AI Prompt Engineering
- Prompt Engineering Frameworks

AI Prompt Engineering

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AI Prompt: AI Prompt Engineering

Imagine a quirky AI named [Prompty], who’s been tasked with turning the vague into the valuable by mastering the art of [AI Prompt Engineering]. Your request is to help [Prompty] understand how to craft prompts that elicit the best responses from various AIs, while also keeping a straight face. For example, you could say, “Explain quantum physics in the style of a stand-up comedian,” or “Describe the emotional journey of a sock losing its partner in the dryer.” Adjustments can be made by introducing elements like genre, tone, or complexity, because who doesn’t love a [dramatic twist] in their AI interactions? The desired output is a playful guide for [Prompty] that mixes informative content with a sprinkle of humor, like a comedian telling dad jokes at a science fair. As an extra, throw in a pie chart that humorously illustrates the 'Emotional Turmoil of AI' when faced with ambiguous prompts—because let’s face it, even AIs can have a bad day!

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What is AI Prompt Engineering?

AI Prompt Engineering is the process of designing and refining prompts that are used to interact with artificial intelligence models, particularly large language models. It involves crafting specific inputs that guide the AI's responses, ensuring that the outputs are relevant, accurate, and aligned with user expectations.

Key aspects of AI Prompt Engineering include:

Clarity: Clear and concise prompts help the AI understand the task at hand, reducing ambiguity and improving response quality.
Context: Providing sufficient context can help the AI generate more specific and relevant outputs. This can include background information or detailing the desired format of the response.
Iterative Refinement: The process often involves iterating on prompts based on the AI's performance. Users may need to adjust wording or structure to obtain the desired results.
Use of Examples: Providing examples within the prompt can guide the AI's responses, making it easier for the model to understand the intended style or tone.
Limitations Awareness: Understanding the limitations of the AI is crucial. Certain prompts may lead to less reliable outputs, so engineers should be aware of these pitfalls.

AI Prompt Engineering Frameworks Analysis

quadrantChart title AI Prompt Engineering Frameworks 2024 x-axis Low Adoption --> High Adoption y-axis Low Maturity --> High Maturity quadrant-1 Leaders quadrant-2 Challengers quadrant-3 Niche players quadrant-4 Visionaries CREATE: [0.95, 0.9] RISE: [0.85, 0.8] GLUE: [0.75, 0.75] ITAP: [0.7, 0.7] APE: [0.6, 0.6] RACE: [0.55, 0.55] COAST: [0.5, 0.5] TAG: [0.45, 0.45] STAR: [0.35, 0.4] LM-BF: [0.3, 0.35]

Prompt Engineering Frameworks

CREATE (Character, Request, Examples, Adjustment, Type of Output, Extras)
RISE (Role, Input, Steps, Execution)
GLUE (Goal, List, Unpack, Examine)
ITAP (Input, Task, Annotation, Prediction)
APE (Action, Purpose, Expectation)
RACE (Role, Action, Context, Expectations)
COAST (Character, Objectives, Actions, Scenario, Task)
TAG (Task, Action, Goal)
STAR (Situation, Task, Action, Result)
LM-BF (Large Language Model Best Friend)
PARE (Prompt, Action, Response, Evaluation)
SCOPE (Situation, Context, Objective, Plan, Execution)
FRAME (Focus, Role, Action, Method, Evaluation)
TRACE (Task, Role, Action, Context, Evaluation)
QUEST (Question, Understanding, Example, Solution, Test)
GUIDE (Goal, Understanding, Instruction, Demonstration, Evaluation)
MAP (Model, Action, Purpose)
LEAD (Lead, Explain, Act, Deliver)
DRIVE (Define, Role, Input, Validate, Execute)
PLAN (Purpose, Layout, Action, Note)
RTF (Role, Task, Format)
SMART (Specific, Measurable, Achievable, Relevant, Time-bound)
COAST (Challenge, Objective, Actions, Strategy, Tactics)
FOCUS (Focus, Objective, Context, Understanding, Strategy)
Bloom’s Taxonomy (Remember, Understand, Apply, Analyze, Evaluate, Create)
Pros and Cons Analysis (Evaluate benefits and drawbacks)
3Cs Model (Company, Customer, Competitor)
4S Method (Structure, Style, Substance, Speed)
CAR-PAR-STAR (Context, Action, Result - Problem, Action, Result - Situation, Task, Action, Result)
PROMPT (Persona, Request, Output, Modifier, Provide Example, Tone)

Table of Contents

AI Prompt Engineering

What is AI Prompt Engineering?

AI Prompt Engineering Frameworks Analysis

Prompt Engineering Frameworks