Prompt Engineering for Specific Tasks: A Focused Tutorial - Zroam Tools

Large language models (LLMs) are incredibly powerful, but their output is highly dependent on the prompt you provide. This tutorial focuses on the art of prompt engineering, specifically tailored to achieving desired results for specific tasks. We’ll move beyond generic prompts and dive into crafting prompts that unlock the true potential of these models.

What is Prompt Engineering?

Prompt engineering is the process of designing and refining prompts to elicit specific and desired outputs from LLMs. It’s about understanding how the model interprets your instructions and then crafting those instructions in a way that leads to the best possible outcome.

Why is it Important?

A well-crafted prompt can be the difference between a vague, unhelpful response and a highly accurate, insightful one. Prompt engineering allows you to:

Control the tone and style of the output.

Guide the model’s reasoning process.

Ensure factual accuracy and relevance.

Minimize bias and unintended consequences.

Task: Summarizing a News Article

Let’s tackle a common task: summarizing a news article. A naive prompt like “Summarize this article:” might yield a result, but it might not be concise, accurate, or focused on the key information. Let’s improve it!

Step 1: The Naive Prompt



        

        Summarize this article:
[PASTE ARTICLE TEXT HERE]

This is a starting point, but we can do much better.

Step 2: Adding Context and Instructions

Let’s provide more context and specific instructions to the LLM:



        

        You are a professional news editor.  Summarize the following news article in three concise sentences, focusing on the main event, key figures involved, and the potential impact.  Ignore any irrelevant details.
Article:
[PASTE ARTICLE TEXT HERE]

Here’s what we added:

Role-playing (“You are a professional news editor”): This guides the model to adopt a specific persona and writing style.

Length constraint (“Summarize in three concise sentences”): This ensures the output is concise.

Focus keywords (“main event, key figures involved, potential impact”): This directs the model to extract specific information.

Negative constraint (“Ignore any irrelevant details”): This helps filter out unnecessary information.

Step 3: Refining the Prompt (Adding Examples – Few-Shot Learning)

To further improve the summary, we can provide the LLM with a few examples of what a good summary looks like. This is called few-shot learning.



        

        You are a professional news editor.  Summarize the following news articles in three concise sentences, focusing on the main event, key figures involved, and the potential impact.  Ignore any irrelevant details.
Here are a couple of examples:
Article 1: [PASTE ARTICLE TEXT HERE]

        Summary 1: [EXAMPLE SUMMARY HERE]
Article 2: [PASTE ARTICLE TEXT HERE]

        Summary 2: [EXAMPLE SUMMARY HERE]
Now summarize this article:
Article:
[PASTE ARTICLE TEXT HERE]

By providing examples, you’re teaching the model what you consider a good summary.

Task: Generating Code (Python Function)

Let’s look at another task: generating Python code. We’ll aim for a function that calculates the factorial of a number.

Step 1: The Basic Prompt



        

        Write a Python function that calculates the factorial of a number.

This will likely produce a function, but let’s make it better.

Step 2: Specifying Requirements



        

        Write a Python function called `factorial` that takes an integer `n` as input and returns the factorial of `n`. Include error handling to raise a `ValueError` if `n` is negative.  Include a docstring explaining the function's purpose and parameters.

We’ve added:

Specific function name (`factorial`) and parameter name (`n`): This defines the function signature.

Error handling requirement: This makes the code more robust.

Docstring requirement: This promotes good coding practices.

Step 3: Optimizing for Style (Optional)



        

        Write a well-documented Python function called `factorial` that takes an integer `n` as input and returns the factorial of `n`.  Use an iterative approach (not recursion). Include error handling to raise a `ValueError` if `n` is negative. The docstring should follow PEP 257 conventions.