OpenAI API: Build and Review a Small Work Project

The OpenAI API lets you send requests to a language model from your own code, a tool like n8n, or a connected app, instead of typing in the ChatGPT interface. You control the inputs, the instructions and how the output is used. Tools like fenbs give your project a shared record of requirements, decisions and lessons that every assistant you connect can read, so each session starts from what was already agreed.

This guide covers what the API gives you, how to set up your first request, how to review outputs reliably, and how to keep the project context your team needs across sessions. For the Gemini API, see Gemini API: a practical guide to your first workflow. For the Claude API, see Claude API.

What the OpenAI API gives you beyond ChatGPT

ChatGPT is a conversation interface. The API is a programmatic connection: you send a structured request and receive a structured response, which your code can then parse, route or store. This means you can build a tool that drafts a document from a template, summarizes incoming emails, or answers questions about your data, without any manual copy-pasting.

The API also lets you control the system prompt, the conversation history, and any tool definitions, on every call. That control is what makes repeatable workflows possible. You are not relying on a chat interface to remember your preferences; you supply the context yourself, programmatically, every time.

What to set up before your first request

You need an OpenAI account with API access and a key. The key authenticates your requests. Keep it out of source code and notes; use an environment variable or a secrets manager.

• Create an account at platform.openai.com and navigate to API keys to generate a new key.
• Store the key in an environment variable (for example, OPENAI_API_KEY) rather than pasting it directly into code.
• Install the official Python or Node.js SDK, or use the HTTP API directly. The SDK handles request formatting and retries.
• Check the usage dashboard to track spending and set limits. API calls cost money even if the key was created on a free account tier.

OpenAI's documentation at platform.openai.com↗ (opens in a new tab) covers the latest models and their limits. Model names change over time, so link there rather than hard-coding a model name in your notes.

Making your first API call

A basic chat completion request sends a list of messages: a system message that sets the model's behavior, and one or more user messages. The model returns an assistant message. Here is a minimal Python example:

from openai import OpenAI

client = OpenAI()  # reads OPENAI_API_KEY from environment

response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[
        {"role": "system", "content": "You are a concise assistant."},
        {"role": "user", "content": "Summarize this in two sentences: ..."},
    ],
)

print(response.choices[0].message.content)

The model name, the system prompt and the user content are your main controls. Change the system prompt to adjust tone or scope. Add more user messages to give the model context from earlier in a workflow. The response object includes the completion text, token usage and finish reason.

How to review outputs before using them

An API call is not the same as a human check. Before you use an output in a real workflow, decide what a good result looks like and write that down. For a summary task, a good result is accurate, does not add information that was not in the source, and fits in the space you allocated. Test with a few inputs you already understand before processing anything live.

Common problems to check for: the model adds plausible-sounding facts not in the input; the output format drifts when the input length changes; the model ignores a constraint in the system prompt. Log inputs and outputs during development so you can compare results when you change the prompt or the model.

If your workflow has a human review step, define when it fires. For drafts: always. For summaries of low-stakes internal notes: spot-check every tenth run. Write that decision down, not just in a comment in the code. See API documentation for more on structuring API workflows.

Tracking decisions and lessons as you build

A small API project generates more decisions than you expect: which model to use, how long the system prompt should be, when to add a retry, how to handle a refusal. If those decisions live only in chat history or in a comment, the next person, or the next AI session, has to re-derive them. Write each decision down as you make it, with the reason.

Lessons are different from decisions. A lesson is a mistake that changed how you work: "the model hallucinated dates when the source was a table; we now pre-process tables before sending." Record it once and link it to the task. The next assistant that touches a similar task should read it before starting.

How to manage your API project with fenbs

Start with one real task from your project. Set up the shared record before you write more than a few lines of code, so the context is there from the first session.

1. Create a free account. Personal boards are free with no credit card required. Start with your own board to test the workflow.
2. Add a project for your API integration. Write a short goal (one sentence) and the key constraint, for example "summarize support tickets to under 50 words without adding information."
3. Add tasks for each piece of work: the first working request, the review check, the production guard. Move each task to In Progress when you start it.
4. Write your first Decisions and rules. Include the model chosen and the reason, the review trigger, and any known bad inputs. Mark each one with your name as decider.
5. Connect your AI app over MCP from Integrations. Ask it to read the AI context at the start of each session, including Rules and Where we left off, before it changes any code.

What goes wrong and how to fix it

Most early API problems are not model problems. They are setup and context problems. Here are the three most common:

• Authentication errors: the key is not in the environment, has been revoked, or has run out of credits. Check the dashboard first.
• Inconsistent outputs: the system prompt is ambiguous. Add one concrete example of the expected format. Test with three different inputs before deciding the prompt is stable.
• Unexpected refusals: the request contains phrasing the model treats as a policy issue. Rewrite the request to describe the task plainly, without hypothetical framing.

For integrations with other tools, see OpenAI API integration. For API key management in detail, see Codex API key.