You’re Using ChatGPT Wrong!
Improve Your ChatGPT Prompts and Discover How Searching Differs from Prompting
The Old Way of Searching
Have you ever felt that using Google is like navigating a cluttered library, a bit like searching for a needle in a haystack? With Generative AI models such as ChatGPT, Gemini, and Perplexity, you no longer need to wade through endless search results. However, effectively prompting a Generative AI model is quite different from performing a standard search.
Precision: The Key to Effective Prompting
A recent episode of The X-Files had protagonist Agent Mulder encounter a genie who granted him a wish for world peace—by removing all humans from Earth. While he got what he asked for, it wasn’t in the way he intended. This scenario highlights the importance of precision, a critical factor when working with Generative AI.
Imagine searching Google for “What is the color of snow?” The top result might simply say, “white.” But if you ask ChatGPT the same question, you’ll likely get a more detailed response:
“The color of snow is typically white because snow is composed of small ice crystals, which reflect and scatter all wavelengths of visible light, making it appear white to our eyes.”
To get a brief answer, a more specific prompt works better, such as, “What is the color of snow? In one word only,” resulting in a concise reply: “white.”
Crafting the Perfect Prompt
When working with AI, specificity is essential. In the example above, refining the prompt to specify the answer’s format made it more concise. Improving prompts often involves providing extra context or structure, which can include formatting requirements, assigned roles, or examples.
Example: Bypassing AI Detectors
If you’re familiar with Generative AI, you know that getting the model to produce text indistinguishable from human writing can be tricky. However, creating more specific prompts can solve this issue. For example, if you want a story about a cat to pass AI detection tests, you might create a prompt with added detail, such as assigning the AI a role and specifying context and formatting. This approach can produce text that bypasses most AI detectors.
Prompting for Multimodal AI
Prompt engineering extends beyond text-only AI. For multimodal systems, like text-to-image generation, adding context and stylistic instructions can lead to more visually appealing images. An image prompt with only a task may produce a basic result, while a prompt with added detail creates something much richer.
Where to Learn More
Adding specificity to prompts can significantly improve AI-generated results, but precision is only part of the prompt-engineering process. To learn more, try the course “Prompt Engineering with Llama 2” and other LLM/Generative AI courses at the Deep Learning Institute. OpenAI’s “Prompt Engineering Documentation” is also a valuable resource.
- ChatGPT prompting techniques
- Improve ChatGPT prompts
- Difference between search and prompt in AI
- Effective prompt engineering
- Bypass AI detection with prompts
- Generative AI models like ChatGPT
- Learning prompt engineering
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