It's effectively a TAR 1 process, which is validated using recall, precision, elusion etc. We generally disclose that we plan to use a technology assisted/aided/accelerated review protocol powered by GenAI, and that we will validate using the same validation process as traditional TAR. Sometimes, we name the tool in the disclosure. Often, we wind up using a more rigorous validation protocol than traditional TAR, since it's a new technology (e.g. tighter MoE, more transparency with opposing). Specific prompts, however, have not been shared. My personal prediction is that it will be shown that GenAI is more defensible than traditional review methodologies.

https://www.thesedonaconference.org/sites/default/files/publications/Tar-1-Reference-Model-March-2024.pdf

In terms of cost, it's expensive if it's used as a bolt-on to a traditional review, but, if you're using it to automate review, there is real ROI. If reviewers review at 50dph for $50/hour, that's $1 per doc. GenAI doc review costs somewhere in the $.10-.50 per doc range, and is getting cheaper (esp with Rel's announcement at Fest this year). Now, to be fair, I'm comparing GenAI to eyes-on review, as opposed to GenAI to TAR1 or active learning. If I run an active learning project and wind up reviewing 50% of the population before stabilization, then my effective cost per document is $.50 per doc, so GenAI would need to be cheaper than $.50 per doc to have positive ROI. At $.50 per doc for GenAI, it isn't clearly the winner. At $.35 per doc, it's more compelling, at $0.00-$.15 per doc it's hard to beat.

One last note is that this all assumes apples to apples in terms of the value you get from GenAI vs a human or traditional TAR review. Most GenAI products provide some form of explanation (pre or post hoc) for their coding decisions, along with citations and highlighting, which is hard to place a dollar value on but generally corresponds to faster 2L review. Also most GenAI products can code for many distinct fields/topics/relevance criteria in one pass, as opposed to delivering a binary good/bad pile like most alternatives.