Prompt Engineering
Prompt engineering is a research field focused on designing effective prompts to maximize the performance of Large Language Models in practical applications. This discipline, which arose from the need to effectively guide LLMs, encompasses a wide spectrum of methods, from creating hand-crafted, human-readable prompts to developing automatically generated ones.
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References
Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing
Reference of Foundations of Large Language Models Course
Reference of Foundations of Large Language Models Course
Reference of Foundations of Large Language Models Course
Reference of Foundations of Large Language Models Course
Reference of Foundations of Large Language Models Course
Reference of Foundations of Large Language Models Course
Reference of Foundations of Large Language Models Course
Reference of Foundations of Large Language Models Course
Reference of Foundations of Large Language Models Course
Reference of Foundations of Large Language Models Course
Reference of Foundations of Large Language Models Course
Tags
Data Science
Ch.2 Generative Models - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Ch.3 Prompting - Foundations of Large Language Models
Ch.4 Alignment - Foundations of Large Language Models
Related
Prompt Engineering
Example of a Prompt for Machine Translation
A user wants a language model to generate a summary of a long article, but specifically for a non-expert audience. They provide the model with the following input: 'Summarize the attached article in simple, easy-to-understand language, as if you were explaining it to a middle school student.' What is the primary function of this input technique?
Analyzing Model Inputs
The Purpose of Prompting
Instruction Following as a Prerequisite for Prompting
Improving AI Application Performance
A marketing team is using a powerful, pre-trained, general-purpose language model to generate creative ad copy for a new product line. The team has no access to the model's underlying code and cannot retrain it. To ensure the project's success and produce high-quality, relevant content, which of the following skills is most crucial for the team to develop?
Prompt Engineering
Analyzing Prompt Effectiveness
Learn After
Prompt Shape Types
Iterative Refinement of Prompts
Automated Prompt Design
Variability of Prompts Across LLMs
Prompt Template
Empirical Nature of Prompt Design
Challenges of Manual Prompt Design
Complex Structure of Prompts
Problems with Natural Language Prompts
Discrete Prompts (Hard Prompts)
Continuous Prompts (Soft Prompts)
Simplifying Prompt Text for Efficiency
Fundamental Questions in Prompt Engineering
Evolution and Impact of Prompting in NLP
Efficient Prompting
Dependency of Prompting Effectiveness on LLM Capabilities
Creating Prompt Templates for Existing NLP Tasks
Using Naturally Occurring Internet Data for Fine-Tuning
Unrestricted Nature of LLM Prompts
Prompt Design as a Core Component of Prompt Engineering
Categorization of Prompting Techniques
A user wants a large language model to write a short, professional biography for a software engineer. The user's initial input is: 'Write about Alex Doe.' The model's output is generic and unhelpful. Which of the following revised inputs best demonstrates an effective technique for guiding the model to produce the desired output?
Improving LLM Consistency in a Team Setting
Major Design Considerations for Prompting
Developing Prompt Engineering Skills Through Practice
External Resources for Learning Prompt Engineering
A development team is using a pre-trained large language model to build a chatbot for customer support. They observe that the model's responses, while fluent, do not consistently adhere to the company's specific tone and policy guidelines. To address this, the team begins a process of methodically crafting and testing various instructions and examples as inputs to guide the model's output, without altering the model's internal weights. This process involves numerous cycles of adjusting the input text to achieve the desired response quality. Which discipline best describes the team's primary activity?