Course Overview
The course "Improve Accuracy of LLM using TAG and RAG" delves into enhancing Large Language Model (LLM) accuracy by integrating Techniques of Augmented Generation (TAG) and Retrieval-Augmented Generation (RAG). Students will explore supervised classification techniques, utilizing taxonomy to incorporate domain-specific knowledge into LLMs. By combining custom embeddings, vector databases, and structured taxonomies, participants will address challenges like outdated corpora and improve LLM outputs. The course emphasizes practical applications, teaching students how to fine-tune LLMs for accurate, context-aware responses in real-world scenarios through hands-on exercises and capstone projects.
Prerequisites: Basic computer literacy, an analytical mindset, and a willingness to learn. Knowledge of ChatGPT is beneficial but not required. Successfully completion of our Mastering Prompt Engineering using LLM or equivalent course is a required pre-requisite.
Complete quizzes and assignments to earn certification.
Advance your AI skills today.
Learning through Videos
Videos provide you concepts and examples associated with Vision AI
No New Set-up needed
All the development environments will be provided by the Applied AI Institute in a secure Microsoft Azure cloud environment. In addition, all solution accelerators associated with various use cases will be provided by the Applied AI Institute
Knowledge Check and Extensive Class Exercises
As you learn a concept, test your understanding using Interactive Knowledge Checks or quizzes
You can also test how you apply all learnt concepts by exercising all your class assignment. Upon successful completion of class assignment students will receive a Credly certification of completion for adding into their profile.
Curriculum
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