When utilizing large language models, prompt engineering emerges as one of the most critical topics to learn. It is possible to achieve the same level of accuracy by crafting the right prompt, even when opting for less powerful or open-source models. During the course, we will explore the art of writing the perfect prompt, equipping you with the skills to maximize the potential of your models and get the response in the specified format. Furthermore, few-shot prompts provide a unique opportunity to enable the models to quickly acquire new knowledge and generalize to unseen tasks with minimal data, offering a remarkable capability for customization. The rest of this module is organized as follows.
- Intro to Prompt Engineering: Tips and Tricks: Explore a range of valuable tips and tricks for effective prompting. We will cover techniques such as role prompting, which involves specifying a specific role for the model, such as assistant or copywriter. Additionally, we will delve into few-shot prompting, teaching the model how to respond based on limited examples. Lastly, we will examine the chain of thought approach, which aids in enhancing reasoning capabilities. Throughout the lesson, we will provide numerous examples of successful and ineffective prompts, equipping you with the skills to master the art of crafting optimal prompts for various scenarios.
- Using Prompt Templates: Dynamic prompting provides a flexible approach to improve the model’s context. This lesson offers a comprehensive explanation of few-shot learning, providing compelling examples and allowing the library to select suitable samples based on the input window length of the model.
- Getting the Best of Few Shot Prompts and Example Selectors: Discussing the advantages and disadvantages of few-shot learning, including the enhanced output quality achievable by defining tasks through examples. However, we will also delve into potential drawbacks, such as increased token usage and subpar results when utilizing poorly chosen examples. Furthermore, we will demonstrate how to use example selectors effectively and provide insights into when and why they should be employed.
- Managing Outputs with Output Parsers: Parsing the output is a crucial aspect of interacting with language models. Output parsers offer the flexibility to select from pre-defined types or create a custom data schema, enabling precise control over the output format. Additionally, output fixer classes are crucial in identifying and correcting misformatted responses, ensuring consistent and error-free outputs. These powerful tools are indispensable in a production environment, guaranteeing the reliability and consistency of application outputs.
- Improving Our News Articles Summarizer: This project will leverage the code from the previous module as a foundation and integrate the new concepts introduced in previous lessons. Still, the project's primary objective is to generate summaries of news articles. The process begins by retrieving the content from a given URL. Then, a few-shot prompt template is employed to specify the desired output style. Finally, the output parser transforms the model's string response into a list format, facilitating convenient utilization.
- Creating Knowledge Graphs from Textual Data: Unveiling Hidden Connections: The second project in this module utilizes the text understanding capability of language models to generate knowledge graphs. We can effortlessly extract triple relations from text and format them accordingly using predefined variables within the LangChain library. Furthermore, the option to visualize the knowledge graph enhances comprehension and facilitates easier understanding.
This module's remaining part will primarily focus on formatting the model's input and responses by utilizing prompt templates and output parsers, respectively. These tools play a pivotal role in scenarios where we lack access to the models or are unable to enhance them through fine-tuning. Moreover, we can employ the in-context learning approach to further customize models according to the requirements of our specific application.