High-Value GPT Use-Case Filter
Prioritize GPT ideas with unique data, multimodality, or executable actions
- Difficulty
- Easy
- Time to result
- ~weeks to results
- Steps
- 5
- Confidence
- 93%
Evaluate a proposed GPT by asking whether it gains leverage from at least one strong differentiator: proprietary data, multimodal capabilities, or API-driven actions. Proprietary data can help the model surface organization-specific correlations and recommendations unavailable in public training material. Multimodality enables workflows such as turning visual input into a working webpage rather than merely generating text. API connections let the GPT retrieve live information or perform tasks in external software. Keep the target job narrow and compare the result with ordinary ChatGPT; if the custom GPT offers no material improvement, reconsider the idea. Before exposing it to users, test whether uploaded proprietary files can be recovered through prompting and restrict sensitive information accordingly.
Origin
Extracted from Marketing Against The Grain as the hosts compared generic writing GPTs with differentiated data, vision, and API use cases.
Core principles
- 01Unique inputs create stronger differentiation than public content
- 02Multimodal capabilities expand GPTs beyond text generation
- 03API access turns answers into completed actions
- 04Narrow use cases are easier to make useful
- 05Sensitive data requires explicit protection against extraction
How to run it
- 1
Define the job
Describe the specific outcome the GPT should produce for one user and one recurring situation. Reduce broad application ideas to a focused micro-use case.
Pro tip Phrase the job as a concrete result rather than a general capability.
Watch out A GPT that attempts to reproduce an entire large web application will be difficult to differentiate and evaluate.
- 2
Score the data advantage
Determine whether the GPT can use proprietary, under-monetized, or organization-specific data. Verify that the data meaningfully improves its answer over public information.
Pro tip Use controlled comparisons against ordinary ChatGPT.
Watch out Do not expose confidential files until extraction and access-control risks have been tested.
- 3
Test multimodal leverage
Ask whether images or other non-text inputs enable a materially better workflow. Favor cases where the GPT produces a usable artifact from those inputs.
Pro tip Evaluate the resulting artifact rather than the persuasiveness of the conversational response.
Watch out Multimodal capability is not valuable unless it contributes directly to the desired outcome.
- 4
Map executable actions
Identify the APIs and applications required to retrieve live information or complete the task. Distinguish a GPT that advises from one that can take a useful action.
Pro tip Begin with a read-only integration before allowing consequential writes.
Watch out External actions require authentication, permission boundaries, and failure handling.
- 5
Validate the advantage
Compare the completed GPT with the standard ChatGPT experience. Continue only when the custom version is meaningfully more useful, accurate, or actionable.
Pro tip Test with realistic user requests rather than curated demonstrations.
Watch out Novelty alone does not create repeat usage.
In the wild
A RevOps team used private organizational data with GPTs to investigate relationships and visualize results. Because the information was unavailable on the public internet, the GPT could provide analysis tailored to the team's actual operation.
→ The proprietary dataset created useful internal analysis that a generic chatbot could not reproduce independently.
Pieter Levels used the unique Nomad List dataset to build a GPT that answered questions about remote-work destinations. The underlying dataset gave the assistant detailed location knowledge, although later discussion highlighted the danger that uploaded files could be extracted.
→ The GPT demonstrated both the commercial value and the security risk of wrapping an under-monetized dataset in a conversational interface.
Designer GPT generated a live breath-work webpage and then accepted a natural-language request to add an image. The use case moved beyond prose by producing and editing an artifact the user could open directly.
→ Multimodal generation made the GPT more useful than a generic writing assistant.
Common mistakes
Building another generic writer
Public text and basic style instructions may produce little improvement over ordinary ChatGPT, weakening the reason to use the custom GPT.
Ignoring data exfiltration
Uploaded knowledge can sometimes be recovered from a GPT. Sensitive datasets require adversarial testing and appropriate access controls.
Stopping at recommendations
A GPT that only describes what to do may be less valuable than one connected to APIs that can retrieve information or execute the workflow.
Is it for you?
Best for
It is best for teams evaluating internal assistants, data products, and task-oriented GPT applications.
Not ideal for
It is not ideal for projects whose only input is widely available text and whose only output is generic prose.
From the transcript
“when they're plugged into the multimodal uh vision and when they actually have propriety data”
“under monetized data sets are going to be huge”
“when you start to plug it into the apis that to me is like going to be really interesting”
From the episode
The Best & Worst GPTs + How To Make Your Own (#177)