How to Do Custom GPTs: A Step-by-Step Workflow
Building a Custom GPT that actually gets used comes down to a repeatable sequence: define the job it’s doing before you touch the builder, write instructions like documentation instead of a prompt, structure your knowledge files for retrieval rather than just dumping PDFs in, wire up Actions only where they earn their complexity, then test it the way a skeptical employee would. Skip the first step and every step after it gets harder.
- Step 1: Define the Job Before You Open the Builder
- Step 2: Write Instructions Like a Job Description, Not a Prompt
- Step 3: Build Knowledge Files for Retrieval, Not Just Storage
- Step 4: Add Actions Only Where They Earn the Complexity
- Step 5: Test It Like a Skeptical User, Not the Person Who Built It
- Step 6: Set Sharing, Access, and an Update Owner
- Step 7: Plan the First Revision Before You Launch
This is the workflow we run for client GPTs at Salterra. It’s not the only way to do it, but it’s the one that consistently produces a tool people open a second time.
Step 1: Define the Job Before You Open the Builder
The single biggest predictor of whether a Custom GPT gets adopted is whether it has one clear job. “A GPT for our marketing team” is not a job. “A GPT that drafts first-pass product descriptions in our brand voice, flags any claim that needs a citation, and formats output for our CMS” is a job.
Questions we ask before writing a single instruction
- Who is the primary user, and what are they doing right before and after they open this GPT?
- What’s the one task it needs to nail, even if it does nothing else well?
- What does a bad output look like, specifically — not “wrong,” but wrong in what way?
- Does this need live data (Actions) or is static knowledge enough?
We’ve scrapped more first drafts because the job was too broad than for any other reason. Narrow the scope until it hurts a little — you can always expand later.
Step 2: Write Instructions Like a Job Description, Not a Prompt
The instructions field is where most Custom GPTs succeed or fail. Treat it as a brief you’d hand a new hire on day one, not a single clever sentence.
What good instructions include
- Role and purpose: Who the GPT is acting as, and what it’s for, in plain language.
- Tone and voice rules: Specific enough to be checkable — “avoid exclamation points,” not “be professional.”
- Explicit boundaries: What it should refuse or defer, such as pricing questions or legal claims that need human review.
- Output format: Headings, length, whether to use bullet points, what to do when it’s uncertain.
- Reference priority: Tell it to check uploaded knowledge files before answering from general training, especially for policy or product-specific questions.
One habit that consistently improves output: explicitly instruct the GPT on what to do when it doesn’t know something, rather than leaving it to guess. “If the knowledge files don’t cover this, say so and suggest who to ask” beats silence on the topic every time — without that line, the model will often produce a plausible-sounding but unsupported answer rather than admit a gap.
Step 3: Build Knowledge Files for Retrieval, Not Just Storage
Uploading a 40-page brand guideline PDF and hoping for the best is the most common mistake we see in half-built GPTs. Retrieval works by pulling relevant chunks of a document at answer time, so how a document is structured directly affects how well the GPT can find the right piece.
Structuring knowledge files that actually retrieve well
- Break large documents into topic-focused files instead of one giant reference — a “Shipping Policy” file and a “Return Policy” file retrieve more precisely than one combined “Policies” file.
- Use clear headings inside each document; the model leans on structure to identify what a section is about.
- Remove outdated or conflicting information before upload — a GPT can’t tell which of two contradictory policy drafts is current.
- Keep a maintenance owner on record. Knowledge files go stale the same way a webpage does, and nobody notices until a client asks why the GPT quoted last year’s pricing.
Step 4: Add Actions Only Where They Earn the Complexity
Actions connect a GPT to an external API using an OpenAPI schema, letting it check live data or trigger a process instead of just talking about one. They’re powerful, and they’re also the part of a build most likely to break, so we only add them when static knowledge genuinely can’t do the job.
A basic Actions setup, in order
- Confirm the API exists and has documentation you can turn into (or already provides as) an OpenAPI schema.
- Scope authentication tightly — a Custom GPT should hold the minimum access it needs, never a master API key with broad permissions.
- Start with read-only actions (checking data) before adding write actions (submitting or changing data). Read-only mistakes are inconvenient; write-action mistakes can be costly.
- Write a clear description for each action so the model knows when to call it — vague descriptions cause the GPT to either call it at the wrong time or ignore it entirely.
A practical example: connecting a GPT to a scheduling tool like a Zapier or Make webhook is often faster to set up and safer to scope than a direct database connection, and it gives you an audit trail for free.
Step 5: Test It Like a Skeptical User, Not the Person Who Built It
The person who wrote the instructions is the worst tester of a Custom GPT, because they already know what it’s supposed to do. Real testing means handing it to someone who doesn’t, and watching where they get confused.
A testing pass we run before any client handoff
- Ask the exact questions the intended user will actually ask — pull these from real support tickets or Slack threads, not hypotheticals.
- Ask something it should refuse or defer, and confirm it does.
- Ask a question the knowledge files don’t cover, and confirm it says so instead of guessing.
- Try to break tone — ask it something off-topic or slightly hostile, and see if the persona holds.
We keep a running doc of failed test prompts per client GPT. It’s the closest thing to a regression test suite this workflow has, and it catches drift when we update instructions later.
Step 6: Set Sharing, Access, and an Update Owner
A finished GPT still needs a home. Decide whether it’s shared by direct link, published to an internal organization list on a Team or Enterprise workspace, or — rarely, for client work — listed publicly in the GPT Store. Match the access level to who actually needs it; over-sharing an internal tool is an easy, avoidable mistake.
Just as important: name an owner responsible for updates. Knowledge files, pricing, and policies change. A GPT with no update owner slowly drifts out of sync with reality until someone stops trusting it — and once a team stops trusting a tool, they don’t come back to it even after it’s fixed.
Step 7: Plan the First Revision Before You Launch
Every Custom GPT we’ve built needed at least one round of instruction tweaks after real users touched it — not because the first draft was wrong, but because real questions expose edge cases hypothetical testing misses. Build in a two-week check-in from day one instead of treating launch as the finish line.
Frequently Asked Questions
How long does it take to build a working Custom GPT?
A simple knowledge-based GPT with no Actions can go from brief to working draft in a single working session. Adding Actions, structuring multiple knowledge files properly, and running a real testing pass typically stretches the timeline to several days, mostly for review and revision rather than build time.
Do I need developer help to set up Actions?
Not always. If the API you're connecting to already publishes an OpenAPI schema, you can often configure the Action directly. Custom or internal APIs without documented schemas usually need a developer to write one, even if the GPT Builder itself stays no-code.
What file formats work best for knowledge files?
Plain text and well-structured PDFs with clear headings tend to retrieve most reliably. Spreadsheets work for structured reference data but retrieve less predictably than prose documents, so pair them with a short text summary when precision matters.
How do I know if my instructions are too long?
Length itself isn't the problem — clarity is. Instructions that ramble or bury key rules in the middle of a paragraph get followed inconsistently. Structure instructions with clear sections and short, direct statements rather than worrying about a specific word count.
Can I update a Custom GPT after it's been shared with a team?
Yes, edits apply immediately to everyone using the GPT going forward — there's no need to redistribute a new link. This is exactly why naming an update owner in Step 6 matters; it's easy to fix, so there's no excuse for letting it go stale.
Should every client get a Custom GPT?
No. If a client's need is a one-off task or the knowledge changes too fast to maintain reliably, a Custom GPT isn't the right tool — a well-crafted reusable prompt or a different workflow often serves them better. We scope the job first, precisely because not every job needs this solution.
Terry has 30+ years in software and SEO. He’s the founder of Salterra Digital Services and SEO Spring Training, host of the Roundtable SEO Mastermind, and lead instructor at SEO University — teaching the exact tactics his team uses on client work.
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