Building Systems with the ChatGPT API

Building Systems with the ChatGPT API is a free, ~1-hour project-based short course from DeepLearning.AI, co-taught by Isa Fulford (OpenAI) and Andrew Ng, that teaches you to compose multiple LLM calls into a single application rather than relying on one giant prompt. Across roughly nine code-along lessons you build up a customer-service assistant: classifying user intent, screening inputs with OpenAI's Moderation API and guarding against prompt injection, applying chain-of-thought ('inner monologue') reasoning, chaining prompts into discrete stages, checking outputs, and finally evaluating the end-to-end system. It is best understood as the practical sequel to ChatGPT Prompt Engineering for Developers, and is widely rated highly (4.7/5 from 348 reviews on Coursera's mirror) for its tight, hands-on format. The main caveats are real: it is very short and conceptual rather than deployment-focused, awards no certificate on the native platform, and some example code lags current OpenAI API conventions, with a few learners reporting outdated snippets or a completion bug. For developers who already know basic Python and want a fast, credible mental model for structuring multi-step LLM apps, it is an easy recommendation; people seeking production frameworks (RAG, agents, orchestration) or a deep ML course should look elsewhere.

Free, short, and taught by authoritative instructors (OpenAI + Andrew Ng), it delivers a genuinely useful architectural pattern for multi-step LLM systems with hands-on Jupyter notebooks, and holds a strong 4.7/5 (348 reviews). The honest downsides (1-hour scope, no native certificate, some dated code) are minor relative to the zero cost and high signal, so the time investment is easily justified for its target audience.

Best for: Developers and data scientists comfortable with basic Python who have done some prompt engineering and now want to learn how to structure a real multi-step LLM application -- splitting work into classification, moderation, chain-of-thought reasoning, prompt chaining, and output evaluation. Ideal as a direct follow-on to ChatGPT Prompt Engineering for Developers, and useful for engineers prototyping customer-support or workflow-automation chatbots who want safety guardrails and a debuggable pipeline rather than one monolithic prompt.

Skip if: Complete programming beginners (it assumes Python and moves fast), people who want a certificate-bearing credential on the native DeepLearning.AI platform, and anyone seeking production-grade depth -- retrieval-augmented generation, agent frameworks, LangChain/orchestration, vector databases, deployment, or fine-tuning -- which this 1-hour course does not cover. It is also not a machine-learning fundamentals course; those wanting the theory should take a full specialization instead.