Prompt engineering is the practice of giving inputs for generative AI tools that will generate best results.
Let’s say you’re planning to bake a cake for your friend’s birthday. You could buy a cake mix from the store and follow the instructions on the box. But what if you made the cake from scratch? You could use fresh ingredients like eggs, flour, sugar, and butter to make a delicious cake that’s unique and special. And what if you added your own customization to the recipe by adding your friend’s favorite flavors or colors? It would be a great cake that your friend would love.
By taking the time to make something from scratch, you can create a dish that’s not only delicious but also meaningful and memorable.
Similarly, better the inputs better the results would be for the generative AI. The practice of giving inputs to generative AI is called Prompt Engineering, where we are designing the prompt carefully, so that the output is much better. For example, asking to write emails, writing a technical blog, marketing promotional ad, generating a code, or asking for a dish recipe.
Let’s take an example now let’s ask few questions to ChatGPT
If you observe the output it looks so generic and no specific output for the course of who is giving and what are they working on and why take the course. Now let’s look if I change the input Prompt here.
As you can see now how the entire output has changed by adding few specific details to the prompt. This way it is easier to get the output which we need.
Generative AI has a key role in the future of business and society.
Organizations are already beginning to make changes to their hiring practices that reflect their generative AI ambitions, according to McKinsey’s latest survey on AI. That includes hiring prompt engineers. The survey indicates two major shifts. First, organizations using AI are hiring roles in prompt engineering: 7 percent of respondents whose organizations have adopted AI are hiring roles in this category. Second, organizations using AI are hiring a lot fewer AI-related-software engineers than in 2022: 28 percent of organizations reported hiring for these roles, down from 39 percent last year.
Another Intresting example that I came across was this one:
Let’s say a large corporate bank wants to build its own applications using generative AI to improve the productivity of relationship managers (RMs). RMs spend a lot of time reviewing large documents, such as annual reports and transcripts of earnings calls, to stay up to date on a client’s priorities. The bank decides to build a solution that accesses a generative AI foundation model through an API (or application programming interface, which is code that helps two pieces of software talk to each other). The tool scans documents and can quickly provide synthesized answers to questions asked by RMs. To make sure RMs receive the most accurate answer possible, the bank trains them in prompt engineering. Of course, the bank also should establish verification processes for the model’s outputs, as some models have been known to hallucinate, or put out false information passed off as true.
If you’re an employee in the AI industry, it’s important to keep up with the latest trends and developments in the field. You can develop your skills in areas such as machine learning, natural language processing, and data analysis to stay competitive in the job market. It’s also important to remember that AI is still a relatively new technology, and there is still much to learn about how it works and how it can be used effectively. Also, Employees should have open mind to learn new things and stay up to date.