📘 Prompt Engineering

What is Prompt Engineering?

Prompt Engineering is the process of designing, refining, and
optimizing prompts to guide a foundation model (FM) or large language
model (LLM) toward producing the best possible output for your needs.

A naïve prompt gives little guidance and leaves interpretation up to
the model.
Example: “Summarize what is AWS.”
This works, but the answer may not be clear or focused.

By contrast, Prompt Engineering uses a structured approach to
improve results.

Components of an Effective Prompt

Instructions – What the model should do (e.g., summarize,
explain, compare).\
Context – Additional background that helps guide the response.\
Input Data – The text, question, or data you want the model to
work with.\
Output Indicator – The desired format or style of the answer
(e.g., 2–3 sentences, bullet points, JSON).

Example: Enhanced Prompt

Naïve Prompt: “Summarize what is AWS.”

Enhanced Prompt:
“Write a concise summary that captures the main points of an article
about learning AWS (Amazon Web Services).
Ensure that the summary is clear and informative, focusing on key
services relevant to beginners.
Include details about learning resources and career benefits.
I am teaching a beginner’s AWS course.
Provide a 2–3 sentence summary that captures the essence of the
article.”

This approach makes the task more precise and tailored to the user’s
goal.

Negative Prompting

Negative prompting explicitly tells the model what not to include.
This helps: - Avoid Unwanted Content – Prevents irrelevant or
unnecessary details.\

Maintain Focus – Keeps the response on-topic.\
Enhance Clarity – Avoids complex jargon or deep technical detail
if not needed.

Example with Negative Prompting:
“Summarize an article about AWS for beginners.
Focus on key services, learning resources, and career benefits.
Do not include technical configurations, in-depth tutorials, or
personal anecdotes.
Provide a clear, beginner-friendly 2–3 sentence summary.”

Why It Matters (AWS Exam Relevance)

For AWS AI Practitioner and related certifications, you should know: -
Prompt Engineering improves AI model accuracy and usefulness.\

AWS exams may ask about the difference between naïve prompts,
enhanced prompts, and negative prompts.\
Understanding how to guide model behavior is key in real-world AI
applications, from summarization to chatbots.

Key Takeaways

Naïve prompts = vague and open-ended.\
Enhanced prompts = structured with instructions, context, input, and
output format.\
Negative prompts = control what not to generate.\
Together, these techniques ensure clearer, more accurate, and
useful outputs.