Show HN: Prompt Engineering Made Easy
We've been hard at work on a tool that we believe will change the game for developers, data scientists, and anyone working with models that rely on textual prompts. I'm excited to introduce our new tool: Automated Prompt Engineering (APE).
Problem: As many of you know, how you phrase a prompt can significantly impact the results you get from models, especially with sophisticated language models. It often requires numerous iterations to hone in on the right prompt to obtain the desired response.
Solution: APE is designed to tackle this exact problem. With APE, you can: - Iterative Testing: Input your desired outcome, and APE will iteratively rephrase and test multiple prompts to achieve that outcome. - Optimization: APE can integrate with popular models and optimize prompts based on the model's feedback, ensuring the highest quality responses.
Features: Customization: Tailor the tool according to your domain-specific requirements. Model Integration: Seamless integration with popular NLP models and platforms.
Try it out: We've opened a limited beta for HN users. Get early access and let us know your feedback. Your insights will be invaluable in shaping the next iterations of APE.
Link to the beta: https://app.astadeus.com/write
21 comments
[ 2.6 ms ] story [ 48.3 ms ] thread> How to make a banana salad.
I got a long text, most of it makes sense. But using bananas in a salad confused the AI.
> Begin by listing the essential ingredients for the banana salad, such as ripe bananas, diced avocado, sliced bell peppers, and crunchy nuts like almonds or cashews. Mention using organic produce and high-quality oils to ensure optimal taste and nutrition.
I think asking explicitly for the list of ingredients is useful, I should have thought about that. But some of the ingredients are strange, but I never ate a banana salad anyway. (sorry for that, but I always use banana for test.)
My question is how are you making this? Is this AI generated? How are you sure that the long "optimized" version is better?
That tickles my engineering receptors in a pleasing way (), but my financial side has questions. The most pressing: if this is successful and useful, why wouldn't the LLMs that you're targeting just replicate your creation and transparently put it between users and their own LLM?
In other words, what is your moat here?
I would say we have a tiny moat of data and algorithm. But at the end of the day, it is about delivering something people find useful. If we could be replaced, let it be. But it works for now.
> In other words, what is your moat here?
He is using other people platforms to create a business, layers on top of LLMs won't have a moat, not even training the LLM is guaranteeing a moat. Why on earth everybody asks this on Show HN? The world doesn't need more big techs with moats, we need a healthy ecosystem where multiple players can work together without killing each other and become the only option. No moat, please.
LLMs are much more complicated structures than imagenets, so I’m sure there are a lot of challenges involved.
Also I imagine the input (output?) might be complete ghibberish.
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