Watch the video on YouTube:
https://www.youtube.com/watch?v=uX-p5xP8fqA
Full example code on GitHub, with instructions: https://github.com/encoredev/examples...
See more ways to enhance your apps with AI, check out Encore's open source templates: https://github.com/encoredev/examples
Top comments (6)
I was interested on reading a post about what the title says, instead I found a link to a Youtube video.
I want to believe we all understand these are two different target audiences (or similar audience in different time windows). It's not bad that you add a link to the video, or even embed it
as you did above. Now if I'd like to watch a video instead of reading I'd probably be on youtube directly. Or maybe I am reading Dev.to because I cannot access youtube or turn on the volume where I am at. Evaluating just the text above, this is a very low quality post by all means. One could well use AI to transcribe the video double check-it, correct the format and generate a high quality post with low to medium effort.
Thanks for the feedback! This was posted using Dev.to's "video" feature, which I presume is intended to post videos and puts less emphasis on the written part. I agree the UX around what is a "video post" and what is a "blog post" is very unclear.
Pretty impressive!
I wonder how the response time can improve, I noticed in the demo it took some time to process the user prompt, would caching be a good option in this case?
Thanks for sharing!
The response time is likely due to OpenAI's API needing to process the request. Caching may help speed up responses for repeat questions, and may be a good idea to minimize the use of the OpenAI API since it is not free/unlimited.
Pretty cool. How many requests to OpenAI were there? Is it only one or is it one after each function invocation?
Hey! There is one request to OpenAI after each function invocation. So if you allow the LLM to call a lot of functions in your system you can potentially get a lot of request to OpenAI for each prompt. You should also try to limit the output of your functions because the LLM will read through it all and that eat a lot of tokens.