Learn Before
Complex Structure of Prompts
Contrary to being viewed as simple sequences of tokens, prompts possess a complex internal structure. This structure is typically composed of distinct fields, such as the user's input, a guiding instruction, and illustrative demonstrations, which all contribute to the prompt's overall function.
0
1
Tags
Ch.3 Prompting - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Related
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?
Learn After
Deconstructing a Model's Input
Analyze the following prompt provided to a language model. What is the primary function of the section containing 'Review: "The plot was predictable and the acting was wooden." / Sentiment: Negative' within the overall structure?
Prompt Start
Instruction: Classify the sentiment of the following movie review as 'Positive', 'Negative', or 'Neutral'.
Review: "The plot was predictable and the acting was wooden." Sentiment: Negative
Review: "An absolute masterpiece of modern cinema. I was captivated from start to finish." Sentiment: Positive
Review: "The movie was two hours long and was filmed in color." Sentiment:
Prompt End
A developer needs a language model to summarize lengthy technical reports into a single, easy-to-understand sentence. Consider two approaches for structuring the prompt:
Approach 1:
[Instruction: Summarize the following report in one sentence.] + [The full technical report]Approach 2:
[Instruction: Summarize the following report in one sentence for a non-technical audience.] + [Example Report 1] + [Example Summary 1] + [Example Report 2] + [Example Summary 2] + [The full technical report to be summarized]Which approach is more likely to yield consistently better results, and what is the most accurate reason?
A well-structured input for a language model often contains several distinct components that work together. Match each component with its primary function.