DeepLearning.AI

Sharon Zhou

Amit Sangani

In this course, you will learn to improve the accuracy of LLM applications by understanding development steps from evaluation to fine-tuning. You will explore memory tuning to enhance model performance and use the Llama 3-8b model to build an LLM application that converts text to SQL. The course covers prompt engineering, self-reflection, and fine-tuning techniques like LoRA and memory tuning to reduce hallucinations.

Improving Accuracy of LLM Applications

An introductory video to set the stage for the course content.

Introduction

A comprehensive overview with code examples to understand the course objectives.

Overview

Learn to build an SQL agent with practical code examples.

Create an SQL Agent

Develop an evaluation framework to systematically measure model performance.

Create an Evaluation

Explore fine-tuning techniques and memory tuning to enhance model accuracy.

Finetuning, PEFT, & Memory Tuning

Learn to generate training data and apply fine-tuning techniques to improve model performance.

Generate Data & Finetune

A brief conclusion to summarize the course learnings.

Conclusion

A short quiz to test your understanding of the course material.

Quiz

Join this short course to systematically enhance the accuracy and reliability of your LLM applications. Learn from industry experts and build an SQL agent, apply fine-tuning techniques, and improve model performance.

Developers looking to enhance the accuracy and reliability of their LLM applications.

Developers

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Data Scientists

AI enthusiasts with intermediate Python knowledge seeking to improve LLM application performance.

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Improving Accuracy of LLM Applications

Overview

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