Recurring-Task GPT Training Loop
Turn frequent tasks into trained assistants through sustained iteration
- Difficulty
- Moderate
- Time to result
- ~months to results
- Steps
- 5
- Confidence
- 96%
Start by selecting a small number of tasks that consume substantial time and recur often enough to justify training. Create one narrowly scoped GPT for each task, then provide representative examples, formatting rules, and a clear definition of acceptable output. Treat the initial build as a baseline rather than a finished product. Review real outputs, identify systematic failures, and add instructions or examples that address those failures. Repeat this cycle over four to twelve weeks or longer. The practical target is not necessarily complete replacement: a GPT that reliably completes roughly 70% of a task can still save meaningful time while leaving judgment, refinement, and approval to the user.
Origin
Extracted from Marketing Against The Grain during Kip Bodnar and Kieran Flanagan's assessment of early custom GPT performance.
Core principles
- 01Creation is fast, but quality requires sustained training
- 02Frequent, expensive tasks offer the greatest potential return
- 03Representative examples matter more than raw content volume
- 04Human review should guide each training iteration
- 05Partial automation can still create meaningful leverage
How to run it
- 1
Select recurring work
Choose two or three tasks that occur frequently and consume meaningful time. Keep each use case narrow enough to evaluate consistently.
Pro tip Prioritize tasks with repeatable inputs and recognizable output patterns.
Watch out Do not begin with a broad assistant expected to handle unrelated jobs.
- 2
Define the target output
Document the desired structure, tone, constraints, and quality standard. Give the GPT explicit instructions rather than relying on uploaded examples alone.
Pro tip Include examples that already represent the result you want reproduced.
Watch out A collection of inconsistent examples can teach an equally inconsistent style.
- 3
Build a baseline
Create the first version quickly and test it on realistic inputs. Preserve its outputs so later versions can be compared against the same cases.
Pro tip Use a small evaluation set containing both ordinary and difficult requests.
Watch out Do not mistake the speed of creation for production readiness.
- 4
Diagnose recurring failures
Review where the GPT ignores formatting, becomes generic, invents details, or misses the task's purpose. Convert each recurring failure into a clearer instruction or better example.
Pro tip Correct patterns rather than rewriting every individual answer manually.
Watch out Random prompt changes make it difficult to know which intervention improved the result.
- 5
Iterate over sustained use
Continue testing and refining the GPT for several weeks or months. Measure whether its useful contribution approaches a level such as 70% of the workflow.
Pro tip Retain human review for the final portion requiring taste or judgment.
Watch out Do not deploy unattended merely because a few demonstrations worked.
In the wild
Kieran supplied more than 50 LinkedIn posts plus instructions about hooks, short paragraphs, audience relevance, and structure. The first GPT still produced generic prose and ignored the intended LinkedIn formatting, revealing that uploaded content and one instruction pass were insufficient.
→ The failed baseline demonstrated the need for continued evaluation and refinement rather than a one-time upload.
A marketer identifies campaign briefs, performance summaries, and draft follow-ups as three frequent tasks. Each receives its own GPT, representative examples, and a fixed evaluation set. Weekly corrections address repeated omissions until the assistants reliably prepare most of each deliverable for human approval.
→ The marketer delegates a substantial portion of repetitive preparation while retaining final judgment.
Common mistakes
Treating creation as completion
A GPT created in minutes is only a baseline. Useful quality may require four to twelve weeks or months of iteration.
Uploading examples without a standard
Examples alone may not communicate which attributes matter. Explicitly define formatting, purpose, and acceptable quality.
Demanding total replacement
Rejecting a GPT because it cannot complete 100% of the task overlooks the value of reliable partial assistance.
Is it for you?
Best for
It is best for people with recurring knowledge-work tasks, representative examples, and time to review outputs regularly.
Not ideal for
It is not ideal for rare tasks, undefined quality standards, or workflows that require perfect unattended execution immediately.
From the transcript
“all the work is in the elongation of the fine tuning which we have found it normally takes four to 12 weeks of iteration to…”
“take the two to three things you do a ton spend a lot of time and effort on and try to create a few gpts…”
“they might not be able to do them for you but they might be able to do like 70% for you”
From the episode
The Best & Worst GPTs + How To Make Your Own (#177)