Hello everyone, welcome to the Prompt Engineering Podcast. I am glad that you are giving a very good response and learning this unique skill with me. In the previous video, we learned many useful Prompt Engineering techniques to boost productivity, like zero one and few short prompting. In this video, we are going to dive deep into some more interesting Prompt Engineering techniques which will enhance your productivity and give you surprising results.
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The first slightly more advanced technique that I want to discuss is the ‘Ask Before Answer’ prompting technique. The idea is simple - before generating its main answer to your prompt, GI GPT should first ask a couple of clarification questions. These questions allow ChatGPT to produce a better answer.
Here’s an example of this technique:
Role: Experienced travel blogger with a focus on destinations for nature and activity lovers Goal: Write a blog post about snorkeling at Champagne Beach on Dominica
Prompt: ‘As an experienced travel blogger with a focus on destinations for nature and activity lovers, write a blog post about snorkeling at Champagne Beach on Dominica. Provide details about the underwater features, marine life, snorkeling conditions, and nearby attractions.’
By adding this last paragraph to the prompt, we tell ChatGPT to ask for more information if needed to ensure the best possible blog post is written. This technique allows us to refine the response and get a better output.
The ‘Ask Before Answer’ prompting technique is really special and helpful in many scenarios. It allows us to define a goal, role, and constraints, while taking advantage of ChatGPT’s vast knowledge and capabilities to generate a better response.
Another technique we can use is asking ChatGPT for extra information to improve its response. After receiving a response, we can ask for additional details or clarification to get a better output. This approach complements the ‘Ask Before Answer’ technique and helps us refine the response even further.
We can also ask ChatGPT to criticize, evaluate, and improve itself. By asking for feedback on the generated output, we can get suggestions on how to improve it. This self-critical approach can lead to better results.
In addition to these techniques, we can use the ‘Splitting the Problem’ technique. Instead of solving a complex problem all at once, we can split it into smaller steps and let ChatGPT handle each step. This technique leverages ChatGPT’s ability to split up a problem and generate results for each step.
By using these Prompt Engineering techniques, we can enhance our productivity and get better results from ChatGPT. It’s important to remember that ChatGPT is a language model, and sometimes it may generate nonsensical or less useful responses. However, by refining our prompts and leveraging the capabilities of ChatGPT, we can achieve fruitful results.
In the next episode, we will continue exploring more exciting Prompt Engineering techniques that will further boost your productivity. Stay tuned!
