Basic understanding of Python programming
- Familiarity with command-line interfaces
- Access to a computer with internet connection
What You'll Be Able to Do After
- Build and deploy functional generative AI applications using Llama
- Customize model outputs through prompt engineering and parameter tuning
- Integrate Llama-based models into interactive user interfaces
- Evaluate and improve the quality of generated content
- Apply ethical best practices in generative AI development