Comparing Promptcraft and Prompt Engineering: Test Video generated from AI Prompt script in Pictory
Channel: Realitycraft: GenAI & Creative Worlds
Date: 2023-02-27

Unique Generations test on workflows with Pictory for AI Video at #Realitycraft for the Promptcraft series of video demos:

Workflow in this case was a written prompt in ChatGPT to a script then edited and rewritten for Pictory, then edited again for video generation layers with a voiceover of a Kiwi #NewZealand #AIVoice #Voiceover #AIvideo #review #experiment #producttest 

Script was originally generated then edited quite a bit from ChatGPT, this was the starting script:

"Promptcraft and #promptengineering are two distinct approaches to generating language prompts for #naturallanguageprocessing models.

#Promptcraft involves crafting prompts manually by human experts. This process typically involves identifying the key features of the problem at hand and formulating questions or statements that will elicit the desired response from the model. Promptcrafting can be effective for certain tasks, such as sentiment analysis or named entity recognition, where the relevant features of the text are relatively straightforward.

Prompt engineering, on the other hand, involves using data-driven techniques to automatically generate prompts that are tailored to the specific problem and dataset. This approach typically involves training a separate model to predict the performance of a given prompt on the downstream task. This model can then be used to generate new prompts that are expected to perform well.

The advantage of prompt engineering is that it can be more efficient and scalable than manual promptcrafting, particularly for more complex tasks where the relevant features may be difficult to identify. However, it requires significant expertise in machine learning and data analysis to implement effectively.

In summary, promptcrafting involves manual creation of prompts, while prompt engineering involves automatic generation of prompts using data-driven techniques. Both approaches have their strengths and weaknesses and can be used effectively depending on the specific problem and context."