Step 1: Understand Generative AI

Generative AI refers to systems that create new content based on patterns learned from data, including:

• Text (e.g., ChatGPT, GPT models)

• Images (e.g., DALL·E, MidJourney)

• Audio and music

• Code and 3D models

These systems rely heavily on prompts—the instructions or inputs provided by the user—to generate relevant outputs.

Step 2: Define the Objective of the Prompt

Before writing a prompt, clarify what you want the AI to produce:

• Text completion, summary, or translation

• Creative content (story, poem, artwork)

• Problem-solving or analysis

• Code generation or debugging

A clear goal ensures the prompt is precise and the AI output is useful.

Step 3: Structure the Prompt Effectively

Well-structured prompts increase output quality. Consider:

• Context: Provide background information or scenario.

• Instruction: Specify exactly what you want the AI to do.

• Constraints: Include format, length, style, or tone requirements.

• Examples: Show a sample input-output to guide the AI.

Example:

“Write a 150-word professional email responding to a client complaint about delayed delivery. Use a polite and apologetic tone.”

Step 4: Experiment and Iterate

AI output may vary, so test multiple prompts and refine them:

• Adjust wording for clarity or specificity

• Add constraints to reduce ambiguity

• Use different approaches (questions, step-by-step instructions, or examples)

Iteration helps discover the most effective prompt for your task.

Step 5: Use Advanced Prompting Techniques

• Chain-of-thought prompting: Ask the AI to reason step by step.

• Few-shot prompting: Provide a few examples in the prompt to guide output.

• Role prompting: Assign the AI a role or persona (e.g., “Act as a financial analyst”).

• Instruction tuning: Specify the desired style, format, or perspective.

These techniques improve accuracy, relevance, and creativity.

Step 6: Evaluate and Refine Outputs

• Check AI responses for correctness, bias, and clarity

• Compare multiple outputs to select the best one

• Refine prompts if outputs are incomplete, irrelevant, or off-topic

Evaluation ensures the final output aligns with objectives.

Step 7: Apply Prompt Engineering Across Domains

Prompt engineering can be applied to:

• Business: Report generation, customer support, email drafting

• Education: Tutoring, explanations, quiz creation

• Creativity: Storytelling, visual art, music composition

• Software Development: Code generation, debugging, documentation

Good prompts adapt AI to the specific domain and task.

Step 8: Ethical and Responsible Prompting

• Avoid prompts that encourage harmful, biased, or illegal outputs

• Be transparent when AI-generated content is used

• Respect privacy and intellectual property in prompt data

Responsible prompting ensures safe and fair use of generative AI.

Summary

Prompt engineering is the practice of designing, testing, and refining instructions to guide Generative AI in producing accurate, relevant, and creative outputs. Mastering it involves clarity, structure, iteration, and ethical awareness.

Text Prompt Best Practices 

1. Give Clear Instructions:
   Unclear prompts may result in generic or inaccurate outputs. Be sure to include relevant details, such as the desired output format and context.
2. Assign a Persona:
   Begin your prompt by assigning a specific role to the AI assistant.

Examples:
“You are a senior marketing professional specializing in social media campaigns…”
“As a user experience expert, you are capable of…”

3. Use Delimiters to Structure Your Prompt:
   Delimiters can help organize the prompt clearly. This example uses three quotes as delimiters:

The following is a part of a Wikipedia article on Language Models:

“””
A language model is a probabilistic model of a natural language.[1] In 1980, the first significant statistical language model was proposed, and during the decade IBM performed ‘Shannon-style’ experiments, in which potential sources for language modeling improvement were identified by observing and analyzing the performance of human subjects in predicting or correcting text.
“””

In this prompt, markdown-style ticks are used with the label “python”

How can we make this function more efficient with lru_cache?

`python

def fib(n):

    if n in {0, 1}:

        return n

    return fib(n-1) + fib(n-2)

`

4. Break Tasks into Subtasks:
   Divide complex tasks into manageable steps.

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