What Will You Learn?
Join our new short course, Improving Accuracy of LLM Applications with Lamini and Meta. Learn from Sharon Zhou, Co-founder & CEO of Lamini, and Amit Sangani, Senior Director of Partner Engineering, Meta.
Many developers have experienced frustration with inconsistent results when working with LLM applications. This course offers a systematic approach to enhance the accuracy and reliability of your LLM applications.
You will build an SQL agent, add evaluation metrics to measure performance, and use prompt engineering and self-reflection to make the model perform better. Finally, you will fine-tune the model with techniques like LoRA and memory tuning that embed facts in model weights to reduce hallucinations.
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About This Course
Provider: Coursera
Format: Online
Duration: 1 Hour to complete [Approx.]
Target Audience: Intermediate level
Learning Objectives: By the end of this course, you will build an SQL agent, add evaluation metrics to measure performance, and use prompt engineering and self-reflection to make the model perform better.
Assessment and Certification: NA
Instructor: Sharon Zhou and Amit Sangani.
Course Prerequisites: Basic Python coding experience and understanding of LLM.
Key Topics: In this course, you’ll learn about LLM Application, Performance Testing, Transfer Learning, Model Evaluation, Generative AI Agents, Test Data, Prompt Engineering, etc.
Topic Covered:
- Build a text-to-SQL agent and simulate situations where it hallucinates to begin the evaluation process.
- Build an evaluation framework to systematically measure performance, including criteria for good evaluations, best practices, and how to develop an evaluation score.
- Learn how instruction fine-tuning enhances pre-trained LLMs to follow instructions, and how memory fine-tuning embeds facts to reduce hallucinations.
- Break fine-tuning myths and see how Performance-Efficient Fine-tuning (PEFT) techniques like Low-Rank Adaptation(LoRA).
- Reduce training time by 100x, and Mixture of Memory Experts (MoME) reduces it even further.
- Go through an iterative process of generating training data and fine-tuning.
- learning practical tips such as adding examples, generating variations, and filtering generated data to increase model accuracy.
- Start improving the accuracy of LLM applications today!
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