OpenAI API Essentials

intermediate ~4 hours tool-use streaming models structured-output

Learn the OpenAI API from first request to advanced features. Covers Chat Completions, function calling, structured outputs, streaming, vision, reasoning models, and embeddings.

Steps

  1. Key concepts openai intermediate

    Key concepts to understand when working with the OpenAI API.

    Start here to understand the mental model — models, tokens, rate limits, and API structure. Understanding the token-based billing model upfront prevents surprises when your usage scales.

  2. Text generation openai intermediate

    Learn how to use the OpenAI API to generate text from a prompt. Learn about message types and available text formats like JSON and Structured Outputs.

    Text generation is the foundation of every OpenAI API call. Focus on how the messages array is constructed and the role of system/user/assistant turns — this structure carries over to every other feature.

  3. Conversation state openai intermediate

    Learn how to manage conversation state during a model interaction with the OpenAI API.

    The OpenAI API is stateless — you must send the full conversation history with every request. Understanding this design and techniques for managing context window limits is essential for building multi-turn applications.

  4. Function calling openai intermediate

    Learn how function calling enables large language models to connect to external data and systems.

    OpenAI calls this 'function calling' while Anthropic calls it 'tool use' — the core pattern is identical but the request/response format differs. Pay attention to the tool_choice parameter and how parallel tool calls work.

  5. Structured model outputs openai intermediate

    Understand how to ensure model responses follow specific JSON Schema you define.

    Structured outputs use constrained decoding to guarantee valid JSON matching your schema. This is more reliable than Anthropic's tool-use-based approach but requires additionalProperties: false — a gotcha that trips up many developers.

  6. Streaming API responses openai intermediate

    Learn how to stream model responses from the OpenAI API using server-sent events.

    Streaming delivers tokens via server-sent events for responsive UIs. Focus on how delta objects accumulate into the final response — and note that the Responses API and Chat Completions API have slightly different event formats.

  7. Images and vision openai intermediate

    Learn how to understand or generate images with the OpenAI API.

    Vision lets you send images for analysis alongside text prompts. The detail parameter (low/high/auto) has a significant impact on token costs — use 'low' for quick checks and 'high' only when you need fine-grained analysis.

  8. Reasoning models openai intermediate

    Explore the capabilities of OpenAI's o1 series for complex reasoning and problem-solving. Learn about their features and how they compare to GPT-4o models.

    The o-series models use chain-of-thought reasoning internally before responding. They replace system messages with 'developer' messages and have different temperature constraints — do not simply drop them in as GPT-4 replacements.

  9. Vector embeddings openai intermediate

    Learn how to turn text into numbers, unlocking use cases like search, clustering, and more with OpenAI API embeddings.

    Embeddings convert text to vectors for search, clustering, and RAG pipelines. The dimensions parameter lets you trade quality for cost — and you must use the same model for both indexing and querying or similarity scores become meaningless.

  10. Prompt caching openai intermediate

    Learn how prompt caching reduces latency and cost for long prompts in OpenAI's API.

    OpenAI caches prompt prefixes automatically — no code changes required, unlike Anthropic's explicit cache_control markers. Check the cached_tokens field in the usage response to verify caching is working on your requests.