Improve accuracy of LLM using TAG & RAG
Enhance LLM Performance with Techniques of Augmented Generation and Retrieval-Augmented Generation
 
👉 Start learning today and gain a competitive edge in AI!
 
Course Overview
"Improve Accuracy of LLM using TAG and RAG" is a self-paced course designed to help you enhance the accuracy and relevance of Large Language Models (LLMs) by integrating advanced techniques like Techniques of Augmented Generation (TAG) and Retrieval-Augmented Generation (RAG).
As LLMs become increasingly important in business and AI applications, ensuring their accuracy and context-awareness remains a significant challenge. This course provides a structured approach to improving LLM outputs by incorporating supervised classification techniques and taxonomy-based domain knowledge into the model framework.
You will explore how to build custom embeddings, leverage vector databases, and apply structured taxonomies to address issues like outdated corpora and low-quality responses. The course includes practical exercises and capstone projects to give you hands-on experience in fine-tuning LLMs for real-world use cases.
Through a blend of theory and hands-on application, you’ll gain the skills to create more accurate and context-aware LLM-driven solutions, making your AI systems more reliable and responsive to user needs.
 
Key Learning Objectives
By the end of this course, you will be able to:
✅ Understand the core concepts of TAG and RAG – Learn the principles behind augmented generation and retrieval-augmented generation.
✅ Improve LLM performance using domain-specific knowledge – Integrate structured taxonomies and supervised classification techniques.
✅ Develop custom embeddings and vector databases – Enhance LLM retrieval accuracy using specialized data structures.
✅ Fine-tune LLMs for context-aware responses – Apply techniques to adjust model outputs based on real-world data.
✅ Solve challenges with outdated and incomplete corpora – Use retrieval-based methods to enhance information accuracy.
✅ Implement real-world solutions – Apply your knowledge to capstone projects focused on improving LLM accuracy in practical settings.
 
Who Should Attend
This course is ideal for:
✔️ AI developers and data scientists working with LLMs and AI models.
✔️ Business analysts and consultants implementing AI-driven solutions.
✔️ NLP and machine learning engineers seeking to improve model accuracy.
✔️ Product managers looking to enhance AI product performance.
✔️ AI strategists and business leaders aiming to improve customer experience through AI.
 
Prerequisites
To enroll in this course, you should have:
Basic computer literacy and an analytical mindset.
Completion of "Mastering Prompt Engineering using LLM" or an equivalent course is required.
Familiarity with ChatGPT or other LLMs is helpful but not mandatory.
 
Expected Outcome
Upon successful completion of the course, you will:
✅ Gain a deep understanding of TAG and RAG techniques and how they apply to LLMs.
✅ Develop and integrate custom embeddings and vector databases to improve model accuracy.
✅ Fine-tune LLM outputs using structured taxonomies and domain-specific knowledge.
✅ Solve real-world challenges related to model relevance and context awareness.
✅ Build and deploy LLM-driven solutions that deliver accurate, context-aware responses.
✅ Earn a recognized certification in LLM Accuracy Improvement from the Applied AI Institute (AAII).