Start with inline prompts. A dedicated file is premature optimization for a newbie and adds friction when you're still figuring out what works.
Here's why: Your initial goal is to learn the assistant's behavior patterns and what context actually matters. If you bury your prompts in a separate file, you're less likely to iterate quickly. You need the tight feedback loop of typing a prompt directly into the chat and seeing the immediate result.
Do this for your first week:
- Use single, focused prompts right in the chat for a specific task.
- Copy the prompt and the good output to a scratch document.
- Note what failed and how you had to rephrase.
Once you see repetition, *then* formalize. For example, if you always need to remind the assistant about your team's code style, that becomes a candidate for a custom instruction or a snippet in a prompt file.
A basic prompt file only makes sense when you have repeatable patterns. Like this:
```markdown
### Code Review
- Framework: React 18, TypeScript 5
- Style: Airbnb config, functional components, no class components
- Critical: Check for missing error boundaries in async components.
- Output: Provide a bulleted list of actionable fixes.
### Terraform Module Scaffold
- Provider: aws 5.0
- Required: variables.tf, outputs.tf, README.md with example
- Pattern: Use `for_each` over `count` where applicable.
```
But you won't know what to put in that file until you've burned through a few dozen inline prompts. Start simple, get the reps in, then systematize what proves valuable.
-shift
shift left or go home
I mostly agree with starting inline, but there's a specific failure mode I've observed where newcomers don't document their successful prompts at all. The "scratch document" step is critical and often skipped, leading to them relearning the same prompt phrasing weeks later.
A practical middle ground I've used with junior engineers is to enforce a simple convention from day one: paste every final, successful prompt into a single, flat Markdown file with a one-line description. It's not a structured prompt library, it's just a searchable log. This creates the tight feedback loop you advocate for while automatically building the artifact you'll later formalize. The friction is nearly zero, just an extra copy-paste, but it prevents the "what worked last Tuesday?" problem.
The transition to a dedicated, structured prompt file then becomes a refactoring exercise on an existing body of work, not a speculative design task.
data is the product
That's a solid, pragmatic approach. I've seen this work well in our onboarding guides for new community managers. The key is that "scratch document" step - without it, the learning evaporates.
You're right about spotting repetition as the trigger to formalize. One thing I'd add is that the type of repetition matters. If you're repeating the same *task* with different contexts, that's a candidate for a template in a prompt file. But if you're just repeating the same *phrasing* tweak to get a good answer, that's better suited for a custom instruction. It's the difference between "always review code this way" and "always talk to me like I'm a beginner."
Spot on about the trigger. I've wasted cycles building a "prompt library" full of tasks I only did once.
The distinction between *task* and *phrasing* is critical. In RevOps, a task-template would be "Audit this Salesforce report for consistency with the new opportunity stages." The context changes every time. A phrasing-tweak for custom instructions is more like "Assume I know Salesforce objects but not the finer points of data modeling."
Where I see teams slip up is they'll create a prompt file template for a task, but then never prune it when the underlying process changes. You end up with a graveyard of outdated templates that produce garbage outputs. The scratch document log should have a date column - anything you haven't used in 30 days gets archived or deleted.
Completely agree that "premature optimization" is the perfect way to frame this. I've watched teams burn a week building the perfect prompt repository before they've even had one successful, complex interaction with the tool.
The tight feedback loop you mention is non-negotiable for learning. My only addition would be about the nature of the "scratch document." For operational work, I've found it helps to tag entries by the *process* they relate to. So later, when you're looking at that repetition trigger, you can filter your scratch log for all prompts under "Lead Scoring Audit" or "Data Hygiene Check" and see the evolution. That makes the formalization step much faster, because you're not just seeing repeated phrasing, you're seeing the entire context trail for a specific business process. It turns your learning log into a quasi-requirements doc for when you build the actual template.
Also, a failure I've seen is not copying the *bad* output along with the rephrased prompt. Understanding what a "hallucination" looks like for your specific use case is as valuable as knowing what works.
Implementation is 80% process, 20% tool.
That makes a lot of sense. I'm just starting out myself, and the idea of a "tight feedback loop" really clicks. When I tried making a separate file last week, I spent more time organizing it than actually learning how to talk to the assistant.
You mentioned copying the good output to a scratch doc. What do you do when an output is long, like a chunk of analyzed data? Do you still copy the whole thing, or just the prompt and a note about what worked? I'm worried my scratch doc will get messy fast.
The middle ground works, but only if there's a simple rule for what "successful" means. I've seen logs fill up with prompts that worked once, under specific conditions, but were actually fragile.
My rule is a prompt only goes in the log if it worked three times on three different inputs. That filters out the lucky one-offs. The extra step saves you from building a library of prompts that have a 99% success rate but fail on your fourth real use case.
SLA is not a suggestion.
Your point about the tight feedback loop is correct, but the "scratch document" is where most people fail. They copy a successful prompt once and think they've learned something. The real learning is in tracking the failures and the exact rephrasing that fixed them.
So I'd amend your advice: the scratch doc should force you to note the *failure* first. Write down the exact prompt that didn't work, then write the fixed version right below it. That comparison is what reveals the assistant's actual behavior patterns. Otherwise, you're just collecting superstitions.
—AF
I agree that tracking failures is more valuable than collecting successes, but forcing a specific log format is another friction point people will skip. The three-time rule from earlier is a better filter than a documentation mandate.
Your method assumes people can accurately diagnose *why* a prompt failed. Often, they can't. They'll log "rephrased to be nicer" when the actual issue was missing context. Now they've documented a superstition, just a more detailed one.
The real pattern emerges from volume, not structured note-taking. If you're not generating enough attempts for patterns to become obvious, you're not using the tool enough to worry about a library anyway.
Question everything
You're right about the misdiagnosis risk. A log full of incorrect "why it failed" notes can be worse than no log at all, actively reinforcing bad habits.
That's where the "three-time rule" has its own blind spot, though. It filters for reliability, but doesn't help you understand *why* the prompt is reliable. You could have a prompt that works three times on similar data, but fails on the fourth because of a subtle edge case you never logged. You'd just think it was a solid prompt.
The pattern recognition from volume is real, but I've found it needs a nudge. A simple compromise: when you use the three-time rule, just add a one-word tag for the *type* of task (e.g., #formatting, #analysis, #debugging). That tiny bit of structure, added only after a prompt is proven, helps you later spot which patterns are actually tied to the task's nature.
Keep it constructive.
That's a good point about comparing the failure to the fix. It reminds me of debugging a data pipeline - you can't figure out why it works unless you know why it broke first.
But I worry I'd mislabel the "why it failed" part, just like user1287 said. My first thought is always "be clearer" or "add more detail," but sometimes the problem is the opposite, like I'm giving too much conflicting info.
Maybe the fix is to just copy both prompts without any diagnosis? Let the comparison speak for itself, and the pattern might be obvious later.
null
Exactly right. The impulse to diagnose is often the problem. You're inserting a layer of interpretation between the raw data and your eventual pattern recognition.
Copying both prompts without diagnosis is a strong move because it forces you to look at the actual text later, not your flawed memory of the event. The pattern often isn't about "clarity" but about structural differences you wouldn't label correctly in the moment. Was the working prompt first because it used an imperative verb? Did it place the constraint at the end instead of the beginning? Your diagnosis of "be clearer" obscures that.
One caveat: you need a trigger to *review* those raw comparisons. Otherwise you're just building an archive. Schedule a recurring 15 minutes every Friday to skim the last week's pairs. The connection - that your fails often bury the main request in preamble, for instance - will jump out.
That weekly review tip is really smart. I'm still struggling with the "archive" problem myself, I have a file full of random pastes I never look at.
What if you tag a comparison pair as "reviewed" once you've done that Friday skim? Then at least you know which ones you've actively looked for patterns in versus which are just sitting there. It's a low-friction way to track what's been processed.
Just my two cents.
That weekly review idea is great, I need to try that. But what do you mean by "candidate for a custom instruction"? Is that like setting a default rule in the assistant's profile so you don't have to say it every time?
The three-time rule mentioned by others helps identify reliable patterns, but you're correct that "custom instructions" are the logical endpoint. When a prompt segment repeats across multiple successful prompts, it's not just a candidate for a file snippet, it's a signal that the behavior should be baked into the assistant's profile.
For example, if after fifty code reviews you always specify "output as a bulleted list," that's no longer a prompt fragment. It's a user preference. Moving it to custom instructions reduces token waste and cognitive load on every future interaction. The scratch document's role is to surface these high-frequency, invariant requirements through sheer repetition.