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0OpenAI APIs1Gemini2Claude3Ollama4Open Source Models5Prompt Templates6Output Parsers7Streaming8Image Models9Vision Models10Audio Models11Multimodal
LLM Engineering & APIs
Phase 2Module 1 of 12

OpenAI APIs

Training and hosting LLMs requires massive GPU infrastructure. OpenAI APIs abstract this away — developers call a REST endpoint and get state-of-the-art model capabilities without managing infrastructure.

OpenAI API is like electricity from the grid — you don't build your own power plant. You plug in, pay per use, and get reliable power (intelligence) on demand.

Visual Workflows

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Key Takeaways

  • 1.OpenAI APIs provide production-ready access to GPT models, embeddings, image generation, and speech — the most widely used LLM platform for building AI applications. Core APIs: Chat Completions (GPT-4o, GPT-4o-mini) for text generation with message-based interface.
  • 2.Embeddings (text-embedding-3-small/large) for vector representations. Images (DALL-E 3) for generation.
  • 3.Audio (Whisper for STT, TTS for speech). Key parameters: model, messages (system/user/assistant roles), temperature, max_tokens, top_p, stream, tools (function calling), response_format (JSON mode).
  • 4.Pricing per token (input + output priced separately). Rate limits by tier.
  • 5.SDK: openai Python/JS package with sync and async clients.

Real Example

Scenario

Building a support chatbot: system message defines persona, user messages are customer queries, assistant messages maintain history. GPT-4o generates responses. Embeddings API indexes FAQ docs. Function calling checks order status from your database.

What you would do

In LLM Engineering & APIs, apply OpenAI APIs to this scenario: Building a support chatbot: system message defines persona, user messages are customer queries, assistant messages maintain history. Identify the inputs, run the technique, validate the output, and note one thing you would monitor in production.

Practice Task

Open the Code Walkthrough below and run it locally. Change one parameter related to OpenAI APIs (e.g. model, temperature, top_k, or tool name), observe the difference in output, and write 2–3 sentences explaining what changed.

Code Walkthrough

Highlighted lines show where OpenAI APIs happens in the code.

OpenAI APIs
1from openai import OpenAI  # import dependencies2import os  # import dependencies3
4client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))  # create API client5
6# Chat completion7response = client.chat.completions.create(  # core API call for OpenAI APIs8    model="gpt-4o-mini",9    messages=[10        {"role": "system", "content": "You are a helpful coding assistant."},11        {"role": "user", "content": "Write a Python function to reverse a string."},12    ],13    temperature=0,14    max_tokens=200,15)16print(response.choices[0].message.content)  # show output for debugging17
18# Embeddings19emb = client.embeddings.create(  # core API call for OpenAI APIs20    model="text-embedding-3-small",21    input="OpenAI embeddings for semantic search",22)23print(f"Embedding dim: {len(emb.data[0].embedding)}")  # show output for debugging

Cheat Sheet

Quick recap

quick ref
  • •client.chat.completions.create()
  • •model: gpt-4o-mini, gpt-4o
  • •messages: [{role, content}]
  • •temperature: 0 for factual
  • •stream=True for streaming
  • •embeddings: text-embedding-3-small

Common Mistakes

  • ✕Hardcoding API keys instead of environment variables
  • ✕Not tracking token usage — surprise bills
  • ✕Using gpt-4o for simple tasks — overpaying
  • ✕Ignoring rate limits in production — 429 errors