It's actually taking a long time just to write my prompts

Sure AI has saved me several hours by automating my tasks, but now I'm spending a tonne of time trying to optimize those prompts: can't AI do that for me too?


DSPy Prompt Optimization

You can use AI to optimize your AI prompts without having to manually find the right combination of words...More


Mike Taylor

Built a 50-person growth agency.
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Free access for email subscribers.
Python experience recommended.
1. Scenario
GoolyBib Headquarters - Strategy Discussion Post-Lunch
At GoolyBib HQ, Gustav Gieger, our CEO, introduces DSPy Prompt Optimization for smarter baby bibs. He explains prompt engineering essentials, system design, and optimization strategies like chain of thought and co-pro, emphasizing the complexity yet vast potential for enhancing our prompts. Inspired, we're ready to apply these techniques.
Gustav Gieger
at GoolyBib

Prompt engineering is crucial for our success in creating smart bibs for babies. Even with AI, we need to carefully design our prompts using DSP Y. It's a complex process, but it can significantly improve the performance of our prompts. Let's dive in and unleash the potential of DSPy Prompt Optimization!

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

DSPy Prompt Optimization: Improving AI Prompt Engineering

Prompt engineering plays a crucial role in training AI models to generate accurate and relevant responses. The recent development of DSPy (DeepSpeed Prompt) Optimization offers a framework that automates the process of prompt engineering, making it more efficient and effective. In this blog post, we will delve into the details of DSPy and explore its potential in optimizing prompts for AI models.

The transcript provided introduces us to DSPy and highlights its significance in the AI community. It emphasizes that even when AI is used to generate prompts, it still requires prompt engineering to design the inputs, outputs, and evaluation metrics. DSPy simplifies this process and enables prompt optimization through various strategies such as chain of thought and retrieval.

To begin using DSPy, the first step is to define the task. It is recommended to initially experiment with the OpenAI API to familiarize oneself with the complexity of the framework. For example, one can attempt to create a model that generates funny jokes on a specific topic. By setting up the task and configuring DSPy, it becomes possible to generate initial results.

The next crucial aspect is defining the prompt signature. DSPy offers flexibility in defining the inputs and outputs of the prompt. This can be done in a prediction signature format, specifying the topic as an input and the joke as an output. Alternatively, a class-based signature can be created, allowing for more advanced functionalities. This signature serves as the foundation for prompt optimization.

DSPy provides modules for prompting strategies, and the choice depends on the specific requirements. For instance, chain of thought involves asking the AI to provide rationale before generating the final result. Another strategy, retrieval (RAG), incorporates context from a search or external tools to enhance the prompt. These strategies can be combined and customized to create a tailored prompt pipeline.

Once the prompt pipeline is established, it is essential to evaluate the performance of the prompts. DSPy offers multiple evaluation metrics, including programmatic, human annotation, and synthetic evaluation. The choice of metric depends on the nature of the task and the availability of resources. Synthetic evaluation, where AI assesses the quality of the generated content, proves particularly useful when prompt optimization involves creative tasks like joke generation.

To train the model and optimize the prompts, it is necessary to gather data. DSPy provides various options, including manual entry of examples or using AI to extract examples from external sources. Synthetic examples can be generated by instructing AI to mimic the desired prompt format

3. Tutorial

  Hey, I'm going to walk you through DSP Y which is a prompt optimization. Framework or library. And it's pretty cool, actually, a very complicated. And a little bit over-engineered, but I did get pretty good results with it. Now th the reason why I wanted to cover DSP, why is that? Some engineers in the AI community have been saying about how. It is the end of prompt engineering and I, my, my pistol opinion. Is that. Even if you use AI to write the prompts, which is what DSP Y is doing that is still prompt engineering. Because you're actually engineering their entire system around the prompt. And that's the important thing, like how does the, what are the inputs?

4. Exercises
5. Certificate

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