AI promises to transform content conversion, but what does it actually look like when you’re processing thousands of documents a day? In this episode, Sarah O’Keefe (Scriptorium) and Rich Dominelli (DCL) dig into the real-world challenges of using AI for large-scale structured content conversion.

Rich Dominelli: If you have millions of articles and you’re asking the AI, ‘What did we do for this project six months ago?” The AI has to find those articles, pull the relevant information out of those articles, summarize it, and hand it back to you. The best way of doing that is to give extra signals to the AI, structured relevant bits of information, front matter, back matter, publication date, keywords, abstract, that allows the AI to query the corpus and get the relevant chunks out of that corpus in a very quick manner. Then, it can summarize what those chunks are. So the AI almost becomes the user interface over that corpus. But to find that data in the first place, structured content is key. Structured content is key when you’re dealing with big indexes and the web, and it’s the same with AI.

Related links:

Defeating Nondeterminism in LLM Inference (white paper)
Data Conversion Laboratory (DCL)
Scriptorium, Machine experience (MX): Making content work for humans and machines (podcast)

LinkedIn:

Host: Sarah O’Keefe
Guest: Rich Dominelli

Transcript:

Disclaimer: This is a machine-generated transcript with edits.

Introduction with ambient background music

Christine Cuellar: From Scriptorium, this is Content Operations, a show that delivers industry-leading insights for global organizations.

Bill Swallow: In the end, you have a unified experience so that people aren’t relearning how to engage with your content in every context you produce it.

Sarah O’Keefe: Change is perceived as being risky; you have to convince me that making the change is less risky than not making the change.

Alan Pringle: And at some point, you are going to have tools, technology, and processes that no longer support your needs, so if you think about that ahead of time, you’re going to be much better off.

End of introduction

Sarah O’Keefe: Hey everyone, I’m Sarah O’Keefe and I’m here today with Rich Dominelli who is a Senior Developer and Architect at DCL. Rich, welcome.

Rich Domineli: Hi, thank you for having me.

SO: Glad to have you. We were talking before we hit the record button, and you described yourself as a perhaps hopeful AI evangelist.

RD: Yeah, I am well and thoroughly immersed in the AI game at DCL and using it and plus I play with AI assistants at home. I’m enthusiastic about the future of AI, sometimes disappointed about the present.

SO: So DCL, as I think many of our listeners know, is focused on conversion at scale, which to me makes a great use case for AI because ultimately conversion is about edge cases and about inconsistency, right? If everything was 100% consistent, conversion would be pretty easy.

RD: Yeah, no, DCL does a lot of structured content generation out of unstructured data, and the creativity, especially in the academic space, of what that unstructured data looks like is sometimes nightmarish. So the AI lets us, does a lot of the heavy lifting for us when it comes to looking for particular items, identifying concrete data points within the documents, pulling things like authors and affiliation, front matter type information, and back matter type information out of the documents and in automated fashion. It can be painful from time to time, but it’s definitely helped.

SO: Yeah, so this is, think, you know, the reality of working with AI and working with it in a production environment in order to address all these weird edge cases and what’s going on. So tell us a little bit about how you’re using AI in, you know, these conversion use cases. What does it look like to go in there and start applying some of these tools that we have?

RD: So, I mean, typically our flows work in a way where we’re coming in with a PDF or a Word document or some other unstructured format. We take it, we reformat it into a version that’s more AI-friendly, like Markdown, for example. And that’s usually the first step we’re doing when we’re looking for information to pull out of it like front matter. It’s a very common use case.

If you look at academic papers, the front matter, the authors and the affiliations that are on that paper can be formatted in more ways than I could list out during the course of this podcast. It’s kind of crazy. So what we’ve started doing, and we’ve been doing this for a couple of years now, is we’re using the AI, we’re handing it the Markdown document, and we’re saying we need to list authors and affiliations, please extract it for us.

Now, naively, when we started that process, we assumed that the AI would give us a consistent list of authors and affiliations. And sometimes it does. But every time you do that call, you’ll get it in a different format. So then you have to start tightening things down. So OK, give me a list of authors and affiliations. I want it to be structured exactly like this. And typically, we have a JSON structure that we’re presenting to the AI, along with our prompt, and saying, give it to us. Well, okay, and that gets you a good chunk of the way there. And that was very exciting when we had that working consistently, we were getting things out of the system on a consistent basis. Awesome. But then you start looking at the results, and every once in a while, you get an author that was missed, or there would be too many authors on that paper.

We had one test paper, which I loved, which had 600 collaborative authors in it. And the AI would just choke after about 280-ish. So then you have to start dealing with things like paging through the data and formatting the data. And then you have to figure out, well, did it miss anything? You have 600 authors. Good luck. So now you have to take what the AI did and compare it against your own representation of it and write a program to do that comparison to say, OK, is it good? Is it good?

You have to take a step back and you look at it and you say, okay, we have the information that’s in the non-structured format. We’re handing it to the AI. The AI is gonna give us a structured version of it and we need to validate it. Well, the first validation is very easy. Does that structured version match the schema that we gave it? Yes or no, that’s easy. Well, then you have to say, okay, is everybody there? Well, is there anybody added? Because the nice thing about AI is they occasionally get very creative. Even if you have that temperature dial turned all the way down to zero, it will pull names out of thin air and then come back to you with some random name and stick it in the middle of the data where it’s not obvious, of course, and then hand it back to you. So then you have to start saying, are all the names that appear in this list actually in the document? Are the counts matching? And if it’s not, you go back to the AI and you ask it again, and usually you’ll get a better answer the second or sometimes the third or fourth time.

But you need to be able to catch that, especially if you’re doing this at scale, because if you’re doing a few, it’s easy, you can eyeball it. If you’re doing 1,000 of these a day, you can eyeball all of them. You can say, you can ask the AI, OK, give me a confidence level, but if you can’t trust it in the first place about what it’s returning, yeah, I’m very confident about what I’m giving you right now. It’s really the truth, I promise you this time. I don’t know how trustworthy that would be. So you have to write tools to validate what the AI is producing, or you have to use the AI to validate what it’s producing. So coming in the first time, obviously, we did the count, we did the schema validation. We then said, okay, we’re going to check to make sure all the names appear in the document, we’re going to have landmarks in the document that we can refer back to. So if you start with Microsoft Word and you have track changes on, you can have paragraph IDs that are supplied. So you can make sure that you can find all of the authors in that list and they all have a paragraph ID and you can have your landmarks and that’s great. Or you can even hand t...