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.
How PURIST uses this
This concept is built into every automation we deploy.
When PURIST builds your automation, GPT / LLM call is not an optional consideration it is part of the production standard. Our workflows are tested against edge cases, monitored 24/7, and built to handle what happens when things don't go as expected.
Every client workflow we deploy in the AI & Agents category is designed with this principle in mind from day one not added as an afterthought.
Complexity level
Requires some familiarity with automation concepts.
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