What is LLM API?
Definition: A programmatic interface to a large language model (such as OpenAI's GPT or Anthropic's Claude) that accepts text prompts and returns generated text, enabling automated AI-powered workflows.
LLM APIs let you call AI models programmatically. You send a prompt via HTTP request. You get back generated text. This is how GTM Engineers embed AI into automated pipelines instead of copy-pasting from ChatGPT.
The two main providers: OpenAI (GPT-4, GPT-3.5) and Anthropic (Claude). Both charge per token (roughly per word). GPT-4 costs $0.03-$0.06 per 1K tokens. Claude 3 Opus costs similar. Cheaper models (GPT-3.5, Claude Haiku) cost 10-20x less and work fine for simpler tasks like email personalization and data categorization.
In a GTM workflow: n8n calls the OpenAI API to classify leads by industry based on their company description. Clay calls Claude's API to generate personalized email opening lines. A Python script calls GPT-4 to summarize a prospect's LinkedIn profile into 3 bullet points for the AE's pre-call prep. Each of these is an API call with a prompt, input data, and a structured response.
Practical tips: use the cheapest model that produces acceptable output (GPT-3.5 for classification, GPT-4 for writing). Set temperature to 0.3-0.5 for consistent output (higher temperature = more creative but less predictable). Include examples in your prompt (few-shot prompting) for better results. Parse the response programmatically (ask for JSON output) instead of trying to extract data from freeform text.