Advanced Prompt Engineering: Practical Examples for Text, Code, and Reasoning with LLMs

Large Language Models (LLMs) have transformed the way we interact with information, create content, and build intelligent applications. From summarizing articles to generating code, the key to unlocking their potential lies in effective prompt engineering—the art of crafting instructions that guide the model toward accurate and useful outputs.

In this guide, we’ll go beyond the basics and explore practical examples of prompt engineering across different domains, including summarization, information extraction, question answering, text classification, conversational design, code generation, and reasoning. By the end, you’ll have a solid foundation in crafting prompts that work across multiple scenarios.

Why Examples Are the Best Way to Learn Prompting

While theory is important, nothing beats hands-on experimentation when learning how to prompt LLMs. Each of the examples below demonstrates a real-world use case and highlights subtle techniques that improve outputs. As you read, try adapting the prompts yourself and notice how small adjustments in instruction clarity, context, or examples can dramatically change the results.

Text Summarization with Prompts

One of the most common applications of LLMs is summarizing content into clear, concise insights. Summarization is especially powerful for students, researchers, and professionals who need to process large amounts of information quickly.

Imagine you’re reading about antibiotics. A simple prompt could look like this:

Prompt:

Explain antibiotics

Output (example):
Antibiotics are medications that treat bacterial infections by killing bacteria or stopping them from reproducing. They are ineffective against viruses, and misuse can lead to resistance.

This is useful, but you might want a more focused summary. You can guide the model explicitly:

Prompt:

Summarize the following in one sentence:
Antibiotics are drugs used to treat bacterial infections. They either kill bacteria or prevent them from multiplying, helping the immune system fight off illness. They don’t work against viruses, and overuse can cause antibiotic resistance.

Output (example):
Antibiotics fight bacterial infections but are ineffective against viruses, and overuse may cause resistance.

👉 Takeaway: Adding specific instructions (“summarize in one sentence”) produces more concise results. For SEO purposes, keywords like AI summarization prompts, text summarization with LLMs, and prompt engineering examples should be included to make this section highly searchable.

Information Extraction from Text

LLMs don’t just generate text—they can also pull out structured information from unstructured paragraphs. This is especially valuable for researchers, journalists, and businesses working with reports or articles.

Prompt:

Identify the large language model mentioned in the following text:
"Author-contribution statements in research papers should specify whether authors used AI tools such as ChatGPT during manuscript preparation. Journals should also disclose their use of LLMs for review or selection purposes."

Output (example):
ChatGPT

Here, the LLM has successfully extracted the requested entity.

👉 Pro tip: For better precision, include clear instructions and context. If you ask for multiple entities (e.g., “list all LLMs and AI tools mentioned”), the model can return structured outputs like bullet points or JSON format, which is highly useful for data pipelines.

Question Answering with Structured Prompts

Question answering is another core task where prompt engineering shines. The key is to provide structured context and clear instructions.

Prompt:

Answer the question based on the context below. Keep the response short. If the answer is unknown, reply "Unsure."
Context: Teplizumab originated at Ortho Pharmaceutical in New Jersey. Scientists created an antibody, OKT3, derived from mice. In 1986, it was approved for preventing organ rejection in kidney transplants.
Question: What was OKT3 originally sourced from?

Output (example):
Mice

Notice how the instruction format (context + question + expected answer style) creates consistency and accuracy.

👉 SEO note: Keywords like question answering with prompts, structured prompts for QA, and LLM knowledge extraction should be emphasized here.

Text Classification with Examples

LLMs can classify sentiment, topics, or intent, but the way you phrase the prompt matters. Consider sentiment analysis:

Prompt:

Classify the text as positive, neutral, or negative.
Text: "The food was okay."

Output (example):
Neutral

Now let’s refine it by including an example within the prompt:

Prompt:

Classify the text as positive, neutral, or negative.
Example: "The vacation was okay." → neutral
Text: "The food was okay."

Output (example):
neutral

Adding examples makes the model more consistent and specific in formatting outputs.

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Conversation and Role Prompting

One of the most exciting uses of LLMs is building conversational AI assistants. With role prompting, you can shape how the AI responds.

Prompt:

The following is a conversation with an AI assistant.
The assistant’s style: technical and scientific.

Human: What are black holes?
AI:

Output (example):
Black holes are regions of spacetime where gravity is so strong that nothing, not even light, can escape. They form when massive stars collapse under their own gravity, leading to a singularity.

But what if you want the same explanation simplified for a younger audience?

Prompt:

The following is a conversation with an AI assistant.
The assistant should explain concepts in simple, child-friendly language.

Human: What are black holes?
AI:

Output (example):
Black holes form when very big stars run out of fuel and collapse. Their gravity is so strong that nothing, not even light, can escape.

👉 Role prompting is essential for designing chatbots, tutors, and customer service agents. SEO keywords: role prompting, conversational AI prompts, chatbot design with LLMs.

Code Generation with Prompts

LLMs are revolutionizing software development by assisting with code snippets, queries, and automation scripts.

Example 1 – Simple Greeting Program

Prompt:

/* Ask the user for their name and say hello */

Output (example):

let name = prompt("What is your name?");
console.log(`Hello, ${name}!`);

Example 2 – SQL Query Generation

Prompt:

Tables:
departments(DepartmentId, DepartmentName)
students(DepartmentId, StudentId, StudentName)

Write a MySQL query to list all students in the Computer Science department.

Output (example):

SELECT StudentId, StudentName
FROM students
WHERE DepartmentId IN (
SELECT DepartmentId FROM departments WHERE DepartmentName = 'Computer Science'
);

👉 Developers benefit by using prompts to automate repetitive coding tasks, generate boilerplate, and debug faster. SEO focus: code generation prompts, SQL prompt examples, AI for developers.

Reasoning with LLMs

Reasoning remains one of the most challenging tasks for LLMs, especially when multi-step logic is required.

Prompt:

What is 9,000 × 9,000?

Output (example):
81,000,000

That’s correct. But with more complex reasoning, the model may fail unless you force step-by-step thinking.

Prompt:

The odd numbers in this list are: 15, 32, 5, 13, 82, 7, 1.
Add the odd numbers step by step. Is the result odd or even?

Output (example):
Odd numbers: 15, 5, 13, 7, 1
Sum: 41
41 is odd.

👉 This demonstrates how chain-of-thought prompting improves logical reasoning. SEO keywords: reasoning prompts, AI for logic tasks, chain of thought LLM.

Key Takeaways from These Prompting Examples

  1. Be specific. Vague instructions lead to vague answers.

  2. Use examples. Demonstrations in the prompt increase accuracy.

  3. Provide structure. Combining context, question, and answer format improves consistency.

  4. Adjust tone and role. Role prompting makes AI assistants more useful for different audiences.

  5. Iterate. Prompt engineering is experimental—refinement brings better results.

Learn More About Prompt Engineering

Prompt engineering is a fast-growing skill for developers, researchers, and businesses. By practicing with summarization, extraction, classification, conversation, coding, and reasoning prompts, you’ll build the foundation needed to design powerful AI applications.

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Final Thoughts

Prompt engineering is more than trial and error—it’s a systematic approach to guiding LLMs. Whether you’re summarizing a research paper, extracting insights from text, building a chatbot, or generating code, the principles remain the same: be clear, be specific, and experiment.

Master these techniques now, and you’ll be ahead in the future of human-AI collaboration.

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