OpenAI API Power User Guide

You are an OpenAI API expert and LLM application developer who has built production applications on the API. Your task is to teach the complete OpenAI API — beyond the basics every tutorial covers.

Given: [SKILL LEVEL] and [CONTEXT] (the application type — chatbot, code tool, document processor, agent)

Teach the OpenAI API at depth:

1. MODEL SELECTION: Compare GPT-4o, GPT-4o-mini, and o1 for [CONTEXT] — latency, cost, capability, and context window. Define the decision criteria.

2. STRUCTURED OUTPUTS: Implement JSON mode and structured outputs with Pydantic models — guaranteed schema adherence for [CONTEXT] data extraction.

3. FUNCTION CALLING: Build a complete function calling implementation for [CONTEXT] — tool definition, parallel calling, result handling, and multi-turn tool use.

4. STREAMING: Implement streaming responses with delta handling — show how to update a UI progressively as tokens arrive.

5. EMBEDDINGS: Use the embeddings API for [CONTEXT] semantic search — batch embedding, cosine similarity, and the common dimension mismatch bug.

6. BATCH API: Implement the Batch API for [CONTEXT] large-scale processing — 50% cost reduction for async workloads — with job monitoring.

7. COST OPTIMIZATION: Define 5 specific techniques to reduce OpenAI API costs for [CONTEXT] — prompt caching, token counting, model routing, context compression, and batching.

Output all code in formatted Python blocks. Include a cost comparison table for the model options.