Generative AI and Prompt Engineering.
As technology has continued to progress, and humans have found more tasks to push to machines, the use of Artificial Intelligence has grown, developing beyond just industrial usecases and applications. Now, AI systems have been incorporated into mobile devices, and even become “open source” for individuals to utilize and build with. This is where “Generative AI” and “Prompt Engineering” comes into play, based on the demands of the general internet users.
What is Generative AI?
Generative AI is a novel type of artificial intelligence (AI) that can create various forms of content, based on a user’s input. Generative AI applications make use of the user’s input/command (mostly in text and voice form), to create (samples of the) results of the input. The generated content could be in a variety of forms including text, images, audio, and video.
How does Generative AI work?
AI bots are trained to study a voluminous supply of data. This study helps the bots understand patterns, relationships, and commands, with which it uses to provide results and create content, based on the input query.
For example, if a crypto researcher inputs the phrase “Crypto TVL” as the query, the AI bot will browse through its database, and provide content that concerns “Crypto TVL” as its search results for the researcher to make use of. What the bot has done is to make use of the query to find content that has been optimized for “Crypto TVL” search results, and compiled them to assist the researcher with their research.
Although AI is known to reduce errors and increase accuracy and reliability, the use of generative AI requires a certain level of expertise, in order to help the bots maintain their reliability standard.
Prompt Engineering.
Simply put, Prompt Engineering is the skillful structuring of command requests that helps improve the output accuracy of an AI system. Because AI systems work with very large datasets, knowing how to carefully construct the command query is very important to get the desired results. This structuring helps both the AI to better understand the task, and the user to be satisfied with the output accuracy.
Although simplified, prompt engineering for generative AI is quintessential, as it can greatly affect the quality of work of its user. It determines how effective the AI’s output will be, and how reliable the user’s deliverables will be.
For example, when the user inputs “Crypto TVL”, the AI bots will provide results on “Crypto” and “TVL”. However, the user’s interest may be in just Ethereum as a cryptocurrency, and not the entire crypto industry. Hence, better prompting will require the user’s input command to be “Ethereum TVL”. Assuming there are more specifics like date, time, etc, adding these details to the prompt/command request will further help the bot produce better results.
This means that if a researcher wants to know the total value of tokens locked on Ethereum within a specific timeframe, “Ethereum TVL between (adds timeframe)” will help the AI produce more accurate information, than “Ethereum TVL” or “Crypto TVL” would.
This shows how important prompting is in the use of AI systems. To learn more about Prompt engineering, visit:
How can generative AI be used?
Generative AI bots can be employed for a variety of roles and purposes, including;
- Making Arts.
Generative AI tools like Dall-E, Midjourney, etc can be used by professional digital artists to make unique arts. This can either be used to simplify their work process, or during concept development.
- Drafting Written Content.
ChatGPT is a great AI tool that can be used for content creation, planning, development. It’s access to the internet makes it possible for it to draw up content samples, and give results to suit content keywords and style.
- Making Research.
Google’s Bard has become a reliable tool for researchers who want to streamline the search results they can find on the Google search engine. With a tool like Bard, research materials that may take you hours to find, could be made available in seconds, or minutes, depending on the user’s prompt efficiency.
- Personalized content.
Generative AI can be used to create personalized content for lyrics, storylines, graphic arts, concept designs, you name it. There is an AI tool that can be relied upon to make your work/life easier.
Challenges and limitations of generative AI.
Although a great technology, generative AI still has its limitations and challenges that oppose its 100% reliability. These challenges include:
- Output efficiency.
Mostly as a result of poor prompting, AI models may be unreliable with their performance. This can often lead to badly shaped drawings, poor content quality, incorrect research results, and ultimately a terrible job performance.
- Sentiment/Bias.
Generative AI models can be guilty of bias. This is because they are trained on data that is compiled by “sentimental humans” from the real world, and can often be obvious in the references (sites and sources).
- Plagiarism.
AI models aren’t smart enough to create content in different tones. This is because it is only a machine language. And it’s single database makes plagiarism a major issue, as multiple users who enter the same keywords, may all have the same output results.
- Fake content.
Generative AI models can be used to create fake content which can be used for malicious purposes such as spreading misinformation, creating deepfakes, and scamming unsuspecting individuals.
Best practices when using generative AI.
As a support tool, AI can be of great assistance. AI systems can reduce time spent on jobs, increase productivity, improve performance, and simplify work processes. But as an independent tool, AI can be greatly counterproductive. When using generative AI, it is important to;
- use it responsibly to simplify tasks and responsibilities,
- use it as a support tool and not overdepend on it,
- generate responsible content and avoid unnecessary buzzwords,
- avoid plagiarism by ONLY using AI for concept and not final deliverables,
- maintain safety practices and avoid over exposure to AI.
Conclusion.
In all, AI cannot replace humans. Rather, as a support tool, can improve output depending on proficiency of use. And while we continue to develop technologically, AI will continue to increase in relevance, and efficiency across all usecases. So while we grow, endeavor to stay in touch with tech, and make us of it responsibly.
