7 ms·

Agreed. I've never seen a concrete answer with an outcome that can be explained in clear, simple terms.

moron4hire 8mo ago
Lately, it seems like all the blogs have shifted away from talking about productivity and are now talking about how much they "enjoy" working with LLMs. If firing up old coal plants and skyrocketing RAM prices and $5000 consumer GPUs and violating millions of developers' copyrights and occasionally coaxing someone into killing themselves is the cost of Brian From Middle Management getting to Enjoy Programming Again instead of having to blame his kids for not having any time on the weekends, I guess we have no choice but to oblige him his little treat.
- wiseowise 8mo ago
  It’s the honeymoon period with crack all over again. Everyone feels great until their teeth start falling out.
jdross 8mo ago
I work in insurance - regulated, human capital heavy, etc. Three examples for you: - our policy agent extracts all coverage limits and policy details into a data ontology. This saves 10-20 mins per policy. It is more accurate and consistent than our humans - our email drafting agent will pull all relevant context on an account whenever an email comes in. It will draft a reply or an email to someone else based on context and workflow. Over half of our emails are now sent without meaningfully modifying the draft, up from 20% two months ago. Hundreds of hours saved per week, now spent on more valuable work for clients. - our certificates agent will note when a certificate of insurance is requested over email and automatically handle the necessary checks and follow up options or resolution. Will likely save us around $500k this year. We also now increasingly share prototypes as a way to discuss ideas. Because the cost to vibe code something illustrative is very low, an it’s often much higher fidelity to have the conversation with something visual than a written document
- hattmall 8mo ago
  Thanks for that. It's a really interesting data point. My takeaway, which I've already felt and I feel like anyone dealing with insurance would anyway, is that the industry is wildly outdated. Which I guess offers a lot of low hanging fruit where AI could be useful. Other than the email drafting, it really seems like all of that should have been handled by just normal software decades ago.
  - mjevans 8mo ago
    A big win for 'normal software' here is to have authentication as a multi-party/agent approval process. Have the client of the insurance company request the automated delivery of certified documents to some other company's email.
- potamic 8mo ago
  > our policy agent extracts all coverage limits and policy details into a data ontology. Are they using some software for this or was this built in-house?
- heyitsguay 8mo ago
  Thanks for this answer! I appreciate the clarity, I can see the economic impact for your company. Very cool.
- ThrowawayTestr 8mo ago
  >our policy agent extracts all coverage limits and policy details into a data ontology Aren't you worried about the agent missing or hallucinating policy details?
  - tonyedgecombe 8mo ago
    Management has decreed that won't happen so it won't.
    - senko 8mo ago
linkjuice4all 8mo ago
Here's some anecdata from the B2B SaaS company I work at - Product team is generating some code with LLMs but everything has to go through human review and developers are expected to "know" what they committed - so it hasn't been a major time saver but we can spin up quicker and explore more edge cases before getting into the real work - Marketing team is using LLMs to generate initial outlines and drafts - but even low stakes/quick turn around content (like LinkedIn posts and paid ads) still need to be reviewed for accuracy, brand voice, etc. Projects get started quicker but still go through various human review before customers/the public sees it - Similarly the Sales team can generate outreach messaging slightly faster but they still have to review for accuracy, targeting, personalization, etc. Meeting/call summaries are pretty much 'magic' and accurate-enough when you need to analyze any transcripts. You can still fall back on the actual recording for clarification. - We're able to spin up demos much faster with 'synthetic' content/sites/visuals that are good-enough for a sales call but would never hold up in production --- All that being said - the value seems to be speeding up discovery of actual work, but someone still needs to actually do the work. We have customers, we built a brand, we're subject to SLAs and other regulatory frameworks so we can't just let some automated workflow do whatever it wants without a ton of guardrails. We're seeing similar feedback from our customers in regard to the LLM features (RAG) that we've added to the product if that helps.
- procaryote 8mo ago
  This makes a lot of sense and is consistent with the lens that LLMs are essentially better autocomplete
deleted 8mo ago
[deleted]

Lately, it seems like all the blogs have shifted away from talking about productivity and are now talking about how much they "enjoy" working with LLMs. If firing up old coal plants and skyrocketing RAM prices and $5000 consumer GPUs and violating millions of developers' copyrights and occasionally coaxing someone into killing themselves is the cost of Brian From Middle Management getting to Enjoy Programming Again instead of having to blame his kids for not having any time on the weekends, I guess we have no choice but to oblige him his little treat.

wiseowise 8mo ago
It’s the honeymoon period with crack all over again. Everyone feels great until their teeth start falling out.

jdross 8mo ago

I work in insurance - regulated, human capital heavy, etc. Three examples for you: - our policy agent extracts all coverage limits and policy details into a data ontology. This saves 10-20 mins per policy. It is more accurate and consistent than our humans - our email drafting agent will pull all relevant context on an account whenever an email comes in. It will draft a reply or an email to someone else based on context and workflow. Over half of our emails are now sent without meaningfully modifying the draft, up from 20% two months ago. Hundreds of hours saved per week, now spent on more valuable work for clients. - our certificates agent will note when a certificate of insurance is requested over email and automatically handle the necessary checks and follow up options or resolution. Will likely save us around $500k this year. We also now increasingly share prototypes as a way to discuss ideas. Because the cost to vibe code something illustrative is very low, an it’s often much higher fidelity to have the conversation with something visual than a written document

hattmall 8mo ago
Thanks for that. It's a really interesting data point. My takeaway, which I've already felt and I feel like anyone dealing with insurance would anyway, is that the industry is wildly outdated. Which I guess offers a lot of low hanging fruit where AI could be useful. Other than the email drafting, it really seems like all of that should have been handled by just normal software decades ago.
- mjevans 8mo ago
  A big win for 'normal software' here is to have authentication as a multi-party/agent approval process. Have the client of the insurance company request the automated delivery of certified documents to some other company's email.
potamic 8mo ago
> our policy agent extracts all coverage limits and policy details into a data ontology. Are they using some software for this or was this built in-house?
heyitsguay 8mo ago
Thanks for this answer! I appreciate the clarity, I can see the economic impact for your company. Very cool.
ThrowawayTestr 8mo ago
>our policy agent extracts all coverage limits and policy details into a data ontology Aren't you worried about the agent missing or hallucinating policy details?
- tonyedgecombe 8mo ago
  Management has decreed that won't happen so it won't.
  - senko 8mo ago

linkjuice4all 8mo ago

Here's some anecdata from the B2B SaaS company I work at - Product team is generating some code with LLMs but everything has to go through human review and developers are expected to "know" what they committed - so it hasn't been a major time saver but we can spin up quicker and explore more edge cases before getting into the real work - Marketing team is using LLMs to generate initial outlines and drafts - but even low stakes/quick turn around content (like LinkedIn posts and paid ads) still need to be reviewed for accuracy, brand voice, etc. Projects get started quicker but still go through various human review before customers/the public sees it - Similarly the Sales team can generate outreach messaging slightly faster but they still have to review for accuracy, targeting, personalization, etc. Meeting/call summaries are pretty much 'magic' and accurate-enough when you need to analyze any transcripts. You can still fall back on the actual recording for clarification. - We're able to spin up demos much faster with 'synthetic' content/sites/visuals that are good-enough for a sales call but would never hold up in production --- All that being said - the value seems to be speeding up discovery of actual work, but someone still needs to actually do the work. We have customers, we built a brand, we're subject to SLAs and other regulatory frameworks so we can't just let some automated workflow do whatever it wants without a ton of guardrails. We're seeing similar feedback from our customers in regard to the LLM features (RAG) that we've added to the product if that helps.

procaryote 8mo ago
This makes a lot of sense and is consistent with the lens that LLMs are essentially better autocomplete

deleted 8mo ago

[deleted]