I don’t think a chat app is the best place to start using the API.
We have the QuickStart guide and the CookBook, of course, but I wanted to see what the community of humans here thinks about a good introductory AI project … and why!
TIA for any answers!
This is a great topic idea!
My suggestion is a little biased, but for creative beginners, I think a small persona-prompting project can be a good first API project.
Instead of starting with a generic chat app, they could build a simple “persona tester” using the Responses API:
write a short persona/system instruction
send the same user prompts to different models
compare which model follows tone, constraints, style continuity across turns and prompt-defined boundaries best
save a few outputs for side-by-side evaluation
It can stay very simple at first: text input, model selector and output comparison.
By constraints, I mean simple prompt rules such as “keep replies short,” “use dry humor” or “ask only one follow-up question.”
By boundaries, I mean behavior rules defined in the prompt, such as “do not make romantic claims,” “do not pretend to have personal emotions” or “do not go outside the assigned role.”
For people who enjoy voice, the same idea could later be extended with TTS or even Realtime for a spoken persona/assistant test. That makes it fun without needing a big production app immediately.
So my biased answer would be: a small persona/model-comparison playground. It teaches prompting, model behavior, API basics, evaluation and iteration as well as meaning testing, adjusting and testing again. Personally I think it is actually fun enough that beginners might keep using it.
Maybe something they would use a lot in “space they know” with a little “tweak”? For a example a custom gpt for daily task?
(just rendered that public)
Great topic!
I’d say start with Codex and produce an automation for whatever repetitive task you don’t want to do manually.
I’d start learning how to use a jupyter notebook, because delegating everything to AI will not give a minimal insight on how things work.
A jupyter notebook will allow to freely explore how to call the API, inspect the returning json objects and variables as you run things step by step.
Quick start: install python and vs code (with jupyter extension), then follow the already mentioned quickstart guide and cookbooks.
You know how to “chat” with AI.
You might even know that a continued conversation is simply by sending a growing history of messages into the model to have the latest input answered with a memory.
Now: data processing using a chat model. Reinforcement that there is no user to talk to, with language, with a structured output schema.
For that, you have the new control of a system (“developer”) message guiding the model with some authority of its operation mode on the API, and need to strike a balance with a model still trained to respond to “user” tasks.
A 0-shot prompted application taken right from some shared code elsewhere. It has the pattern of “instructions” and “input” (as might be seen on Responses), but is facilitated by Chat Completions and “system” and “user” role messages.
"You are part of an automated function of a program: no chat is possible.\n"
"The user is currently composing an image creation or image-editing AI prompt. "
"You act as instructed revision helper who can act on selections within that prompt. "
"You revise selected text inside image-making prompts sent to a \"smart\" image AI model. "
"Use surrounding prompt context, but output only the replacement prompt text for a user's selection.\n"
"Do not act as a moderator, safety supervisor, or judge: perform the requested action only, without unsolicited embellishment.\n"
"Out of scope requests: no refusal is possible; simply recite back an unchanged selection, at user expense."
Then after defining operational parameters, a task is better-followed when described by a “user”.
## Full prompt context
Use the full prompt only as context for understanding where your selection is located.
`````context
{full_prompt_text.strip()}
`````
---
## Editable selection for replacement
The selected text below is the only part you may replace, which will be replaced in full:
`````selection
{selection_for_ai}
`````
---
User instruction, for action on the selected text:
`````task
{improvement}
`````
---
When improvement is requested, write replacement scoped text that is ready for a modern image model:
- natural, specific prose that describes visible subjects, setting, composition, style, lighting, mood, and character-defining traits only when relevant. Prefer coherent phrasing over keyword piles. Avoid generic quality boosters and legacy prompt jargon such as hyper-detailed, ultra-sharp, 8k, masterpiece, intricate textures, or microdetail. If the user asks for a narrow edit, such as spelling or clarity, make only that edit.
Return only the replacement text for the selected span.
Do not include explanations, markdown fences, or surrounding prompt text.
Keep the replacement compatible with, and exclusive from, surrounding context.
input → output; you have created a function.
If you are already coding a new chat application or trying to build an AI backend, I don’t think using a high-parameter model as a simple answer machine is a very smart choice.
If you want your backend to be solid, you should code it by using smaller local models in the flow and response process. Because the smarter the model is, or the higher its parameter count is, the less backend precision you will need. And that will kill your backend’s flexibility and capabilities, making it dependent on the model.
After your backend has proven itself, you can use OpenAI’s powerful models at runtime.
Of course, these are just my personal opinions. They may be right or wrong, I can’t say for sure.
Well, the first thing beginners need to determine is how much money they are willing to spend…
Well, that would be 5$ at least for a Tier-1 account or one can make use of the free tier with 3 requests per minute for a mini model.
These make some interesting constraints for a beginner project.
Ooh, “cheapest API project” that’s still fun/useful could be interesting…
math solver software, there was a popular one i saw on social media that just used chatgpt
Start with your typical chatbot but WRITE YOUR OWN API from the documentation.
The APIs from one provider to another very similar, so if you can implement one of them you can implement all of them.
If a new feature is released, you are at the mercy of the framework maintainer.
But if you understand the API, you can have it implemented in minutes.
Great question! For beginners, I recommend starting with a simple containerized API project using Docker + OpenAI. This way you learn two fundamental skills at once.
For reference, the OpenAI Quickstart guide is a great starting point: Developer quickstart | OpenAI API
A simple CLI chatbot that runs inside a Docker container teaches you:
It’s beginner-friendly and immediately deployable anywhere!
cheapest API project that’s still fun/useful could be interesting
Agreed! That constraint actually makes it a more realistic project — similar to how production apps need to optimize costs. A Docker + OpenAI CLI chatbot fits perfectly within free tier limits for learning purposes.