Maximizing LLM & Image Model Performance: Advanced Evaluation Strategies

Discover cutting-edge techniques for elevating the accuracy and effectiveness of Large Language Models and Image Generation Models. Our guide delves into innovative evaluation metrics, providing insights to enhance model reliability and drive impactful results.


Evaluating Generative AI

This skill involves the proficient assessment of Generative Artificial Intelligence (AI) systems, such as Large Language Models (LLMs) and image generation models...More


James Anthony Phoenix

Data Engineer | Full Stack Developer
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Python experience recommended.
1. Scenario
You're in a strategy discussion and need to learn how to effectively evaluate your generative AI based applications more effectively.
Sam Smirnov
at LeftMedia

Alright team, we've got a lot on our plate today.

Our boss wants us to dive into the world of evaluating generative AI.

We're going to learn about different techniques for evaluating customer valuation metrics for AI models, including programmatic rule-based evaluation, LLM based evaluation, and human-based evaluation.

We'll also learn how to calculate accuracy for each method and get some tips for optimizing the evaluation process.

So grab your thinking caps and let's get started!

This course is a work of fiction. Unless otherwise indicated, all the names, characters, businesses, data, places, events and incidents in this course are either the product of the author's imagination or used in a fictitious manner. Any resemblance to actual persons, living or dead, or actual events is purely coincidental.

2. Brief

Generative AI has revolutionized various industries by enabling the creation of realistic and customized content. However, evaluating the performance and quality of generative AI models can be challenging. In this course, you’ll explore different evaluation techniques and their trade-offs to help you build effective customer valuation metrics for generative AI models.

The first evaluation technique we will discuss is programmatic rule-based evaluation. This approach involves creating custom rules to determine the quality of the generated output. For example, in the context of generating email content, you can evaluate the length of the email or check if it contains specific keywords. The advantage of this technique is its quick feedback loop and cost-effectiveness. However, it may not be suitable for tasks that require nuanced evaluation.

The second technique is LLM (Language Model)-based evaluation. Here, instead of relying on custom rules, another LLM is used to evaluate the generated output. This approach is useful for tasks that are too nuanced for rule-based evaluation, such as evaluating the helpfulness or readability of text. Multiple outputs are generated by the LLM, and another LLM is used to rate each output. While this technique provides more accurate evaluation, it is slower and more costly due to the need for human intervention.

The third technique is human-based evaluation, where human evaluators rate the generated output. This approach is ideal for high-stakes tasks where false positives or false negatives can have significant consequences. However, human-based evaluation is time-consuming, costly, and requires a labeling step. It provides the highest level of accuracy but may not be feasible for large-scale evaluations.

To illustrate these evaluation techniques, we provide code examples using different scenarios. In the programmatic rule-based evaluation example, we demonstrate how to set up evaluation metrics using Python libraries like pandas, numpy, and TQDM. We define custom rules, such as the minimum length of a social media blog post or the presence of specific keywords, and evaluate the generated output accordingly.

In the LLM-based evaluation example, we show how to use one LLM to generate multiple outputs and another LLM to rate each output. We discuss the importance of ground truth labels and how synthetic data generated by a powerful model can be used for evaluation.

In the human-based evaluation example, we generate images using OpenAI's GPT-3.5-Turbo and ask human evaluators to rate the quality of the images. We demonstrate an interactive approval system that allows evaluators to provide feedback on each output.

3. Tutorial

 Hey, welcome back. , and so basically in this video, we're going to have a really long gypsy new book talking about how you can build customer valuation metrics for alarms. , we're going to explore a different couple of options that you've got, and we'll look at some of the trade-offs as well. And there's a lot more texts that you can read inside this Jupiter notebook.

4. Exercises
5. Certificate

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