GPT / LLM call
Making an API request to a large language model (Claude, GPT-4, Gemini) from within a workflow. Used for content generation, classification, extraction, and summarisation tasks that require human-level language understanding.
Expert insight
An LLM call added mid-workflow introduces latency (typically 1-5 seconds, sometimes more) and cost per call that a simple API request doesn't have. Before adding an LLM step to classify or extract something, check whether a simpler rule-based approach (a keyword match, a regex) gets 90% of the way there for a fraction of the latency and cost, reserve the LLM call for genuinely ambiguous cases.
In a real workflow
A workflow sends a support ticket to a language model and asks for the category, urgency and a suggested reply as JSON.
Common mistakes
- Not validating the model’s output before using it.
- Sending large documents when a short excerpt would do, which increases cost.
Complexity level
Requires some familiarity with automation concepts.
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