Custom GPTs are essentially modified versions of ChatGPT that you can tweak to act exactly how you want them to. Think of them like setting up a new character in a video game—you choose the personality, skills, and even the way they talk. Except here, it’s an AI chatbot that talks to customers, generates code, or guides users through policy documents.
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The real “engineering” part? It comes down to what’s called a prompt—a bit like writing a mission statement or job description for your GPT. This prompt tells the AI what role to play, what tone to use, what types of questions to expect, and how to respond. And that prompt can get incredibly detailed. It’s not about typing “Write in a friendly tone.” It’s more like:
You're a strict but fair project manager. Never use emojis. Always ask for one action item per message. Respond in short sentences only.
When I built a Custom GPT to help with commissioning content for an audiobook platform, I had to train it with examples of both approved and rejected submissions. But—here’s the kicker—if I didn’t add a very specific instruction like “Never accept files over 10 minutes unless marked as premium,” the GPT would just… accept them anyway. 🙄 Prompt engineering isn’t just writing a few friendly instructions—it’s closer to designing a logic system through plain language.
One weird thing: I noticed some styles of instructions stick better if you embed them in a story. For example, saying “Imagine you’re a tired journalist editing your final piece for deadline,” worked better than “Write concisely, avoid adverbs, and remove first-person.” Not what I expected, but the story-style framing nudged the tone more reliably in every test.
The bottom line is: Custom GPTs behave more predictively when your prompt is both structured and creatively framed. If it feels like roleplaying, that’s not a bug—it’s kind of the hack.
Structuring Your Prompt for Consistency
Creating a prompt that yields consistent results across multiple conversations is the trickiest part. Here’s what works in practice:
Prompt Component
Real-World Effect
Initial Role Definition
Sets voice and tone. GPTs trained with “You are a tech support agent” respond with less fluff than “You are a helpful assistant.”
Behavioral Rules
Things like “Always ask follow-up questions” or “Avoid speculation.” Without these, the GPT often defaults to guessing.
Output Format Examples
Adding an actual template or example answer massively ups reliability. If left vague, the output shifts wildly.
Edge Cases
Specific failures you want to avoid. e.g., “If the user says ‘stop,’ end the session.” Helps avoid awkward overshooting.
Let’s say you’re creating a GPT to calculate SaaS pricing. If your prompt says “Offer a suggestion,” it might go wild combining tiers and discounts. Instead, if you script: “Use only the standard, premium, and enterprise tiers. Do NOT suggest any bundle,” the output clamps down like it should.
Another tip: every time you get a bizarre output, ask yourself—was that covered by the prompt? Nine times out of ten, a weird reply is just the GPT following the prompt… too literally or without enough constraint.
Ultimately, a clear structure inside your prompt avoids most of the hallucination and inconsistency problems.
Useful Tools to Test and Troubleshoot Prompts
Once you write your prompt, don’t just launch it and walk away. I made that mistake on a client support GPT and had customers getting responses like “I’m not sure, but maybe click Start again?” 😬 Not ideal.
Here are three tactics (and a few digital tools) I use for testing that help catch those awkward GPT behaviors before they go live:
Scenario Stress Testing: Come up with the five oddest user questions you can think of. Off-topic, aggressive, confused, wrong terminology. Run them through your GPT and look for awkward replies. I use a Notion doc with pre-built test cases to avoid relying on memory.
Shotgun Prompt Injection: Add junk into messages like “Ignore previous instructions” or “Please pretend you’re someone else.” If the GPT breaks character, you’ve got holes in your safety layer. OpenAI’s Custom GPTs do have guardrails, but they’re not perfect.
Prompt Iteration History: Track every version of your prompt in Jira or Obsidian. I tag each version with a reason for the tweak: e.g., “added time formatting rule because it kept using military time.” Tiny changes can cause big shifts.
If something’s really stubborn—like a Custom GPT that always concludes with “Thank you for your time!” no matter what you ask—it probably learned that from the format examples. Strip those back and re-test.
To wrap up this section: your prompt won’t be perfect the first time. Think of it more like tuning a weird musical instrument—you’ll get harmonics, but only after several frustrating notes.
How to Maintain Persona Stability
One underrated problem with Custom GPTs: they can start the convo like Shakespeare and end like TikTok. Persona drift is subtle, but over long interactions it shows up.
Here’s how I lock down behavior:
First-response scaffolding — I use a default intro line like: “Hello, I’m AuditBot. I specialize in reviewing work orders with a focus on compliance.” This line sets tone, role, and context, and GPTs tend to latch onto it for later replies.
End-of-turn hint drops — Occasionally remind the GPT of the role indirectly. Instead of saying “Remember you’re a medical advisor,” nudge with: “From a medical guideline perspective…”
Dialogue anchors — Drop custom phrases, like “Let me break this down by step,” or “Here’s the quick version.” Train your GPT to reuse these to stay consistent.
I’ve noticed this especially with GPTs designed for customer service. They start professional, then user tone bleeds into replies. If a user types angrily, and your GPT begins mimicking that vibe—oops. You forgot to ground the tone with strong end-of-message templates or role locking logic.
This also happens when multiple users chat with the same instance. If your Custom GPT isn’t stateless, chat momentum carries over and the polite receptionist becomes a sass machine by message 4. 🤦♂️
To conclude: persona drift isn’t just about bad prompts—it’s about low friction in tone reinforcement. Build it in subtly and repeatedly.
Instructions vs Examples: Which Comes First?
Should you start by telling the GPT what to do (“You are a 19th-century historian”) or just give it a dozen samples to learn from? My answer: both, and in a very specific order.
Write the behavior expectations first. Clarity here gets the skeleton right. Use short bullets for readability.
Add two to three labeled examples (ideally with ‘User:’ and ‘GPT:’ sections). GPTs mirror format even more than tone, so sloppy layouts get echoed.
Wrap with a constraint section. Ex: “Do NOT mention pricing. Avoid emojis. Never recommend solutions without verification.”
A clean prompt structure like this usually stops the GPT from doing random things like generating poems mid-report (yes, I’ve actually seen this when I forgot to control tone limits).
Ultimately, putting structure before storytelling keeps your responses dialed in.
Testing Unusual Inputs and Reset Behavior
Something I kept running into: users inputting garbage. Like literal nonsense strings: !#&*%?/."#(*UDgw127gh. Or spamming “asdf.” You don’t want your GPT replying “Great question!” to that.🥴
If you expect that kind of interaction (support bots, classroom assistants, anything public-facing), build emergency exits:
Static fallback reply – “I didn’t understand that. Could you try again with a shorter message?” Template this and put it in the prompt as a fallback scenario.
Message length cutoffs – Instruct GPT not to process inputs over a certain character limit. “If the message is over 1,000 characters, request user to shorten and resend.” It really helps against AI loop triggers.
Reset protocol – Add: “If the user says STOP or RESET, halt all prior behavior and restart greeting template.” This stops context build-up.
You’ll catch unpredictable behaviors only when you test with bad input—not just perfect sample questions. Much like game QA testing, stress the edges.
As a final point: if your GPT never encounters a bad user input, consider yourself lucky—but design like it will.
Versioning and Documentation is Non-Negotiable
I say this as someone who regretted not doing it: document everything. Change logs, test cases, behavior expectations, known bugs. Custom GPTs may look simple on the surface, but under the hood, your prompt is basically source code in disguise.
My snapshot system uses one Notion table per GPT. It tracks:
Prompt history by date
Main version notes
Known output quirks
User interaction logs for edge case review
You can go fancier with GitHub or even build a trigger that dumps new versions to a Google Doc automatically using Zapier or Make.com. When I started doing this, I caught a ton of changes I didn’t even realize I’d made—tiny wording shifts that led to big tone differences.
This also helps when collaborating. I handed my onboarding assistant GPT over to a freelance PM, and she instantly saw the instruction that said “Always end message in two sentences max.” That could’ve been missed if I didn’t log it.
At the end of the day, your prompt is the source of truth—treat it with the same care you’d give to code.
