Training a prompt for performance

You don't need to fine-tune models to train them, you can provide examples in the prompt.

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In-Context Learning

Providing examples of the task being done well is the most cost-effective way to train LLMs to generate better performing responses...More

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Mike Taylor

Built a 50-person growth agency.
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Python experience recommended.
1. Scenario
GOOLYBIB HEADQUARTERS – AI STRATEGY DISCUSSION
Alright, team, we've got an important AI strategy discussion today at GoolyBib headquarters. We'll be talking about in-context learning and how it can improve the performance of our language models. Picture this: you're training a prompt for performance, and you realize that adding examples to the prompts can make a significant difference. Even just one example can have a big impact. So, we need to learn how to use examples effectively, evaluate the effectiveness of different prompts, and find the optimal balance between performance and computational cost in our real-world AI systems. So, buckle up and get ready to dive into the world of in-context learning. We're going to explore how examples can level up our language models and take our smart bibs to the next level.
Gustav Gieger
at GoolyBib

Alright, team, we've got an important AI strategy discussion today at GoolyBib headquarters. We'll be talking about in-context learning and how it can improve the performance of our language models. \nPicture this: you're training a prompt for performance, and you realize that adding examples to the prompts can make a significant difference. Even just one example can have a big impact. So, we need to learn how to use examples effectively, evaluate the effectiveness of different prompts, and find the optimal balance between performance and computational cost in our real-world AI systems.\nSo, buckle up and get ready to dive into the world of in-context learning. We're going to explore how examples can level up our language models and take our smart bibs to the next level. 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

In-Context Learning: Enhancing Language Models with Examples

Language models have come a long way in recent years, thanks to advancements in artificial intelligence and machine learning. One of the key breakthroughs in this field is the concept of in-context learning, which involves adding examples into the prompt to enhance the model's performance. In this blog post, we will explore the significance of in-context learning and how it can improve the capabilities of language models.

To understand in-context learning, let's first debunk the myth that it is a complicated process. In reality, it simply involves adding examples into the prompt, which allows the model to better understand and follow those examples when performing a given task. This seemingly small addition can have a significant impact on the model's performance.

One famous study that introduced in-context learning is the GPT-3 (Generative Pre-trained Transformer 3) language model. By adding examples into the prompt, researchers found that the model was not only better at following those examples but also improved its performance over time. This demonstrates the value of prompt engineering and the importance of incorporating examples into the learning process.

The transcript highlights the effectiveness of in-context learning by showcasing a product name generator. By providing just one example in the prompt, the model's accuracy increased from 10% to almost 50%. This demonstrates the immediate impact of adding examples and how even a single example can make a big difference.

Moreover, the transcript emphasizes the flexibility and ease of incorporating examples into the prompt. By using an array of examples, the model can learn from various instances without the need to write them all out in the prompt. This approach not only streamlines the process but also enables easy swapping of examples when needed.

The transcript also showcases the programmatic evaluation of the model's performance. By checking if the generated product names start with the letter "I," the evaluation metric provides a quantitative measure of the model's adherence to the given examples. This demonstrates how in-context learning can be evaluated and refined to meet specific requirements.

To further explore the impact of examples on the model's performance, the transcript introduces the concept of AB testing. By comparing different prompts with varying numbers of examples, the model's ability to consistently generate product names starting with "I" is evaluated. This iterative testing enables researchers to determine the minimum number of examples required for reliable performance.

The use of asynchronous testing is another highlight of the transcript. By running multiple prompts simultaneously, the evaluation process becomes more efficient and time-effective. This approach

3. Tutorial

  In context learning. Don't worry. It's not that complicated as it sounds. It's just adding examples into the prompt and this is the killer. Application of LLMs. If you get examples in the prompt, it is much better at following those examples and actually doing a good job of the task that you're asking it to do.

InContextLearning.ipynb
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4. Exercises
5. Certificate

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