Prompt Engineering (novice)

Prompt Engineering is a crucial field in artificial intelligence where the focus is on designing effective inputs (prompts) that guide AI models to produce desired outcomes. It plays a vital role in enhancing the performance of language models and generative AI systems across various applications, from chatbots to content creation. By carefully crafting prompts, developers can improve the accuracy, relevance, and efficiency of AI responses. This practice not only optimises interactions with AI but also addresses challenges related to ambiguity, bias, and system limitations, ensuring that AI systems respond in a contextually appropriate and resource-efficient manner.



Dr White, the creator of a massively popular Prompt Engineering course on Coursera, guides us through the vast potential of Generative AI – from personalised learning to cybersecurity.
prompt engineering

Basic prompt engineering involves straightforward methods to interact with AI models, aiming for clear and direct responses. Key features include:

  • Direct Queries: Simple and clear prompts that directly ask for the information or task needed.
  • Single-step Instructions: Instructions that require the AI to perform one straightforward action or answer.
  • Keyword Emphasis: Using specific keywords that the AI recognizes as central to generating the correct response.
  • Closed Questions: Formulating prompts that lead to limited or yes/no type answers, reducing the scope of possible responses.
The focus here is on achieving specific, often simpler outcomes where the context or depth of understanding is not extensively nuanced.




The Anatomy of a Prompt


INSTRUCTION

instruction prompt
The instruction component serves as the foundation of a prompt, guiding the AI by providing a clear directive on what it should generate or provide. Think of it as asking a precise question to get an exact answer. The clarity of instruction plays a crucial role in steering the AI toward the desired output, ensuring that the responses are in line with and relevant to the user’s expectations.

For example, in higher education, instructing an AI to summarise a research article sets a clear task for it to execute.

Prompt: Explain the concept of Constructivism in Education.

CONTEXT

instruction prompt
Context offers additional details that aid the AI in comprehending the setting or circumstances within which the instruction is provided. Incorporating context can result in more knowledgeable and subtle responses, assisting users in clarifying ambiguity and ensuring that the AI’s answers are both relevant and precise.

For instance, specifying the topic and time frame in a request for summarising a research article helps the AI to focus its response accordingly.

Prompt: Explain the concept of Constructivism in Education, focusing on its impact and implications for online learning.

ROLE

instruction prompt
The role component enables users to define a specific persona or context for the AI’s response. This feature can be especially valuable in educational scenarios, where the tone and perspectives of the response may differ based on the assigned role.

For example, instructing the AI to assume the role of an academic advisor could generate a response that is professional and supportive, whereas asking it to assume the role of a student mentor may generate one that is more friendly and casual.

Prompt: Please take on the role of a university professor and explain the concept of Constructivism in Education, focusing on its impact and implications for online learning.

SUCCINCTNESS

instruction prompt
Succinctness emphasizes the need for conciseness and clarity when creating prompts. A well-structured and concise prompt is easier for the AI to understand and respond accurately. It involves finding a balance between providing sufficient information without overwhelming the AI with unnecessary details, resulting in more efficient communication.

Prompt: As a university professor, explain Constructivism in Education and its impact and implications for online learning, focusing only on how it influences course design.

EXAMPLE

instruction prompt
Including an example in a prompt clarifies or illustrates the instruction(s), aiding the AI in better understanding the task. Examples are particularly useful in complex or ambiguous requests, providing a reference point for the AI to gauge the type of response expected by the user.

Prompt: As a university professor, explain Constructivism in Education and its impact and implications for online learning, focusing on how it might influence course design. For example, discuss how a constructivist approach would encourage students to interact and build knowledge through discussions in online forums.

FORMAT

instruction prompt
The format component defines the preferred structure for the AI’s response, whether it’s in paragraphs, bullet points, tables, or other formats. Specifying a format aids in obtaining responses that are clear, comprehensible, and useful, particularly in educational or research contexts where organizing information and presenting data are crucial.

Prompt: As a university professor, provide a bullet-point summary of Constructivism in Education and its impact and implications for online learning, particularly for course design. Categorise the bullet points into subthemes.

ADJUSTMENT

instruction prompt
Adjustment involves refining the prompt based on previous AI responses. It aims to improve clarity, add missing details, and correct misunderstandings to generate a more accurate output. This iterative approach to prompt engineering creates a learning loop between the user and the AI.

Prompt: As a university professor, provide a detailed explanation of Constructivism in Education, its historical evolution in Asia, and its implications for online course design, in a bullet-point format.