The Four Step Process to Loop Engineer ANYTHING (+ Why Prompt Engineering Isn't Dead)
CChase AI
Computing/Software
Transcript
00:00:00The conversation around loop engineering has completely lost the plot.
00:00:04Every single YouTube video in XThread has the exact same take.
00:00:08Prompt engineering is dead, and you should be doing nothing else but loop engineering.
00:00:12But the problem is, this is not only completely wrong, it's utterly backwards.
00:00:17And that's because a loop at its core is still a prompt.
00:00:20It's just a prompt that we are repeating over and over again with some additional scaffolding.
00:00:25And loops, just like a prompt, are simply tools.
00:00:29Just because we discovered what a wrench is yesterday doesn't mean we throw out the screwdriver.
00:00:34Each one has their place, and it's on you to understand when they fit the job.
00:00:39So in this video, we're going to cut through the hype, and I'm actually going to explain
00:00:42what you need to know about loop engineering, when we should be using them,
00:00:46how to actually build them, and what use cases make sense.
00:00:49So let's begin by defining loop engineering.
00:00:52Loop engineering is the idea that I'm going to go to CloudCode, Codex,
00:00:55whatever agent decoder I'm using, and I'm going to give it some sort of task.
00:00:58And instead of just saying, here's the prompt, go do it, I'm going to set this up in a way
00:01:03that it is going to complete the task by looping, by iteratively, over and over again,
00:01:09trying to complete this task until it hits some sort of defined success criteria.
00:01:15I'm going to say, hey, this is how you know you've done it right.
00:01:18And loop engineering is all about setting up this loop in a way that makes it efficient and
00:01:24effective and that it makes sense.
00:01:25That's it.
00:01:26And at its core, what is it?
00:01:28It's prompts.
00:01:29It's prompts stacked on top of prompts that we do over and over and over again until we
00:01:33complete the task.
00:01:34So the idea that prompt engineering is dead is a total misnomer, because at its core,
00:01:39it's just a bunch of prompts stacked on top of one another.
00:01:41That's it.
00:01:42And the rest of this video is going to be about, hey, how do we actually set up a loop
00:01:47in a way that makes sense to complete these sort of tasks?
00:01:49And furthermore, what tasks actually make sense to be, you know, attacked with loops?
00:01:56Because they don't always need to be done that way, obviously.
00:02:00Which again, is another hit to this whole like prompt engineering is dead thing.
00:02:03This isn't a one size fit all tool.
00:02:04We don't need to create a loop for everything, but sometimes we do.
00:02:07So it's a good thing to know.
00:02:08Now, every loop has four phases.
00:02:09We have the trigger phase, execution phase, the verification phase, and then state before
00:02:14it loops over and does it again.
00:02:16Now, phase number one is the trigger, and that's pretty self-explanatory.
00:02:19How are we actually going to get this thing kicked off and started?
00:02:21We have a number of options.
00:02:22We could do things like schedule tasks or routines inside of clawed code.
00:02:26I can make it a cron job, a web hook, whatever.
00:02:28Doesn't really matter.
00:02:29You just need some way to actually get it started.
00:02:31Because again, we want this to be automatic, ideally.
00:02:35Phase number two is the execution phase.
00:02:37This is where AI is actually doing stuff for us, usually in some sort of coding manner.
00:02:41But either way, we probably want this to be some sort of skill.
00:02:45Because skills are perfect for telling clawed code to do a specific thing in a specific way
00:02:51to get a specific output.
00:02:53And the whole idea of the loop is we're going for a specific output, which leads us into phase
00:02:59number three, which is the goal, the verification.
00:03:02Really, this is all about success criteria.
00:03:08Success criteria.
00:03:09What do I mean by success criteria?
00:03:11How do we know we actually completed this?
00:03:14This is a serious question and one people talk to, but then they give you examples where
00:03:18it doesn't make any sense.
00:03:19Okay?
00:03:19So when we talk about success criteria, sometimes it can be very clear.
00:03:23The success criteria for something like, I don't know, a Python application, and the goal
00:03:30is to make it run faster is very obvious.
00:03:33Well, it's runtime.
00:03:34Okay?
00:03:34So we could have a loop that over and over again tries to reduce its runtime.
00:03:38That's a clear goal with objective success criteria.
00:03:43However, not everything is like that.
00:03:45What if we are doing some sort of loop that has to do with content creation or we're trying
00:03:49to create LinkedIn articles?
00:03:51Okay?
00:03:51Part of our loop is we want to consistently go out on the web, find things about AI, turn
00:03:57it into a LinkedIn article, and then over time with this loop, create better LinkedIn articles.
00:04:03Well, how do we create better LinkedIn articles?
00:04:07Do you know?
00:04:08What is success here?
00:04:09Is it engagement?
00:04:10Is engagement always perfectly tied to how quote unquote good an article is?
00:04:16You know, it's very fuzzy.
00:04:18So you could have a loop with sort of like fuzzy success criteria, but understand it reduces
00:04:24its effectiveness if that's the case.
00:04:25And we'll talk a little bit more about what we can do if we have sort of, again, fuzzy goals
00:04:31here that aren't like, oh, a number that's very clear and obvious that we need to improve
00:04:35upon.
00:04:36And then phase four, what do we got?
00:04:37We have state.
00:04:38It's the idea of output and memory because loop engineering, the real sell for loop engineering
00:04:45is that we can have it improve upon itself every single loop and over time, improve upon
00:04:51itself every single run.
00:04:53You know, think again to the Python idea.
00:04:57Okay, the Python idea where we want to reduce the runtime, right?
00:05:01We want to make it faster.
00:05:03Well, what good is the loop if every loop is sort of in a silo and it's just trying different
00:05:08things every time?
00:05:08No, it needs some sort of document or database that it can look at and see, oh, this was the
00:05:15previous runtime.
00:05:16Here's the things we tried to reduce the runtime.
00:05:19Here's what worked.
00:05:20Here's what didn't.
00:05:21Okay, now I know what I need to try.
00:05:23Should sound very reminiscent to RALF loops.
00:05:27You know, a lot of loop engineering kind of goes back on these RALF loop concepts.
00:05:33So we need in any proper loop some way to figure out what our output was.
00:05:38We need to be able to record it.
00:05:40And for follow-on loops, this execution phase needs to be able to look and see, oh, here's
00:05:46what I did.
00:05:47Here's what worked.
00:05:48Here's what didn't, right?
00:05:49That's the only way you're going to make it self-improving in any sense of the word.
00:05:52And the last portion, which really isn't a phase, but it's part of it, is like, what
00:05:56is the stop criteria?
00:05:58When do we stop looping anymore?
00:06:01Now, in some cases, it's like, okay, we hit the goal.
00:06:03It's verified.
00:06:04Boom, we're done.
00:06:05But do you want it to just keep running and running and running and running and running
00:06:08and running and running until that happens?
00:06:11Probably not because, you know, AI isn't free.
00:06:15So do we want to have some sort of like hard stop built in, whether that's, hey, we're not
00:06:18getting more progress.
00:06:19Like maybe the Python runtime just isn't going down enough.
00:06:24Or maybe we have a hard stop like, hey, we're going to do eight iterations, you know, and
00:06:28then we'll kind of call it, these are the kind of things you need to think about, right?
00:06:33And so while loop engineering, like I said at the beginning, is relatively simple from
00:06:37a theoretical point of view, when we do get into the nitty gritty of like how we define
00:06:41the phases and engineer these loops themselves, there is some nuance and there is some questions
00:06:47you need to be able to answer.
00:06:48And that can be a lot to take on all at once.
00:06:51It can be kind of confusing.
00:06:52So if you get nothing else from this video, what I want you to think about really is the
00:06:57success criteria.
00:06:58And this will also play into the idea of does this task actually make sense to be part of
00:07:05a loop format?
00:07:07If the task you have is something that has very clear success criteria, especially if it's
00:07:14objective, like a number, then loops are great.
00:07:17Loop engineering is awesome.
00:07:18If that is not the case, if it's fuzzy, again, think of our LinkedIn article thing.
00:07:23Maybe, maybe it still makes sense.
00:07:26Maybe we need to have you more human in the loop at this part.
00:07:30Maybe there needs to be some sort of like hybrid approach, but that's just something you need
00:07:33to think of it going in because if you don't have a strong goal and you don't have clear
00:07:36success criteria, this is all pointless and you're just gonna be spinning your wheels and
00:07:39burning tokens.
00:07:40So just know that going in.
00:07:42If you get nothing else, success criteria, think about it.
00:07:46Now, before we dive into how you should go about setting up your own loops and your own personal
00:07:50loop engineering, a quick word from today's sponsor, me.
00:07:54So I just released my Claude code masterclass and it is the perfect place to go from zero to AI dev,
00:07:59especially if you don't come from a technical background.
00:08:02I update this every single week and it also includes a codex masterclass and an agentic
00:08:07OS masterclass.
00:08:09You can find it inside of chase AI plus there is a link to that in the pin comment.
00:08:13Now, real quickly, before we go into your own personal loop engineering sort of workflow,
00:08:17want to talk really quick about things like auto research and also forward slash goals,
00:08:21because you might've watched everything up until now and been like, well, why don't we
00:08:23just use something like Carpathy's auto research?
00:08:26Why don't we just use forward slash goals, which is a part of Claude code?
00:08:28Well, first of all, auto research is still great.
00:08:30A lot of what we talked about loop engineering is pretty much what something like auto research
00:08:35does automatically.
00:08:36The thing is though, when it comes to something like auto research, it explicitly needs that defined
00:08:41success criteria.
00:08:42Like we talked about, it cannot do fuzzy things.
00:08:46We can do loop engineering inside of Claude code with somewhat fuzzy success criteria.
00:08:50Not in the case with auto research.
00:08:52So auto research with that Python example, trying to make it faster.
00:08:55Perfect, perfect use case.
00:08:57But if that's not it, we're not talking about an objective, like to the number thing that
00:09:01we're trying to improve.
00:09:02You can't really deal with that with auto research.
00:09:04And when it comes to something like forward slash goal, forward slash goal is sort of loop
00:09:09engineering in a nutshell.
00:09:10It's you telling Claude code, I want to do this certain thing.
00:09:13And I want you to just iterate over and over until you reach a certain condition.
00:09:17The difference between forward slash goal and loop engineering at large is that forward slash
00:09:21goal, and this also applies to codex, is something in a single session, right?
00:09:26We're going to just complete this one thing and that's going to be it.
00:09:29Loop engineering is meant to have like an infinite horizon.
00:09:32It's almost like we're doing forward slash goal all the time.
00:09:36We're looping forward slash goal, right?
00:09:38There's a self-improvement aspect to it.
00:09:39Again, think of something like this LinkedIn article example.
00:09:42I cannot do a forward slash goal that says make me better LinkedIn articles for now and
00:09:47forever, right?
00:09:48It could try to make me one right now, again, in a silo a single time, but if this is something
00:09:54that I want to do every single week over and over, that's not what forward slash goal is
00:09:58for.
00:09:59Loop engineering is bigger picture, if that makes sense.
00:10:02And we'll talk about it a little bit more here.
00:10:04So let's now talk about what your journey should look like when it comes to loop engineering.
00:10:08How should you approach this?
00:10:10You have some sort of task in mind and you want to know, hey, how do I loop engineer this?
00:10:15Well, this is sort of like a hero's journey here you need to follow.
00:10:19And the first step in our journey is a purely manual process.
00:10:23So example, again, this LinkedIn article thing.
00:10:25I want to create LinkedIn articles.
00:10:27Well, what would you do?
00:10:28You would pull up cloud code and you would say research AI stuff and make a LinkedIn article
00:10:35for me.
00:10:37I'm not saying this as a joke.
00:10:39This literally has to be the first step.
00:10:40Why?
00:10:41Because we need to verify that what we're trying to do is even possible and AI can do it.
00:10:45Okay?
00:10:46So that's step one.
00:10:48We're actually making sure we can do this manually and we're being very, very hands-on.
00:10:52Once we've confirmed that we can actually do this and it's something we're going to want
00:10:55to improve upon in the future, well, we're going to codify it.
00:10:58So step two becomes turning it into a skill because nobody wants to sit there and say, hey,
00:11:04do all A, B, and C over and over again.
00:11:06I have a specific outcome I now have and I want to do it in a specific way.
00:11:10So we would turn this into a skill.
00:11:14That's step two.
00:11:15And again, this is where kind of a lot of people sit.
00:11:18Unfortunately, a lot of people really just sit on step one forever, which is manual.
00:11:21So we've validated the process.
00:11:23We've codified it into a skill.
00:11:25The next step is actually just to automate it, right?
00:11:28We just want to automate the skill because I'm so lazy.
00:11:30I don't even want to write forward slash LinkedIn article.
00:11:33I want to just do it on its own.
00:11:34Now, this is pretty easy to do in something like Cloud Code.
00:11:37We can go into routines.
00:11:39We can set up an automation called LinkedIn article.
00:11:42And in the instructions, we can just say, run the LinkedIn article skill, right?
00:11:48Run the description, run the skill.
00:11:51And hey, we're already going to figure out the trigger.
00:11:52We just schedule it however we want.
00:11:54We're going to do it daily at 9 a.m.
00:11:56And so before we even really got into the loop engineering part, we've sort of already figured
00:12:01out the trigger and kind of done part of the execution.
00:12:06So if we then want to go from this automated skill into a true, you know, loop engineered
00:12:11construct, well, what are we going to need to do now?
00:12:14Well, now we need to think about self-improvement.
00:12:17We need to think about success criteria.
00:12:19And we need to think about state, right?
00:12:22What is the definition of success?
00:12:24How are we going to record and therefore improve upon it?
00:12:27So when we're at step three, we're now thinking about this entire second half.
00:12:32And so moving up here, this skill is probably working, but we need to add some things to
00:12:37this skill before we move on to step four.
00:12:40And so what are we going to add?
00:12:42Well, we need to add the success stuff we talked about, right?
00:12:46And we also need to add some sort of like state logging.
00:12:53Again, state logging.
00:12:54What am I saying?
00:12:55Where is this information going?
00:12:57Sure, you posted to LinkedIn, but are you able to scrape the engagement statistics?
00:13:01Because let's say we say success is defined by engagement statistics.
00:13:05We'll just say likes.
00:13:06Well, we need some way to harvest those likes, see what the metrics are, and then we need to
00:13:11put them somewhere.
00:13:12And it is by that that we can then further improve upon this stuff and pull out, hey, here's
00:13:17what worked with this article.
00:13:18Here's what didn't.
00:13:19This hook was good.
00:13:20This hook wasn't bad.
00:13:20This CTA worked, et cetera, et cetera.
00:13:23So while before in the original step one, two, and three, we didn't have that, if we want
00:13:27to move on to step four, which is loop engineering, what do we need?
00:13:31We need these two things.
00:13:32And this applies to anything you do, right?
00:13:36And now comes the question at this point, well, do we even need step four?
00:13:40If you can define success in some way, even if it's sort of fuzzy like this, and you have
00:13:44a way to record the state, then you're going to be okay.
00:13:47Now, let's talk about the success criteria a little bit more, because I think there's sort
00:13:51of like five tiers of verification here.
00:13:54First three are kind of where we want to live.
00:13:56And this is like, hey, you have success criteria that's very clear, right?
00:14:00Ideally, it's deterministic.
00:14:01It's like a yes or no, like that's perfect.
00:14:04Or there's some sort of like rule or constraint, right?
00:14:06When we talked about the Python application running fast, well, that's sort of like a rule
00:14:10or constraint we're trying to improve upon.
00:14:12But if we don't have those, and we're kind of in like tier three through five, where again,
00:14:15it's fuzzy, this is where you need to start thinking, how do I judge the success?
00:14:20Now, if we have something like likes or engagement, that is a number, and that kind of puts us
00:14:24in number three, right?
00:14:26And if you're happy with that, you can continue to make this completely automatic.
00:14:29But if there's something that does require some nuance and judgment in terms of what is
00:14:33good, you need to have like kind of a discussion between you and yourself and probably cloud
00:14:38code of like, okay, are we going to have the large language model as the judge, right?
00:14:44If cloud code is the one who is writing the articles, do we want cloud code to judge the articles?
00:14:51The answer is probably not.
00:14:52You may want to create something in your loop where something like codex comes in.
00:14:56And takes a look at it.
00:14:57Like I have a whole video on things like this, where we use codex to sort of judge cloud
00:15:01codes outputs.
00:15:01Because remember, one of the issues with cloud code and really all AI systems is they tend
00:15:05to really like their own work.
00:15:07So anytime you're like, oh, I'm going to have the AI judge something in my loop.
00:15:11Be careful, especially if subjective.
00:15:14The other option you have is you bring in you into the loop.
00:15:18Now this makes it less autonomous.
00:15:19And this is where you begin to question, does this actually need to be a loop?
00:15:23But it might make sense.
00:15:24There are scenarios where we need some sort of human intervention.
00:15:28And this can be the most powerful, right?
00:15:31Especially in our example of like LinkedIn articles, like, was it good?
00:15:34Did the engagement make sense for the topic I spoke on?
00:15:37You know, you can have an article with tons of engagement and it can have nothing to do
00:15:40with the quality of the article.
00:15:41It was just the timing and the subject that worked well for you.
00:15:44And it's like, do we want to necessarily pull, you know, the information about how we wrote
00:15:49said article as like this gold standard for things going forward?
00:15:51Again, a lot of nuance.
00:15:53There's a lot of nuance.
00:15:54You know, these are the decisions you need to make.
00:15:56And this is what is going to define if your loop is engineered correctly or not.
00:16:00And there's no perfect answer here as well.
00:16:02This is all case by case.
00:16:03And it's something that's going to require experimentation on your part to figure out.
00:16:07But for our example, for now, we're going to say, okay, we're going with likes.
00:16:11That's good enough for us.
00:16:11If it has a lot of likes, we're saying that's a good article.
00:16:14And that's what we're going to base this all on.
00:16:15So when we look at our loop now, we have a trigger at 9 a.m., we have an execution via
00:16:20a skill, we've defined our goal as getting the most likes as possible.
00:16:27We're able to verify this with some sort of scraper, and we're able to put all this into
00:16:33some sort of database that records the article with the amount of likes.
00:16:40Now, this will then loop essentially every day at 9 a.m.
00:16:44Now, you might notice some issues here right away, because with this thing, this isn't going
00:16:49to just be one loop, is it?
00:16:52Because there's going to be a delay between, hey, when I write the article to where I get
00:16:58likes.
00:16:59So there's also going to be a delay for how well this actually works in reality.
00:17:02We're going to have to wait some time and build a database of actual data showing our articles
00:17:08and likes.
00:17:09Or this video, let's pretend we've sort of been running this for like a month, and we already
00:17:14have a treasure trove of like, here's articles I've written with the likes.
00:17:18So the idea would be, at this point, every morning at 9 a.m., we get the trigger, the skill
00:17:25executes, it looks up things for AI, and it begins to write the article.
00:17:30Now, what it's also going to bring in isn't just AI news, right?
00:17:35It's now going to look at that database of previous articles and previous likes and sort
00:17:40of just some analysis, like what's been trending lately?
00:17:43What did we try in terms of hooks?
00:17:45What was our CTA, et cetera, et cetera?
00:17:48It will then bring in that information into its execution, and that would be all baked into
00:17:52the skill.
00:17:53That's sort of the self-improvement part.
00:17:55From there, hey, it sees what it did.
00:17:59It records the likes.
00:18:01Boom, boom, boom, boom, boom.
00:18:02In reality, you would also have a second loop running outside that just like scrapes the likes
00:18:06every, you know, 24 hours and make sure it's updated.
00:18:11And you can see right away, this sort of fuzzy thing with the likes does increase the
00:18:15complexity of how we engineer our loops versus something as simple as like, hey, I want my
00:18:21Python app to be faster.
00:18:23Okay, well, this triggers, you know, every 10 minutes, it runs the app.
00:18:29We want it to be faster, so it checks the time.
00:18:33It has some sort of handoff doc that has the times with the code changes, right?
00:18:40We just have the diff there, and then it just keeps running over and over and over and over
00:18:45again, right?
00:18:46And it changes the code each time to see if it lowers the time and sees what diffs has worked.
00:18:51So all's that to say, if you have clear success criteria, loop engineering becomes much, much
00:18:56easier.
00:18:57And hopefully this thoroughly confused you at this point.
00:19:01But I thought it was good to kind of go through this sort of like LinkedIn chaos fuzzy thing
00:19:07because truth be told, for a lot of people who use cloud code and want to do these loops,
00:19:11they actually tend to be in this place more often than not in sort of this fuzzy area.
00:19:15Not everything is clearly defined as, you know, something you could throw into auto research.
00:19:20So that is loop engineering in a nutshell.
00:19:23We have a trigger.
00:19:24We're going to execute via skills.
00:19:26It's all about our goals and can we verify our success?
00:19:29And then we're logging everything all the time so that this becomes a self-improving loop.
00:19:34And ideally, at the execution phase with that skill, it needs to be looking at its previous
00:19:40state and figuring out, okay, what have we tried?
00:19:44What do we still need to try?
00:19:45What's worked?
00:19:46What hasn't?
00:19:47So that's where I'm going to leave you.
00:19:48As always, let me know what you thought of this video.
00:19:51I think it's a super interesting topic, but don't get lost and confused by everyone saying
00:19:55prompt engineering is dead.
00:19:56That is not the case.
00:19:58And make sure to check out Chase AI Plus if you want to get your hands on my Cloud Code
00:20:02Masterclass.
00:20:02But besides that, I'll see you around.