LLM prompt engineering might sound like a complex concept, but it’s becoming increasingly important in the modern world.

Large Language Models are beginning to influence every part of the modern world.

They affect how we communicate with machines, create content, and even deliver exceptional customer service. However, while LLMs are often designed to support natural human interactions, speaking to these bots can be complex.

Talking to an LLM means knowing how to craft initial queries (prompts) to show the model exactly what you want to accomplish. That’s where LLM prompt engineering comes in.

How to Talk to an LLM: What is a Prompt?

If you’ve experimented with tools like ChatGPT, Google Bard, or Microsoft Copilot, you might already be familiar with “prompts.” Prompts are instructions given to an LLM to show them precisely what you want them to do. They’re the key to interacting with generative AI bots and algorithms.

Prompts can include instructions, questions, examples, and contextual data, depending on the design of the large language model. In some cases, prompts can even include images. Updated multi-modal language models like Falcon 180B and GPT-4 can assess images using computer vision.

There are various ways to approach prompting. With simple models like GPT-3, you might use a simple text prompt, like “What’s the capital city of Texas?” Prompts can be extremely specific for more advanced use cases, including certain constraints or requirements. For instance, you might ask for a response in length, tone, or style.

The quality of any response delivered by an LLM heavily depends on the prompt’s quality. That’s why prompt engineering has become so crucial in the AI market.

What is Prompt Engineering for LLMs?

LLM Prompt engineering is about refining and structuring your messages to an LLM to ensure the best possible response. The concept first emerged with the release of GPT-3 and ChatGPT in 2020. Initially, many LLMs required highly detailed prompts with examples and in-depth task descriptions.

Today, different LLMs are more effective at responding to specific prompts. While the exact prompt engineering tactic you use when speaking to an LLM may vary, the following factors are usually crucial to ensuring the right results:

  • Prompt Wording: A prompt’s wording is essential to guiding an LLM to produce the correct output. Using specific, detailed, and concise language is often crucial. Complex terms and synonyms can sometimes lead to confusion and AI hallucinations.
  • Roles and goals: In prompt engineering, roles are personas assigned to the LLM and intended audience. For instance, “You’re a sales expert writing a cold email for a SaaS company.” Goals are connected to roles, highlighting what you want the LLM to do. For instance, “write an email encouraging the reader to arrange a meeting.”
  • Positive and negative prompts: Positive and negative prompting is about framing your instructions to show a model what it should and shouldn’t do. For instance, you might ask ChatGPT to create a blog no more than 500 words long. You could also ask it not to discuss specific topics.

When working with LLM models, refining your prompts consistently is crucial to unlocking the best results. Certain models even include specific parameters to improve prompting. For instance, the “temperature” parameter helps control output randomness.

Common LLM Prompt Engineering Strategies

As LLMs continue to evolve, introducing new capabilities, new prompt engineering strategies are emerging. The methods you use to interact with LLMs will likely vary depending on the structure and abilities of the model. However, standard options include:

1. Zero-Shot Prompting

Zero-shot prompting is one of the most common and basic prompt engineering strategies. It involves asking an LLM a fundamental question without providing context or examples. This can be useful if you’re looking for a quick response to a basic question.

However, Zero-Shot prompting can make it difficult to predict the output of a model, as you’re not using any parameters to guide whatever it generates.

Example: “Generate 10 title ideas for a blog on UCaaS.”

2. One-Shot Prompting

One-shot LLM prompt engineering is a slightly more advanced way of speaking to an LLM to guide its response. It involves giving the model a single example of the content you want to produce. This is helpful if you have an idea of the output you’re looking for.

For instance, using the example above, you might include, “One example of a successful blog is “What is UCaaS?”

3. Few-Shot Prompting

With Few-Shot prompting, you provide the LLM with even more context and guidance, improving its chances of generating relevant results. Giving several examples to an LLM as part of your prompt engineering strategy ensures it has plenty of ways to refine its response.

For example, if you wanted an LLM to generate a list of possible names for your new parrot, you might say: “Generate a list of 10 names for my parrot. Names I like already include Mango, Kiwi, and Birdie.”

4. Chain-of-Thought Prompting

One of the more popular forms of LLM prompt engineering, when dealing with conversational LLM bots, is chain of thought prompting. This involves providing an LLM with a handful of examples to help ensure a correct answer, similar to few-shot prompting.

However, unlike few-shot prompting, chain-of-thought methods are structured to encourage critical thinking. Usually, you’ll need to give the model an example of the output you want it to create. For instance, before you ask a mathematical question, you might start with the initial prompt:

Q: Joe has 30 apples. He buys two more bags of apples; each pack includes 15 apples. How many Apples does Joe have now?

A: Joe started with 30 apples and purchased an additional two bags with 15 apples each. 15 multiplied by two is 30, added to the original apples; this gives Joe a total of 60 apples.

Following this example, you can enter your question:

“Using the information above, answer this question: John has 16 eggs. He buys two additional cartons of eggs. Each carton contains eight eggs. How many eggs does John have now?”

5. Self-Criticism Prompting

Self-criticism prompting is all about teaching an LLM to assess its output for possible inaccuracies. This can help to ensure the results you get from your bot are more accurate. After you ask a question and receive a response from the bot, you’d follow up with:

“Please re-read your response. Do you see any mistakes? If so, please identify them and make the necessary edits.”

This prompt engineering strategy is frequently used in the coding landscape when developers find code produced by their LLM doesn’t run as expected.

6. Iterative Prompting

Iterative prompting is another form of advanced prompt engineering used to speak to LLMs. With iterative prompting, you assume your bot isn’t going to give you the best answer to a question straight away. This means you follow up with additional queries to refine the output.

For example, your first prompt might be:

“You’re writing a landing page for a new UCaaS platform. You need to create a headline that will attract the attention of small business owners. Generate five possible headline ideas, and explain why the headlines will appeal to smaller companies.”

The LLM will then produce a range of headlines, after which you can follow up with an additional prompt.

“If you were to use the headline [headline], how could you make it more impactful? In the next paragraph, what would you follow up with to show the product’s benefits? The key benefits of the product are [benefits].”

Quick Tips for Better LLM Prompt Engineering

Mastering LLM prompt engineering and learning how to talk to LLMs effectively can take some time. It’s one of the reasons many employees still struggle to make the most of tools like Microsoft Copilot and Google Bard. However, experimenting with prompt engineering can help businesses provide their team members with structured templates and guidance.

Although the best prompts for any LLM can vary, good prompts should be clear and specific. It’s also worth using some of the following best practices:

  • Focus on what you want the model to do: Instead of telling the model what you don’t want it to do, such as “don’t make the blog too long,” be specific about what you want. Say, “Write a blog about [topic] that is no more than 500 words long.”
  • Provide examples: While zero-shot prompting can work sometimes, providing your model with examples will lead to more accurate responses. Experiment with few-shot prompting, chain-of-thought prompting, self-criticism, and iterative prompting.
  • Structure prompts carefully: Structuring content correctly makes it easier to understand. Bullet points, line breaks, question marks, and so on can give more guidance to your LLM. When mastering prompt engineering, try to master structure.
  • Use leading words: Researchers have found leading words, like telling a model to think through a process “step-by-step,” can lead to more accurate responses. These terms gently guide the model to think through its process carefully rather than just guessing at an answer.
  • Use prompting tools: There are valuable tools available online, such as Semantic Kernel, which allow users to experiment with different prompts and engineering parameters across various models. This will enable users to compare the outputs of different parameters and create more comprehensive prompt templates.

Talking to LLMs: Mastering Prompt Engineering

LLM Prompt engineering is a relatively new and complex topic in the world of artificial intelligence. Ensuring you can speak effectively to LLMs requires creativity, technical knowledge, and experimentation.

Aside from following the tips above and exploring different prompting models, you can boost your chances of success by making sure you:

  • Understand the LLM: Learn as much as possible about the LLM you’re using. Read about how the model was trained, how it’s designed to behave, and how it responds to input.
  • Provide context: An LLM can’t pull context out of the air. It needs insight from you. Providing access to specific examples or allowing LLMs to draw data from your website or database will help produce more accurate output.
  • Experiment: Explore different parameters and templates for fine-tuning your prompts. Once you find a solution that works, share it with your team and build on it collaboratively.

It’s also worth staying up-to-date with the latest advancements in prompt engineering techniques and research; you can enhance your abilities over time.



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