How I Built a Custom GPT to Do My Work For Me #BehindTheBot

By Dan Sanchez · Episode published

Build a useful custom GPT by turning one repeatable process into clear conversational steps, specific constraints, and examples that show what good output looks like.

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Build a Custom GPT Around a Process You Already Know

Custom GPTs become useful when they do one defined job well. The opportunity is not to make a general assistant that tries to do everything. It is to take a repeatable process you already use and give AI the instructions, steps, and examples it needs to execute a useful portion of that work.

NameFrame began with a practical naming process. The goal was not merely to request a list of names. It was to guide someone through a sequence: describe what needs a name, generate related words, explore rhymes, find familiar phrases, swap in the original words, and select the combinations that create a memorable double meaning.

Give the Assistant a Clear Starting Point

The first interaction should tell the user what to do. A button or opening instruction such as “start the naming process” is better than an empty chat field. It reduces uncertainty and gives the assistant a specific trigger to begin its workflow.

From there, each step needs three parts: the condition that starts it, the precise task, and the question that sends the conversation back to the user. That structure keeps the interaction collaborative. The user can remove weak words, add a meaningful term, or redirect the process before the next step compounds a bad choice.

Use Specific Constraints to Improve Consistency

Instructions become more dependable when they explain the kind of answer required. A request for “some related words” is loose. A request for eight simple words with one or two syllables creates a more useful pool for a naming exercise because those words are easier to rhyme and combine.

The same principle applies to content, research, and strategy tasks. State the input, the format, the level of detail, and what success looks like. If the work requires a recognizable final form, include a finished example or a template in the assistant’s knowledge. AI can follow a pattern more consistently when it can see the desired result.

Refine It Through Real Use

The first version will reveal gaps. It may generate words that are hard to use, skip a question, or follow the right process with the wrong output. That is not failure. It is feedback about the instruction that needs to be clearer.

Start with a simple, low-risk process and run it several times. Make small changes to the language and compare the results. This is how a custom GPT moves from a broad specialized chat into an assistant that can reliably support a real workflow.

The important skill is not writing one complicated instruction. It is learning to identify processes inside your work, break them into teachable steps, and provide the examples and constraints that make quality repeatable.