Thanks for your interest. I (very respectfully!) disagree that most, or indeed that any, of my decisions aren't worth recording. I have an explicit "completist" attitude to epistemic materials (records).
First, we have LOTS of space now and decision records are cheap: We now can get several terabytes for a hundred or a couple hundred dollars. Storing the record of decisions is a drop in the bucket. In practical terms, taking the time to specify project-specific records helps ensure adherence and faithfulness from models; but very often, I've found the cross-projects benefits are also extraordinary (more on that in the third point!).
Second, as I mentioned in some other comments, the methodology instructs models to surface all uncertainties and all doubts (Secondary Directive) and to commit all discussions (Prime Directive), but to surface these as ORRCF-formatted questions, with ORRCF (pronounced ORR-ssef) = Options, Recommendation, Rationale, Confidence, Falsifier. It's a way of surfacing doubts where your cognitive load is reduced, because everything you need to think about is brought up in the same screen, and you get to either endorse to recommendation knowingly, or override it with exactly what needs to be captured in disagreement.
Third, when I disagree with the model's recommendation (or even when I agree, but have extended requirements, caveats, ideas, etc.) everyone of my decisions is fully thought-out: When I make decisions of deployment platforms based on predicted economics factors, or when I decide not to use Meta-created or Meta-related libraries for ethical reasons, or when I make decisions about modal grouping in user interfaces, or principles under which the user must be guaranteed never to have drafts clobbered, those are all worth capturing. All of these decisions compound, in the sense that, with the right indexing they can be referred to in the future: I can point to a set of records rather than specify, or I can have the model send a sub-agent to send a cluster of past projects for relevant decision records.
At least, for me (perhaps because of my neurotype), it is more enjoyable and efficient to think deliberately slowly about each decision, to fully specify my thinking factoring everything I know — and to do so, knowing that now it will pay off in the sense that I will never have to explain myself again because I've put down my best thinking on this problem and can cite it forever in my future projects.
I am decoupling thinking and doing, and it's really compounding over time, and producing sustainable epistemic scaffolding to my projects.
To be very concrete, here's the loop of work in this model:
- You do an audio braindump of your thoughts
- Models iterate with you until it is well-defined enough that they can put together an initial draft of one or more ADRs (possibly 10-20 if you are bootstrapping a project)
- Any more-granular tensions are now surfaced to you as ORRCF-formatted QST questions, and you get to look at them in whatever order you want (including in orders suggested by models through the QuestionTour feature)
- You look at the questions and most of the times approve the recommendation, but occasionally, there's an opportunity for you to inject a zest of what makes you, Selkirk, your special epistemic patina, and you override the recommendation with something new, bold, beautiful, unique the models couldn't have foreseen even with what they are starting to know, through the previous records, about you
- After answering, the models, build, build, build, build, build, autonomously, and only surface for air if there are new tensions that surface
- Meanwhile as you see the results take shape, you are hungry for more and start brain dumping more ideas, which they structure, surface questions, which you answer, they build, iterate, and so on
- The records are what the models understand and what they need you for, it's not a post-hoc ceremony, it speeds up the process!
I hope this makes sense?
First, we have LOTS of space now and decision records are cheap: We now can get several terabytes for a hundred or a couple hundred dollars. Storing the record of decisions is a drop in the bucket. In practical terms, taking the time to specify project-specific records helps ensure adherence and faithfulness from models; but very often, I've found the cross-projects benefits are also extraordinary (more on that in the third point!).
Second, as I mentioned in some other comments, the methodology instructs models to surface all uncertainties and all doubts (Secondary Directive) and to commit all discussions (Prime Directive), but to surface these as ORRCF-formatted questions, with ORRCF (pronounced ORR-ssef) = Options, Recommendation, Rationale, Confidence, Falsifier. It's a way of surfacing doubts where your cognitive load is reduced, because everything you need to think about is brought up in the same screen, and you get to either endorse to recommendation knowingly, or override it with exactly what needs to be captured in disagreement.
Third, when I disagree with the model's recommendation (or even when I agree, but have extended requirements, caveats, ideas, etc.) everyone of my decisions is fully thought-out: When I make decisions of deployment platforms based on predicted economics factors, or when I decide not to use Meta-created or Meta-related libraries for ethical reasons, or when I make decisions about modal grouping in user interfaces, or principles under which the user must be guaranteed never to have drafts clobbered, those are all worth capturing. All of these decisions compound, in the sense that, with the right indexing they can be referred to in the future: I can point to a set of records rather than specify, or I can have the model send a sub-agent to send a cluster of past projects for relevant decision records.
At least, for me (perhaps because of my neurotype), it is more enjoyable and efficient to think deliberately slowly about each decision, to fully specify my thinking factoring everything I know — and to do so, knowing that now it will pay off in the sense that I will never have to explain myself again because I've put down my best thinking on this problem and can cite it forever in my future projects.
I am decoupling thinking and doing, and it's really compounding over time, and producing sustainable epistemic scaffolding to my projects.
To be very concrete, here's the loop of work in this model: - You do an audio braindump of your thoughts - Models iterate with you until it is well-defined enough that they can put together an initial draft of one or more ADRs (possibly 10-20 if you are bootstrapping a project) - Any more-granular tensions are now surfaced to you as ORRCF-formatted QST questions, and you get to look at them in whatever order you want (including in orders suggested by models through the QuestionTour feature) - You look at the questions and most of the times approve the recommendation, but occasionally, there's an opportunity for you to inject a zest of what makes you, Selkirk, your special epistemic patina, and you override the recommendation with something new, bold, beautiful, unique the models couldn't have foreseen even with what they are starting to know, through the previous records, about you - After answering, the models, build, build, build, build, build, autonomously, and only surface for air if there are new tensions that surface - Meanwhile as you see the results take shape, you are hungry for more and start brain dumping more ideas, which they structure, surface questions, which you answer, they build, iterate, and so on - The records are what the models understand and what they need you for, it's not a post-hoc ceremony, it speeds up the process!
I hope this makes sense?