Improving Accuracy of LLM Applications from Coursera

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 embeds facts in model weights to reduce hallucinations.

In this course, you’ll use Llama’s family of open-source models.

What you’ll do:

1. Build a text to SQL agent and simulate situations where it hallucinates to begin the evaluation process.

2. Build an evaluation framework to systematically measure performance, including criteria for good evaluations, best practices, and how to develop an evaluation score.

3. Learn how instruction fine-tuning enhances pre-trained LLMs to follow instructions, and how memory fine-tuning embeds facts to reduce hallucinations.

4. 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.

5. 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!

What's inside

Syllabus

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Features instruction fine-tuning, which enhances pre-trained LLMs to follow instructions, and memory fine-tuning, which embeds facts to reduce hallucinations

Explores Performance-Efficient Fine-tuning (PEFT) techniques like Low-Rank Adaptation (LoRA) and Mixture of Memory Experts (MoME) to reduce training time

Uses Llama's family of open-source models, which are widely used in the field and offer flexibility for experimentation and customization

Taught by Sharon Zhou, Co-founder & CEO of Lamini, and Amit Sangani, Senior Director of Partner Engineering, Meta, who bring industry expertise

Requires building a text-to-SQL agent, which may require familiarity with SQL databases and natural language processing techniques

Focuses on improving accuracy of LLM applications, which is a constantly evolving field, so learners should stay updated on the latest research

Reviews summary

Improving llm accuracy techniques

According to students, this course offers a systematic approach to enhancing the accuracy of LLM applications. Learners found the coverage of Performance-Efficient Fine-tuning (PEFT) techniques like LoRA and Memory Tuning particularly valuable, noting they provided practical methods to reduce hallucinations and improve model reliability. The course also received positive remarks for teaching how to build an evaluation framework and the iterative process of fine-tuning. While no major criticisms were consistently reported in the filtered reviews, the emphasis is clearly on delivering actionable techniques for developers.

Practical exercises like building an SQL agent.

"Building the text-to-SQL agent was a great hands-on way to apply the concepts."

"The iterative fine-tuning process shown was a practical demonstration of model improvement."

"Applying prompt engineering and self-reflection methods felt very concrete and helpful."

Build a framework to measure and improve performance.

"The module on building an evaluation framework provided a clear, systematic way to test LLMs."

"Understanding the criteria and best practices for evaluation was a major takeaway."

"The process outlined for developing an evaluation score is highly useful."

Learn efficient methods like LoRA and memory tuning.

"The sections on LoRA and memory tuning were incredibly insightful and practical."

"Learning about PEFT techniques like LoRA felt directly applicable to improving my models."

"I found the explanation and use of memory tuning to embed facts very effective."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Improving Accuracy of LLM Applications with these activities:

Review SQL Fundamentals

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Reviewing SQL fundamentals will help you better understand how to build and evaluate the text-to-SQL agent covered in the course.

Browse courses on SQL

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Review basic SQL syntax and commands.
Practice writing SQL queries for data retrieval and manipulation.
Familiarize yourself with different SQL database systems.

Read 'Natural Language Processing with Python'

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Reading this book will provide a broader understanding of NLP concepts, which can be helpful for prompt engineering and evaluation.

View Natural Language Processing with Python:... on Amazon

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Read the chapters related to text processing and analysis.
Experiment with the Python code examples provided in the book.
Relate the concepts learned to the course material on LLMs.

Prompt Engineering Exercises

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Practicing prompt engineering will improve your ability to elicit accurate and reliable responses from LLMs.

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Choose a specific task or domain.
Generate a variety of prompts for the task.
Evaluate the responses from an LLM and refine your prompts.
Repeat the process to improve your prompt engineering skills.

Four other activities

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Write a Blog Post on LLM Hallucinations

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Writing a blog post will help you solidify your understanding of LLM hallucinations and how to mitigate them.

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Research the causes and consequences of LLM hallucinations.
Summarize the techniques for reducing hallucinations covered in the course.
Provide examples of LLM hallucinations and how to address them.
Publish your blog post on a relevant platform.

Build a Simple Question Answering System

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Building a question answering system will allow you to apply the concepts learned in the course to a practical problem.

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Choose a dataset of questions and answers.
Implement a basic question answering system using an LLM.
Evaluate the performance of your system using appropriate metrics.
Experiment with different prompt engineering techniques to improve accuracy.

Read 'Deep Learning' by Goodfellow et al.

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Reading this book will provide a deeper understanding of the theoretical foundations of deep learning, which can be helpful for fine-tuning LLMs.

View Deep Learning on Amazon

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Read the chapters related to neural networks and deep learning architectures.
Focus on the sections related to fine-tuning and transfer learning.
Relate the concepts learned to the course material on LoRA and MoME.

Contribute to an Open-Source LLM Project

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Contributing to an open-source project will provide hands-on experience with LLMs and allow you to learn from other developers.

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Find an open-source LLM project on GitHub or a similar platform.
Identify a bug or feature that you can contribute to.
Submit a pull request with your changes.
Participate in code reviews and discussions with other contributors.

Career center

Learners who complete Improving Accuracy of LLM Applications will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

A Machine Learning Engineer builds, trains, and deploys machine learning models. This course directly aligns with a machine learning engineer's need to understand how to improve model accuracy and reliability for large language model applications. The course provides experience with techniques like fine tuning, prompt engineering, and evaluation frameworks, all of which are essential in ensuring a model's performance. This course is especially helpful because it focuses on reducing hallucinations and enhancing the performance of a LLM, a key challenge in building realistic ML applications. It also demonstrates how an iterative approach to fine tuning helps achieve increased accuracy, which is essential to real-world ML projects.

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Artificial Intelligence Engineer

An Artificial Intelligence Engineer develops artificial intelligence systems, often involving large language models. This course is highly useful for an artificial intelligence engineer, as it addresses the critical aspect of improving the accuracy and reliability of LLM applications. It teaches hands-on techniques to build evaluation frameworks, understand prompt engineering, fine-tune models and use memory tuning methods, and is therefore essential for an AI engineer to learn. The course's focus on systematic improvement and performance evaluation helps an artificial intelligence engineer overcome problems of inconsistent output. In particular, learning to reduce hallucinations and improve model performance helps build better AI applications.

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Natural Language Processing Engineer

A Natural Language Processing Engineer specializes in building systems that understand and process human language and a course like this is beneficial to their work. This involves a close understanding of how to improve LLM accuracy and reliability. This course's hands-on approach to building SQL agents, adding evaluation metrics, and fine-tuning models directly applies to NLP tasks. Learning about techniques to enhance LLMs through prompt engineering and self-reflection is useful for a Natural Language Processing Engineer. Additionally, the course's focus on reducing hallucinations and iterative fine-tuning processes helps create more accurate NLP solutions.

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Data Scientist

Data Scientists analyze complex data to extract insights and build models. A data scientist will find this course useful, particularly in understanding the nuances of large language models and how to evaluate their performance. The techniques taught in this course, such as building evaluation frameworks, instruction fine tuning, and using performance efficient fine tuning, are directly relevant for a data scientist seeking to apply LLMs to diverse data problems. The course's emphasis on iterative improvement and performance measurement provides a strong methodology for a data scientist to apply when building highly accurate models.

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Software Developer

A Software Developer builds and maintains software applications and systems. This course may be useful to a software developer, especially if they are working on projects that include LLM integration. This course provides hands-on experience in building SQL agents, evaluating LLM performance, and using fine-tuning techniques to enhance model accuracy, all of which are helpful for a software developer integrating LLMs into applications. The course's focus on the iterative process of fine-tuning and practical tips may help a software developer deal with issues in software which use large language models.

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AI Product Manager

An AI Product Manager is responsible for defining the strategy and roadmap for an AI product. An AI product manager may find this course useful, because it helps build a strong understanding of the technical requirements needed to improve accuracy in LLM applications. The course gives insight into critical processes like adding evaluation metrics to measure performance, using prompt engineering, and fine-tuning models. The insights gained can help an AI product manager create realistic, effective, and impactful product strategies and it helps them understand how the process of iterative model fine tuning can lead to a better product.

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AI Researcher

An AI Researcher pushes the boundaries of artificial intelligence through research and experimentation. This course helps an AI researcher by familiarizing them with hands-on techniques in enhancing the accuracy of LLMs. This course focuses on practical methods like building evaluation frameworks, prompt engineering, fine-tuning, and techniques to reduce hallucinations, all of which are useful for an AI researcher exploring new improvements to LLMs. The course's systematic approach to iterative model improvement, may help an AI researcher in their work.

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Data Engineer

A Data Engineer is responsible for designing, building, and maintaining the infrastructure that enables data analysis and machine learning. This course may be useful to a data engineer, especially if their work involves the deployment of LLMs. The course teaches how to use evaluation frameworks, and how to train and fine tune models. This will give a data engineer a better understanding of the practical issues in working with large language models, especially when it comes to improving the accuracy and reliability of the models they have to handle.

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Computational Linguist

A Computational Linguist uses computational methods to understand human language and may find this course helpful. This course discusses the use of LLMs for a variety of tasks and helps a computational linguist learn how to improve accuracy in LLM applications. The course's hands-on approach to building SQL agents, adding evaluation metrics, and using efficient fine-tuning techniques directly applies to their area of work. The specific methods in instruction fine-tuning and memory tuning may help improve the accuracy of models used in linguistic analysis.

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Research Scientist

A Research Scientist conducts research and experiments to advance knowledge in a specific field. This course may be useful for a research scientist whose work involves natural language processing or machine learning. The course's focus on improving model performance through techniques like prompt engineering, fine-tuning, and use of evaluation metrics, can help a research scientist looking to apply large language models in their work. The course's focus on iterative and practical methods to improve model accuracy in LLMs is also relevant to many forms of scientific research.

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Cloud Solutions Architect

A Cloud Solutions Architect designs and implements cloud-based solutions to business needs. They may find this course useful because it introduces them to the practical considerations of deploying and using LLMs, which increasingly are part of cloud-based solutions. This course covers building evaluation frameworks, fine tuning, and practical tips to improve the accuracy of a model running in the cloud. The course's focus on improving the reliability of LLMs when deployed to cloud infrastructure, may help a solutions architect deploy more effective solutions.

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Technical Consultant

A Technical Consultant provides expert advice on technology solutions to clients, and may find this course helpful. This course helps the consultant gain practical knowledge of the challenges and approaches to improving the accuracy of LLMs. The course's focus on techniques like building SQL agents, evaluating performance, and fine-tuning will be useful to a consultant who advises clients on the benefits of AI solutions. They can also use these skills to better guide and advise clients on how to use and deploy LLMs effectively.

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Business Intelligence Analyst

A Business Intelligence Analyst analyzes data and generates reports to help businesses make better decisions. A business intelligence analyst may find this course helpful if they wish to understand and use LLMs when analyzing business data. While this course focuses primarily on the technical aspects of improving accuracy, the analyst may find it useful to understand how to evaluate the quality of the output of LLMs. An understanding of the techniques taught, such as building evaluation metrics and fine tuning models may help an analyst when applying LLM tools.

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Database Administrator

A Database Administrator is responsible for the performance, integrity and security of a database and may find this course helpful. The course specifically teaches hands-on techniques using SQL agents designed to interact with databases. This course helps a database administrator learn how large language models interact with databases and some of the practical considerations to ensure data quality. The course's focus on using SQL agents, a method for connecting language models to databases, is relevant to their work.

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Project Manager

A Project Manager plans, executes, and oversees projects and may find this course helpful. A project manager who is overseeing LLM integration in a project may find it useful to understand the challenges and techniques involved in improving accuracy. This course's coverage of the systematical approach to enhancing applications may give a project manager a framework they can use when designing project plans. The course may help a project manager understand the iterative process of fine-tuning, to better estimate the time needed for a project.

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Improving Accuracy of LLM Applications

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