API Developer Path – Track Overview

Bank code OAI-API 7 domains · 60-item mocks Free Academy API pathway

Build, evaluate and operate an AI application on the OpenAI API. This is the largest and most technical track in this section. It is independent preparation built from publicly available OpenAI learning objectives and product documentation – not an official OpenAI course, and not an official practice exam. Read the credential landscape page first if you have not: Academy badges and pathway certificates are not certifications.

What this track prepares you for

This track mirrors all five courses of the Academy API pathway (the “Build with AI” category, OpenAI API product):

Academy course	Published time	What it covers
Scope AI Solutions	30 min	Spotting AI opportunities and planning a solution
Evaluate AI Applications	70 min	Evals, failure analysis, quality checks
Design and Build Agentic Systems	110 min	Agent roles, tools, handoffs
Build with Retrieval-Augmented Generation	100 min	Retrieval pipelines and answer quality
Optimize AI Application Performance	30 min	Quality, speed, reliability and cost in production

Passing all five course assessments at ≥ 80% earns the Academy API pathway certificate of completion. That certificate is not a certification and does not guarantee eligibility for the broader OpenAI Certification initiative – it proves you completed the pathway. This track adds two full-length independent mock exams and seven in-depth domain pages that go well beyond the ~340 minutes of Academy video, because the assessments reward judgment you only build by doing the work.

Blueprint

Our seven domains and their weights on the OAI-API mock exams (60 items, 90 minutes):

#	Domain	Weight	Items (approx.)	Course page
1	Scoping AI Solutions	12%	~7	Domain 1
2	The Responses API and Model Selection	18%	~11	Domain 2
3	Evaluating AI Applications	16%	~10	Domain 3
4	Designing and Building Agentic Systems	18%	~11	Domain 4
5	Retrieval-Augmented Generation	16%	~10	Domain 5
6	Performance, Latency and Cost	12%	~7	Domain 6
7	Production Safety and Operations	8%	~5	Domain 7

The mindset this track rewards

The API assessments reward one posture repeatedly: an AI application is a system you scope, measure and operate, not a prompt you ship. Correct answers tend to:

• Scope before building – define the job, the success metric and the cost envelope before choosing a model.
• Choose the lowest effort that works – start on gpt-5.6-terra or gpt-5.6-luna, escalate reasoning effort or model only when an eval proves you need to.
• Ground claims in retrieved evidence with citations, rather than trusting model knowledge.
• Measure with evals – a representative dataset and a grader beat a subjective “it looks better”.
• Match the runtime to the problem – Responses + tools for control, the Agents SDK for code-first orchestration, the Agents API for durable managed cloud agents.
• Operate safely – rate and spend limits, moderation, retries with backoff, RBAC and network controls before launch.

Wrong answers tend to: reach for gpt-6-astra at max effort by default, skip evals and ship on vibes, put the whole knowledge base in the prompt instead of retrieving, and treat production concerns (limits, retries, residency) as someone else’s job.

Suggested time allocation (28-hour plan)

Domain	Weight	Hours
The Responses API and Model Selection	18%	6
Designing and Building Agentic Systems	18%	6
Evaluating AI Applications	16%	4.5
Retrieval-Augmented Generation	16%	4.5
Scoping AI Solutions	12%	3
Performance, Latency and Cost	12%	2.5
Production Safety and Operations	8%	1.5

Hands-on preparation checklist

Do these in a real OpenAI API workspace – reading about them teaches nothing.

• Make a first Responses API call to gpt-5.6-terra, then re-run it with store: true and continue the conversation using previous_response_id.
• Add a function (tool) to a request, handle the function_call output item, and return a function_call_output on the next turn.
• Request a structured output with a JSON Schema via text.format and confirm the response validates against it.
• Run the same task on gpt-5.6-luna, gpt-5.6-terra and gpt-5.6-sol and write down the quality, latency and cost difference.
• Build a small eval: a 20-row dataset, one grader, a baseline run, then change the prompt and confirm the grader catches a regression.
• Create a vector store, upload three documents, and answer a question with file search, then read the citations it returns.
• Turn on prompt caching by keeping a long stable prefix and measure the cached-input cost drop.
• Stand up a minimal agent two ways – the Agents SDK locally and an Agents API session (OpenAI-Beta: agents=v1) – and compare who owns the loop.
• Add a spend limit and a rate limit in the dashboard and trigger a 429 on purpose, then implement exponential backoff.

Track pages