Proven Techniques For Incredibly Useful LLM Responses

Why do some of my conversations with ChatGPT and other LLMs, like Claude, fall flat?

while others feel really useful?

I wondered this same thing when I first got started using LLMs back in 2013.

The difference lies in the art of prompt engineering

- even though it is an art, there are engineering principles that work like the laws of physics.

In this post I will teach you the principals so you can create prompts that will turn your basic LLM conversations into incredibly useful insights for your business and projects.

First…

What Is Prompt Engineering?

Prompt engineering is more than just typing in a command and getting a result.

It’s about putting together instructions that lead the AI to deliver exactly what you need. Think of it as the blueprint for a building—the better the blueprint, the stronger and more functional the building.

Let’s break it down into actionable steps:

Constructing the Prompt: This involves providing relevant context to the AI. The more specific you are, the better the response.
Optimizing the Prompt: This is where you refine and tweak the prompt to improve the quality of the output over time.

Why Some Prompts Work Better Than Others

When you move from basic to advanced prompt engineering, you start to notice that not all prompts are created equal.

Some generate spot-on responses, while others fall flat. The difference often lies in the techniques used.

Techniques to Improve Your Prompting

Here are some essential techniques you can use to get more out of your prompts:

Zero-Shot Prompting: The most straightforward approach. You ask the AI a direct question without giving it any examples to guide its response.
Example: “What is algebra?”
Response: “Algebra is a branch of mathematics that studies mathematical symbols and the rules for manipulating these symbols.”
Few-Shot Prompting: This technique involves providing the AI with a few examples to guide its response. It’s like giving a student a few sample problems before asking them to solve a new one.
Example:
Prompt: “Write a poem in the style of Shakespeare. Here are a few examples of Shakespearean sonnets…”
Result: The AI generates a poem that mimics Shakespeare’s style, delivering a much more tailored output.
Chain-of-Thought Prompting: This technique involves breaking down the problem into steps, guiding the AI through a logical process to reach the correct answer.
Example:
Problem: “Alice has 5 apples, throws 3 apples, gives 2 to Bob, and Bob gives one back. How many apples does Alice have?”
Using a chain of thought:
“Lisa has 7 apples, throws 1 apple, gives 4 to Bart and Bart gives one back: 7 - 1 = 6, 6 - 4 = 2, 2 + 1 = 3…”
Result: The AI correctly calculates the answer.
Generated Knowledge: Incorporating specific data from your business or field into the prompt to make the output more relevant.
Example:
Insurance company: ACME Insurance
Insurance products:
- Car, cheap, $500
- Home, expensive, $1200
  “Please suggest an insurance package given a $1000 budget.”
  Result: The AI provides a response tailored to your specific offerings.
Least-to-Most Prompting: Break down a big problem into smaller, manageable parts. This helps the AI tackle complex issues step by step.
Example:
“How to perform data science in 5 steps?”
AI response:
- Collect data
- Clean data
- Analyze data
- Plot data
- Present data
Self-Refine: After receiving an output, ask the AI to critique and improve its own response. This iterative process can lead to significantly better results.
Example:
Prompt: “Create a Python Web API with routes for products and customers.”
AI Output: A basic API structure.
Follow-up: “Suggest 3 improvements for this code.”
Result: The AI refines its initial code, improving it based on its critique.
Maieutic Prompting: Ask the AI to explain each part of its response to ensure it’s correct and consistent.
Example:
Prompt: “How can I create a crisis plan for a pandemic?”
AI responds with steps like “Identify risks, stakeholders, resources…”
Follow-up: “Explain the first step in more detail.”
Result: You get a detailed breakdown of each part, ensuring accuracy.

Managing Output Variability

LLMs (Large Language Models) are naturally non-deterministic, meaning they can produce different results with the same prompt. While this can be useful in creative tasks, it can be an issue if you need consistency.

Controlling Variability with Temperature:
Temperature settings in AI models control the randomness of the output. A lower temperature (closer to 0) will give you more predictable results, while a higher temperature (closer to 1) allows for more varied and creative responses.

Example:
At a low temperature, you might get a more straightforward and repetitive response, while a higher temperature can yield more diverse and sometimes unexpected answers.

Best Practices for Prompting

Here are some practical tips to ensure you get the best results from your prompts:

Specify Context: Always provide as much context as possible.
Limit the Output: If you need a specific number of items or a certain length, make it clear in the prompt.
Specify What and How: Don’t just ask for something—explain how you want it.
Use Templates: For repetitive tasks, create templates with placeholders that you can fill in as needed.
Check Spelling and Grammar: It may seem trivial, but clear and correct language helps the AI understand your request better.

Master Prompt Engineering and Transform Your LLM Conversations Today

Mastering prompt engineering is about experimenting, learning, and optimizing.

By applying these techniques and best practices, you can guide AI models to produce precisely the results you need, whether you’re coding, creating content, or solving complex problems.

Keep refining your approach, and remember—the goal is always to truly help, with no fluff, no filler, just value.

If you’re ready to take your prompt engineering skills to the next level, check out the free resources and templates available on vladshostak.com. Let’s build something great together @ devsquadsix.