Artificial Intelligence (AI) has revolutionized various sectors by automating tasks, analyzing large datasets, and providing intelligent insights. Among the numerous advancements in AI, natural language processing (NLP) stands out, enabling machines to understand and generate human language. At the heart of effective NLP applications lies the concept of prompt engineering, which involves designing inputs (prompts) to guide AI models like OpenAI’s GPT-3 and GPT-4 to generate desired outputs. This article delves into advanced techniques in prompt engineering, highlighting how these methods enhance AI performance and reliability.

Prompting Basics

Prompting is the process of using natural language (e.g. English) to explain what you want to an AI tool to get a response. Large language models (LLMs) are AI tools that generate human-like text based on what they receive. The goal of prompting is to make it clear what you want to get out of the LLM to get an accurate and meaningful output.

Before we get into more advanced techniques in prompt engineering, let's take a look at what prompts are.

For example, let's say you are a web agency and you want to create a tagline for a new tool you've created to make website design easier and more accessible for users.

The Power of Context and Instruction: Prompt Engineering for AI

To get the most out of your language model, you need to include context and instruction in your prompts. Context helps the model to understand the situation, while instruction tells it what to do. For example, let’s say you have a prompt, “As a Health and Wellness Blogger, Summarize the 4 Key Benefits of a Mediterranean Diet.” Context helps the model understand how to approach the summary from a certain perspective and depth. The instruction tells the model what to focus on and what to produce as output. This is important because it reduces ambiguity, reduces the chance of getting irrelevant or off-topic outputs, gives the AI more control, and saves time as there is less back-and-forth communication to get the desired result.

Advanced Techniques in Prompt Engineering

1.      0-shot Prompting

A zero-shot prompt is when you don’t feed the big language models with any examples or context. It’s great for when you need fast answers to simple questions or just general topics.

For example, can you explain about machine learning in a few sentences?

1-shot Prompt

A one-shot prompt is where you extract a response from the user based on a single example or context.

Example: Translate the following English sentence into Spanish. Here is the sentence “He loves playing football” ---à

Output:  "Él ama jugar al fútbol."

Next, give prompt: Now translate " She loves dolls"

Output: "Ella ama las muñecas."

2.  Information Retrieval

Information retrieval prompting is the process of treating large language models (LLMs) like search engines. It’s when you ask the generative AI to answer a very specific question in order to get more detailed answers. Some LLMs are more effective at information retrieval prompts because they have access to a lot of data.

Example: What is the primary advantage of using social media marketing for a campaign?

3.      Creative Writing Prompt

Writing creative content prompts is a great way to create original stories, engaging stories, and original text that speaks to your audience's needs and interests. Here’s a sample of a creative content prompt that challenges GPT’s 3.5 Generative AI to think outside the box:

“Write a short poem on Sun”

4.  Context Expansion

Context expansion is the finest among the advanced techniques in prompt engineering which involves the process of enriching the context given to an artificial intelligence (AI) to improve its understanding. One of the most effective ways to write a context expansion prompt is using the 5 "Ws and How" method. This method involves expanding a query by asking questions related to its subject matter, such as 'Who, What', 'Where', 'When', 'Why', and 'How'. For example, the following example uses the 5 "Ws" and "How" method to expand a statement:

Example: “Fruits are good for health”.

Use the “5 Ws and How” method, you could formulate the following prompt.

5.  Content Summarization

When it comes to prompt engineering, content summaries with a specific focus are one of the most important things to focus the AI's attention on. This is especially true when you want to create concise, high-quality summaries that capture the essence of what you want the AI to focus on.

When you create specific instructions for content summaries, you can make it easy for the AI to highlight the parts of the text that you want it to focus on.

For example, “Summarize this article on website optimization, but only focus on strategies related to [Topic of an article] optimization: [copy+paste article here].”

6.  Template Filling

With Template Filling, you’ll be able to create flexible yet organized content with ease. You will use a template that comes with placeholders so you can quickly customize for different scenarios or inputs while keeping the same format. You can also customize templates with variables and placeholders in template filling. This allows you to define multiple variables for placeholders. This is a common strategy used by content managers and web developers to create many customized AI-generated content snippets on their websites. For example, if you are running an eCommerce site, you could use a standard product description template. The AI can then populate fields such as product name, product features, price, etc. to generate a new product description with each response.

Example:

As an example, let’s take this prompt: “Generate a personalized welcome email using the template ‘Hello {Name}, Welcome to our {Service}. We are glad you are here! {Closing}'”

7.  Prompt Reframing

Using prompt reframing, or AI prompt customization, you can subtly change the wording of your prompts while preserving the original intent of the query. This can cause the language model to generate multiple responses that respond differently to the original intent.

Wording techniques to maintain intent

For example, using synonyms or rephrasing questions while maintaining the main subject is one way to keep the intent consistent. This will result in different nuances in the answers, which can be especially helpful when you are looking for different ideas.

Example

Original Prompt – What are some ways to optimize a website for speed?

8.  Prompt Combination

In the prompt combination technique, different prompts or questions are combined into one, multi-layered prompt to give the AI a complete response. For this tutorial, we are going to use this example prompt as a starting point:

“Can you explain what is the difference between shared hosting and virtual private server (VPS) hosting and suggest which one is better for your small eCommerce site?”

9.  Iterative prompts

Iterative prompts are another great way to build on previous answers by asking follow-up questions. This allows you to explore a topic more deeply, get more information, or clear up any confusion in the original answer.

Iterative prompts require you to pay attention to the AI's initial response. You can use the follow-up question to explain a particular part of the answer, explore a sub-topic, or ask for clarification.

Iterative prompts are especially useful if you need to collect more detailed information. LLLMs who are better at NLP can use iterative prompts to craft their answers in a more human-like way.

Example 1st Prompt: “What is Data Science?”

Once the ChatGPT generates the answer, you can go ahead and ask ChatGPT in the same chat.

Prompt 2: “What are its advantages?”

ChatGPT will answer you in context to the previous prompt, and generate the output advantages of data science, even though it is mentioned ‘its’ instead of using ‘data science’ again and again.

Conclusion

In conclusion, the above-mentioned are the art of techniques in prompt engineering for AI for engineers. Advanced techniques in prompt engineering leverage the use of ChatGPT or other AI that interacts with humans with prompts. Using them in the right way will result in the best output.