6 advanced AI prompt engineering techniques for better outputs

Don't miss the first post in this two-part series: Prompt engineering techniques for GenAI power users 

What if you’re really looking to level up your prompt engineering skills? For more sophisticated applications of GenAI, advanced AI prompt engineering techniques can provide even greater control and precision.  

The ability to fine-tune and optimize AI interactions not only boosts the quality and relevance of AI-generated outputs, but also allows users to tackle more nuanced tasks. For enterprises and individuals using AI to generate solutions, mastering these strategies can help ensure you’ll reach your specific goals. 

1. Chain of thought prompting 

Chain of thought prompting involves breaking down complex problems into smaller, sequential steps. In this way, you guide the model through a logical progression. This will help the model to produce a more coherent and accurate response. 

For example, let’s say you wanted your application to generate guidance on how to improve flexibility and balance in mid-life. The basic approach is to ask, “What should somebody in their fifties do to improve flexibility and balance?” However, with chain of thought prompting, you might engineer your prompt to look like this: 

Consider a person who is in their fifties. 
First, describe some of the problems that they may encounter in life that are related to a lack of flexibility or balance or both. 
Next, outline the potential solutions they can put in place to improve flexibility and balance. 
Finally, evaluate the pros and cons of each solution. 

With the basic approach, you might have been given a short list of exercises that a person should perform, perhaps with some basic descriptions. However, if what you were looking for was comprehensive guidance that is well-explained and considers various angles, then the chain of thought approach will help get you there.

2. Few-shot prompting 

Few-shot prompting involves providing a few examples within the prompt to improve the model's performance. By showing the model what kind of response is expected, you help it understand the desired output. 

For example, the basic approach to asking for a meeting summary may simply be, “Based on the meeting transcript I’m uploading, summarize the meeting.” 

With few-shot prompting, your prompt may look like this instead: 

Here are two examples of how to summarize a meeting: 
1. “The team discussed project timelines and assigned tasks.” 
2. “Key points from the meeting included budget adjustments and resource allocation.” 
Now, based on the meeting transcript I’m uploading, summarize this meeting 

 These examples serve as a guide for the model to generate similar responses. 

3. Meta prompting 

With meta prompting, structure and syntax take priority over traditional content-centric methods. This means that the initial prompt or question is intended to generate a secondary, more specific prompt which is then used to create a final output. This two-step process is meant to refine the input and decomposes a prompt into sub-problems to increase accuracy and improve contextual understanding. 

Here’s an example to help provide clarity around this technique with a hypothetical scenario for a travel itinerary. 

Static Prompt: “Provide a travel guide for Paris.” 
Meta Prompt: The AI could be instructed to first generate a query like, “What’s a popular travel destination in Europe?” Once the model identifies “Paris” as a response, it then crafts the travel guide prompt. 

This approach is to have the AI dynamically identify and focus on relevant travel destinations prior to creating a travel guide for you.   

4. Contextual priming 

Even though an LLM is trained on a vast dataset and can start with a broad context for understanding your query, there is often a lot of additional context surrounding your query that a large language model (LLM) does not have access to. When you can prime your model with this additional context in your prompt, you help to set the stage for the model, and this will greatly influence the output. With a good understanding of the background and the specifics of your request, the AI model can tailor its response to be more aligned with your expectations. This is particularly important in tasks that require understanding of nuanced information or detailed instructions. 

OK: “What strategies should our company adopt to maintain its market share?” 

Better: “Given the recent market trends and the increase in competition, what strategies should our company adopt to maintain its market share?" 

Additional context allows the model to generate a response that is better aligned with your specific circumstances and needs. 

5. Self-consistency 

Self-consistency is a technique where you direct the AI model to generate multiple responses to a prompt, and then you ask the model to select the one that is most consistent, as it is likely the one that best meets your criteria. 

As a simple example, consider this prompt: “Explain the concept of prompt engineering in simple terms." 

With this technique, you would direct the AI model to generate multiple responses. Let’s say you asked it to generate three responses: 

"Prompt engineering is the process of designing and refining prompts to guide AI models to produce desired outputs." 
"Prompt engineering involves creating and adjusting prompts to ensure AI models generate accurate and relevant responses." 
"Prompt engineering is the practice of crafting prompts to get specific and useful outputs from AI models." 

Next, the AI model evaluates the generated responses and identifies the common themes across responses. Based on this, it determines that the first response is most consistent with the identified common themes. This first response becomes the final output. 

Of course, you can tune your self-consistency approach to generate more than three possible responses—though the trade-off may be resources and time. However, this will help you better weed out possible inaccuracies or irrelevance in responses. 

The self-consistency technique ensures that the final output is the most accurate and relevant among the options. 

6. ReAct (Reasoning and Acting) 

ReAct combines reasoning steps with action steps within a prompt to guide the model in a structured and dynamic problem-solving process. With this technique, you help the model generate answers and explain the reasoning behind those answers. This improves both accuracy and interpretability. 

For example, let’s assume you are part of a company that is evaluating energy solutions. You might provide a basic prompt such as: “What’s a good sustainable energy solution for our company?” 

However, with ReAct, your prompt may look like this instead: 

Consider primary factors such as environmental impact and cost efficiency. 
Now, recommend a sustainable energy solution for our company. 
Explain why it is the best option. 

This structured approach encourages the model to provide well-reasoned and actionable responses. 

By using these advanced AI prompt engineering techniques, you enhance the capability of your GenAI models to handle complex tasks and produce high-quality outputs. These techniques provide a deeper level of control and refinement and achieve more precise and effective results. 

Optimal GenAI performance via prompt engineering 

Prompt engineering plays a vital role in your GenAI implementation. It involves crafting specific inputs to guide the outputs of GenAI models, and it’s essential for achieving accurate and useful responses. The influence of well-crafted prompts, though somewhat dependent on factors such as model type or task complexity, can be substantial. Better prompts lead to better results! 

Apply these advanced techniques to significantly influence the quality and relevance of the outputs generated by your GenAI models. By mastering prompt engineering, you can maximize the potential of your GenAI investments, ensuring that they align with your organization's objectives and deliver tangible benefits.