Show me the use cases you have supported in production. Then I might read all the 30 pages praising the dozens (soon to be hundreds?) of “best practices” to build LLMs.
Hi, Hamel here. I'm one of the co-authors. I'm an independent consultant and not all clients allow me to talk about their work.
However, I have two that do, which I've discussed in the article. These are two production use cases that I have supported (which again, are explicitly mentioned in the article):
Eugene Yan works with LLMs extensively at Amazon and uses that to inform his writing: https://eugeneyan.com/writing/ (However he isn't allowed to share specifics about Amazon)
We use LLMs in dozens of different production applications for critical business flows. They allow for a lot of dynamism in our flows that aren’t amenable to direct quantitative reasoning or structured workflows. Double digit percents of our growth in the last year are entirely due to them. The biggest challenge is tool chain, limits on inference capacity, and developer understanding of the abilities, limits, and techniques for using LLMs effectively.
I often see these messages from the community doubting the reality, but LLMs are a powerful tool in the tool chest. But I think most companies are not staffed with skilled enough engineers with a creative enough bent to really take advantage of them yet or be willing to fund basic research and from first principles toolchain creation. That’s ok. But it’s foolish to assume this is all hype like crypto was. The parallels are obvious but the foundations are different.
No one is saying that all of AI is hype. It clearly isn't.
But the facts are that today LLMs are not suitable for use cases that need accurate results. And there is no evidence or research that suggests this is changing anytime soon. Maybe for ever.
There are very strong parallels to crypto in that (a) people are starting with the technology and trying to find problems and (b) there is a cult like atmosphere where non-believers are seen as being anti-progress and anti-technology.
> We use LLMs in dozens of different production applications for critical business flows. They allow for a lot of dynamism in our flows that aren’t amenable to direct quantitative reasoning or structured workflows. Double digit percents of our growth in the last year are entirely due to them. The biggest challenge is tool chain, limits on inference capacity, and developer understanding of the abilities, limits, and techniques for using LLMs effectively.
That sounds like corporate buzzword salad. It doesn't tell much as it stands, not without at least one specific example to ground all those relative statements.
Processing high volumes of unstructured data (text)… we’re using a STAG architecture.
- Generate targeted LLM micro summaries of every record (ticket, call, etc.) continually
- Use layers of regex, semantic embeddings, and scoring enrichments to identify report rows (pivots on aggregates) worth attention, running on a schedule
- Proactively explain each report row by identifying what’s unusual about it and LLM summarizing a subset of the microsummaries.
- Push the result to webhook
Lack of JSON schema restriction is a significant barrier to entry on hooking LLMs up to a multi step process.
Another is preventing LLMs from adding intro or conclusion text.
> Lack of JSON schema restriction is a significant barrier to entry on hooking LLMs up to a multi step process.
(Plug) I shipped a dedicated OpenAI-compatible API for this, jsonmode.com a couple weeks ago and just integrated Groq (they were nice enough to bump up the rate limits) so it's crazy fast. It's a WIP but so far very comparable to JSON output from frontier models, with some bonus features (web crawling etc).
We actually built an error-tolerant JSON parser to handle this. Our customers were reporting exactly the same issue- trying a bunch of different techniques to get more usefully structured data out.
> Lack of JSON schema restriction is a significant barrier to entry on hooking LLMs up to a multi step process.
How are you struggling with this, let alone as a significant barrier? JSON adherence with a well thought out schema hasn't been a worry between improved model performance and various grammar based constraint systems in a while.
> Another is preventing LLMs from adding intro or conclusion text.
Also trivial to work around by pre-filling and stop tokens, or just extremely basic text parsing.
Also would recommend writing out Stream-Triggered Augmented Generation since the term is so barely used it might as well be made up from the POV of someone trying to understand the comment
I only became aware of it recently and therefore haven’t done more than play with in a fairly cursory way, but unstructured.io seems to have a lot of traction and certainly in my little toy tests their open-source stuff seems pretty clearly better than the status quo.
“Use layers of regex, semantic embeddings, and scoring enrichments to identify report rows (pivots on aggregates) worth attention, running on a schedule”
This is really interesting, is there any architecture documentation/articles that you can recommend?
We have a company mail, fax, and phone room that receives thousands of pages a day that now sorts, categorizes, and extracts useful information from them all in a completely automated way by LLMs. Several FTEs have been reassigned elsewhere as a result.
It certainly has use cases, just not as many as the hype lead people to believe.
For me:
-Regex expressions: ChatGPT is the best multi-million regex parser to date.
-Grammar and semantic check: It's a very good revision tool, helped me a lot of times, specially when writing in non-native languages.
-Artwork inspiration: Not only for visual inspiration, in the case of image generators, but descriptive as well. The verbosity of some LLMs can help describe things in more detail than a person would.
-General coding: While your mileage may vary on that one, it has helped me a lot at work building stuff on languages i'm not very familiar with. Just snippets, nothing big.
I have a friend who uses ChatGPT for writing quick policy statement for her clients (mostly schools). I have a friend who uses it to create images and descriptions for DnD adventures. LLMs have uses.
The problem I see is, who can an "application" be anything but a little window onto the base abilities of ChatGPT and so effectively offers nothing more to an end-user. The final result still have to be checked and regular end-users have to do their own prompt.
Edit: Also, I should also say that anyone who's designing LLM apps that, rather than being end-user tools, are effectively gate keepers to getting action or "a human" from a company deserves a big "f* you" 'cause that approach is evil.
I think it comes down to relatively unexciting use cases that have a high business impact (process automation, RPA, data analysis), not fancy chatbots or generative art.
For example, we focused on the boring and hard task of web data extraction.
Traditional web scraping is labor-intensive, error-prone, and requires constant updates to handle website changes. It's repetitive and tedious, but couldn't be automated due to the high data diversity and many edge cases. This required a combination of rule-based tools, developers, and constant maintenance.
We're now using LLMs to generate web scrapers and data transformation steps on the fly that adapt to website changes, automating the full process end-to-end.
I’d you’re interested in using one of the LLM-applications I have in prod, check out https://hex.tech/product/magic-ai/ It has a free limit every month to give it a try and see how you like it. If you have feedback after using it, we’re always very interested to hear from users.
mloncode|1 year ago
However, I have two that do, which I've discussed in the article. These are two production use cases that I have supported (which again, are explicitly mentioned in the article):
1. https://www.honeycomb.io/blog/introducing-query-assistant
2. https://www.youtube.com/watch?v=B_DMMlDuJB0
Other co-authors have worked on significant bodies of work:
Bryan Bischoff lead the creation of Magic in Hex: https://www.latent.space/p/bryan-bischof
Jason Liu created the most popular OSS libraries for structured data called instructor https://github.com/jxnl/instructor, and works with some of the leading companies in the space like Limitless and Raycast (https://jxnl.co/services/#current-and-past-clients)
Eugene Yan works with LLMs extensively at Amazon and uses that to inform his writing: https://eugeneyan.com/writing/ (However he isn't allowed to share specifics about Amazon)
I believe you might find these worth looking at.
anon373839|1 year ago
fnordpiglet|1 year ago
I often see these messages from the community doubting the reality, but LLMs are a powerful tool in the tool chest. But I think most companies are not staffed with skilled enough engineers with a creative enough bent to really take advantage of them yet or be willing to fund basic research and from first principles toolchain creation. That’s ok. But it’s foolish to assume this is all hype like crypto was. The parallels are obvious but the foundations are different.
threeseed|1 year ago
But the facts are that today LLMs are not suitable for use cases that need accurate results. And there is no evidence or research that suggests this is changing anytime soon. Maybe for ever.
There are very strong parallels to crypto in that (a) people are starting with the technology and trying to find problems and (b) there is a cult like atmosphere where non-believers are seen as being anti-progress and anti-technology.
TeMPOraL|1 year ago
That sounds like corporate buzzword salad. It doesn't tell much as it stands, not without at least one specific example to ground all those relative statements.
mvdtnz|1 year ago
robbiemitchell|1 year ago
- Generate targeted LLM micro summaries of every record (ticket, call, etc.) continually
- Use layers of regex, semantic embeddings, and scoring enrichments to identify report rows (pivots on aggregates) worth attention, running on a schedule
- Proactively explain each report row by identifying what’s unusual about it and LLM summarizing a subset of the microsummaries.
- Push the result to webhook
Lack of JSON schema restriction is a significant barrier to entry on hooking LLMs up to a multi step process.
Another is preventing LLMs from adding intro or conclusion text.
adamsbriscoe|1 year ago
(Plug) I shipped a dedicated OpenAI-compatible API for this, jsonmode.com a couple weeks ago and just integrated Groq (they were nice enough to bump up the rate limits) so it's crazy fast. It's a WIP but so far very comparable to JSON output from frontier models, with some bonus features (web crawling etc).
joatmon-snoo|1 year ago
You can check it out over at https://github.com/BoundaryML/baml. Would love to talk if this is something that seems interesting!
BoorishBears|1 year ago
How are you struggling with this, let alone as a significant barrier? JSON adherence with a well thought out schema hasn't been a worry between improved model performance and various grammar based constraint systems in a while.
> Another is preventing LLMs from adding intro or conclusion text.
Also trivial to work around by pre-filling and stop tokens, or just extremely basic text parsing.
Also would recommend writing out Stream-Triggered Augmented Generation since the term is so barely used it might as well be made up from the POV of someone trying to understand the comment
benreesman|1 year ago
Might be worth checking out.
lastdong|1 year ago
This is really interesting, is there any architecture documentation/articles that you can recommend?
thallium205|1 year ago
harrisoned|1 year ago
-Regex expressions: ChatGPT is the best multi-million regex parser to date.
-Grammar and semantic check: It's a very good revision tool, helped me a lot of times, specially when writing in non-native languages.
-Artwork inspiration: Not only for visual inspiration, in the case of image generators, but descriptive as well. The verbosity of some LLMs can help describe things in more detail than a person would.
-General coding: While your mileage may vary on that one, it has helped me a lot at work building stuff on languages i'm not very familiar with. Just snippets, nothing big.
int_19h|1 year ago
joe_the_user|1 year ago
The problem I see is, who can an "application" be anything but a little window onto the base abilities of ChatGPT and so effectively offers nothing more to an end-user. The final result still have to be checked and regular end-users have to do their own prompt.
Edit: Also, I should also say that anyone who's designing LLM apps that, rather than being end-user tools, are effectively gate keepers to getting action or "a human" from a company deserves a big "f* you" 'cause that approach is evil.
hubraumhugo|1 year ago
For example, we focused on the boring and hard task of web data extraction.
Traditional web scraping is labor-intensive, error-prone, and requires constant updates to handle website changes. It's repetitive and tedious, but couldn't be automated due to the high data diversity and many edge cases. This required a combination of rule-based tools, developers, and constant maintenance.
We're now using LLMs to generate web scrapers and data transformation steps on the fly that adapt to website changes, automating the full process end-to-end.
obiefernandez|1 year ago
bbischof|1 year ago
I’d you’re interested in using one of the LLM-applications I have in prod, check out https://hex.tech/product/magic-ai/ It has a free limit every month to give it a try and see how you like it. If you have feedback after using it, we’re always very interested to hear from users.
unknown|1 year ago
[deleted]
cqqxo4zV46cp|1 year ago