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AI for AWS Documentation

awsdocsgpt.com

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Re: AI for AWS Documentation

#111

Post by zim1 »

underlines wrote: post #58

RAG is very difficult to do right. I am experimenting with various RAG projects from [1]. The main problems are: - Chunking can interfer with context boundaries - Content vectors can differ vastly from question vectors, for this you have to use hypothetical embeddings (they generate artificial questions and store them) - Instead of saving just one embedding per text-chuck you should store various (text chunk, hypothe…

Have you found any particularly promising approach towards "hypothetical embeddings" that you can recommend?

Re: AI for AWS Documentation

#113

Post by [deleted] »

shaunpud wrote: post #77
sovietmudkipz wrote: post #31

This is exactly the kind of software that people should be making. Even if this one is bad, the concept is very sound. I want to have a specialist AI that is trained to help me learn how to use the software. 100% what should be happening. General AI should know how to do stuff too but having an specialist AI implies that the company/group of people/person is making sure to tune the model. Just an IMO.

I was looking at the CloudLinux[1] docs the other day and noticed an "Ask GPT?" feature which I found quite helpful. [1] https://docs.cloudlinux.com

[deleted]

Re: AI for AWS Documentation

#114

Post by d4rkp4ttern »

tomhamer wrote: post #83
underlines wrote: post #58

RAG is very difficult to do right. I am experimenting with various RAG projects from [1]. The main problems are: - Chunking can interfer with context boundaries - Content vectors can differ vastly from question vectors, for this you have to use hypothetical embeddings (they generate artificial questions and store them) - Instead of saving just one embedding per text-chuck you should store various (text chunk, hypothe…

Marqo provides automatic, configurable chunking (for example with overlap) and can allow you to bring your own model or choose from a wide range of opensource models. I think e5-large would be a good one to try. https://github.com/marqo-ai/marqo

Using Qdrant doesn’t require docker, like Marqo does (from the README). Any trade offs between the two? Doc chunking is an independent functionality and there are already line that help chunk with overlap etc,and also it’s not hard to roll your own.

Re: AI for AWS Documentation

#115

Post by d4rkp4ttern »

underlines wrote: post #58

RAG is very difficult to do right. I am experimenting with various RAG projects from [1]. The main problems are: - Chunking can interfer with context boundaries - Content vectors can differ vastly from question vectors, for this you have to use hypothetical embeddings (they generate artificial questions and store them) - Instead of saving just one embedding per text-chuck you should store various (text chunk, hypothe…

That’s an interesting list (awesome-marketing-data science). Curious what is specific to marketing in that list, or maybe it’s just an inaccurate name.

Re: AI for AWS Documentation

#116

Post by fragmede »

raincole wrote: post #104
sovietmudkipz wrote: post #31

This is exactly the kind of software that people should be making. Even if this one is bad, the concept is very sound. I want to have a specialist AI that is trained to help me learn how to use the software. 100% what should be happening. General AI should know how to do stuff too but having an specialist AI implies that the company/group of people/person is making sure to tune the model. Just an IMO.

MDN implemented an "AI explains" feature, and the reception is extremely negative. https://github.com/mdn/yari/issues/9208

Is it? It's true there are a lot of very loud messages on that linked issue, but only MDN knows how many people are using and engaging with their system compared to on that thread. We, as the general public, don't know anything other than there's a vocal group of dissidents, and what MDN tells us. Which I looked and didn't find stats on its usage.

Re: AI for AWS Documentation

#117

Post by re5i5tor »

BoorishBears wrote: post #89
scarface_74 wrote: post #81

Earlier quoted context omitted.

I asked ChatGPT the same question again and then followed up with: “Is there a more efficient way to connect to the database” It then went off the rails. It gave me generic answers about using connection pooling that your language framework provides. Of course that doesn’t help with each Lambda being invoked separately. I then asked “But each Lambda runs based on its own vm” It then correctly said that while each Lam…

Any time you ask ChatGPT a technical question on something you're not familiar with it should be three parts: - What are the considerations? - Implement it with the considerations - Did we forget anything? It understands the need for a proxy from step 1: https://chat.openai.com/share/7ca37130-a771-457c-8742-a5f941...

Yes! Excellent approach, capturing.

Re: AI for AWS Documentation

#118

Post by re5i5tor »

BoorishBears wrote: post #89
scarface_74 wrote: post #81

Earlier quoted context omitted.

I asked ChatGPT the same question again and then followed up with: “Is there a more efficient way to connect to the database” It then went off the rails. It gave me generic answers about using connection pooling that your language framework provides. Of course that doesn’t help with each Lambda being invoked separately. I then asked “But each Lambda runs based on its own vm” It then correctly said that while each Lam…

Any time you ask ChatGPT a technical question on something you're not familiar with it should be three parts: - What are the considerations? - Implement it with the considerations - Did we forget anything? It understands the need for a proxy from step 1: https://chat.openai.com/share/7ca37130-a771-457c-8742-a5f941...

I do short quote-posts on my blog and wrote this one up.

Re: AI for AWS Documentation

#119

Post by poxrud »

jacurtis wrote: post #86
scarface_74 wrote: post #41

Earlier quoted context omitted.

One of my pet peeves about almost every answer on Stack Overflow is that the answer always starts with client=boto3.client( , access-key,secret-key) There is never a reason to include those last two parameters. It’s always a security risk no matter how you do it.

For that matter, using an access-key or secret-key at all is basically an anti-pattern now. In the past few months AWS has added to their UI a popup box that asks you why you are creating access keys, it makes you answer it before generating the keys. Once you give it the answer it tells you better ways to do almost anything you're trying to do, and you have to agree to ignore those suggestions one more time before a…

It’s still the fastest and most common way to get aws cli working on a local dev machine.

Re: AI for AWS Documentation

#120

Post by scarface_74 »

poxrud wrote: post #119
jacurtis wrote: post #86

Earlier quoted context omitted.

For that matter, using an access-key or secret-key at all is basically an anti-pattern now. In the past few months AWS has added to their UI a popup box that asks you why you are creating access keys, it makes you answer it before generating the keys. Once you give it the answer it tells you better ways to do almost anything you're trying to do, and you have to agree to ignore those suggestions one more time before a…

It’s still the fastest and most common way to get aws cli working on a local dev machine.

If you’re part of any decently large organization, you’re probably using Control Tower or another solution that lets you create temporary credentials easily.
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