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This is my current drum I bang on when an uninformed stakeholder tries shoving LLMs blindly down everyone’s throats: it’s the data, stupid. Current data aggrega

This is my current drum I bang on when an uninformed stakeholder tries shoving LLMs blindly down everyone’s throats: it’s the data, stupid. Current data aggregates outside of industries wholly dependent on it (so anyone not in web advertising, GIS, or intelligence) are garbage, riddled with errors and in awful structures that are opaque to LLMs. For your AI strategy to have any chance of success, your data has to be pristine and fresh, otherwise you’re lighting money on fire. Throwing more compute and data at the problem won’t magically manifest AGI. To reach those lofty heights, we must first address the gaping wounds holding us back.

FloorEgg 1y ago
Yes, for me both customers and colleagues continually suggested "hey let's just take all these samples of past work and dump it in the magical black box and then replicate what they have been doing". Instead I developed a UX that made it as easy as possible for people to explain what they want to be done, and a system that then goes and does that. Then we compare the system's output to their historical data and there is always variance, and when the customer inspects the variance they realize that their data was wrong and the system's output is far more accurate and precise than their process (and ~3 orders of magnitude cheaper). This is around when they ask how they can buy it. This is the difference between making what people actually want and what they say they want: it's untangling the why from the how.
- marlott 1y ago
  Interesting! Could you give an example with a bit more specific detail here? I take it there's some kind of work output, like a report, in a semi-structured format, and the goal is to automate creation of these. And you would provide a UX that lets them explain what they want the system to create?
  - FloorEgg 1y ago
    Yes, essentially. There are multiple long-form text inputs, one set is provided by User A, and another set by User B. User A inputs act as a prompt for User B, and then User A analyzes User B's input according to the original User A inputs, producing an output. My system takes User A and B inputs and produces the output with more accuracy and precision than User As do, but a wide margin. Instead of trying to train a model on all the history of these inputs and outputs, the solution was a combination of goal->job->task breakdown (like a fixed agentic process), and lots of context and prompt engineering. I then test against customer legacy samples, and inspect any variances by hand. At first the variances were usually system errors, which informed improvements to context and prompt engineering, and after working through about a thousand of these (test -> inspect variance -> if system mistake improve system -> repeat) iterations, and benefiting from a couple base-model upgrades, the variances are now about 99.9% user error (bad historical data or user inputs) and 0.1% system error. Overall it took about 9 months to build, and this one niche is worth ~$30m a year revenue easy, and everywhere I look there are market niches like this... it's ridiculous. (and a basic chat interface like ChatGPT doesn't work for these types of problems, no matter how smart it gets, for a variety of reasons) So to summarize: Instead of training a model on the historical inputs and outputs, the solution was to use the best base model LLMs, a pre-determined agentic flow, thoughtful system prompt and context engineering, and an iterative testing process with a human in the loop (me) to refine the overall system by carefully comparing the variances between system outputs and historical customer input/output samples.
    - marlott 1y ago
      Thanks a lot for the detailed reply! Makes a lot of sense now. I'm working on similar problems, and have dabbled with this kind of approach.
Workaccount2 1y ago
They'll pay academics to create data, in fact this is already happening.
PLenz 1y ago
I've worked in 2 of those domains (I was a geographer at a web advertising company) and let me tell you, the data is only slightly better than the median industry and in the case of the geodata from apps I'd say it's far, far, far worse.
jandrewrogers 1y ago
I have bad news about the quality of the data in geospatial, intelligence, and advertising.

FloorEgg 1y ago

Yes, for me both customers and colleagues continually suggested "hey let's just take all these samples of past work and dump it in the magical black box and then replicate what they have been doing". Instead I developed a UX that made it as easy as possible for people to explain what they want to be done, and a system that then goes and does that. Then we compare the system's output to their historical data and there is always variance, and when the customer inspects the variance they realize that their data was wrong and the system's output is far more accurate and precise than their process (and ~3 orders of magnitude cheaper). This is around when they ask how they can buy it. This is the difference between making what people actually want and what they say they want: it's untangling the why from the how.

marlott 1y ago
Interesting! Could you give an example with a bit more specific detail here? I take it there's some kind of work output, like a report, in a semi-structured format, and the goal is to automate creation of these. And you would provide a UX that lets them explain what they want the system to create?
- FloorEgg 1y ago
  Yes, essentially. There are multiple long-form text inputs, one set is provided by User A, and another set by User B. User A inputs act as a prompt for User B, and then User A analyzes User B's input according to the original User A inputs, producing an output. My system takes User A and B inputs and produces the output with more accuracy and precision than User As do, but a wide margin. Instead of trying to train a model on all the history of these inputs and outputs, the solution was a combination of goal->job->task breakdown (like a fixed agentic process), and lots of context and prompt engineering. I then test against customer legacy samples, and inspect any variances by hand. At first the variances were usually system errors, which informed improvements to context and prompt engineering, and after working through about a thousand of these (test -> inspect variance -> if system mistake improve system -> repeat) iterations, and benefiting from a couple base-model upgrades, the variances are now about 99.9% user error (bad historical data or user inputs) and 0.1% system error. Overall it took about 9 months to build, and this one niche is worth ~$30m a year revenue easy, and everywhere I look there are market niches like this... it's ridiculous. (and a basic chat interface like ChatGPT doesn't work for these types of problems, no matter how smart it gets, for a variety of reasons) So to summarize: Instead of training a model on the historical inputs and outputs, the solution was to use the best base model LLMs, a pre-determined agentic flow, thoughtful system prompt and context engineering, and an iterative testing process with a human in the loop (me) to refine the overall system by carefully comparing the variances between system outputs and historical customer input/output samples.
  - marlott 1y ago
    Thanks a lot for the detailed reply! Makes a lot of sense now. I'm working on similar problems, and have dabbled with this kind of approach.

Workaccount2 1y ago

They'll pay academics to create data, in fact this is already happening.

PLenz 1y ago

I've worked in 2 of those domains (I was a geographer at a web advertising company) and let me tell you, the data is only slightly better than the median industry and in the case of the geodata from apps I'd say it's far, far, far worse.

jandrewrogers 1y ago

I have bad news about the quality of the data in geospatial, intelligence, and advertising.