Ways to Use AI in n8n
AI in n8n is used in two ways. As a flexible LLM node that thinks at run time, and a rigid Code node whose logic can be written by AI and then locked. So the only main AI rule is that actually matters in n8n:
-
LLM nodes = flexible reasoning;
-
Code nodes = fixed behaviour.
Mix them up and the workflow becomes unstable. Use them in the right order and a non-coder can ship production automations.
LLM Node Inside the Workflow
n8n can call a model like ChatGPT or Gemini directly from a workflow. The LLM node takes input data and instructions (the prompt) and returns a structured response. It’s ideal for turning raw product data into analysis, extracting key fields, or generating client-facing text. This represents flexible logic, change the instructions, and the LLM instantly adjusts its behavior without any code edits. A strong LLM prompt for n8n should clearly name the task, the context, and the desired output format.
- Task: what to do;
- Source of truth: what data to use;
- Output format: how to return it;
- Rules: only use data provided, do not invent values;
- Clarification trigger: when to ask for missing or unclear information.
AI Writes Code Node Logic
n8n includes a Code node (JavaScript) for performing specific data transformations. These nodes are not flexible — once the code is written, it executes the same way every time. Instead of hand-writing JavaScript, you can ask AI to generate drop-in code, making it faster to build and adjust logic without manual scripting.
Ignoring the LLM vs Code split leads to three common failures:
- Quiet bad data: the AI guessed a value, no one noticed, and a downstream node broke;
- AI as rule engine: the AI was told to always format like this, but didn't.
- Code fear: no Code node was used, so each step received a slightly different structure.
Beginners often try to make the AI node do everything, fetch, interpret, reformat, and clean. That might work once, but it quickly breaks. Cleaning and reshaping should be handled in Code nodes, even if the AI wrote that code for you.
- LLM = judgment, wording, interpretation;
- Code = structure, shape, consistency.
To build reliable and maintainable AI-powered workflows in n8n, follow these key steps:
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AI in n8n is used in two ways. As a flexible LLM node that thinks at run time, and a rigid Code node whose logic can be written by AI and then locked. So the only main AI rule is that actually matters in n8n:
-
LLM nodes = flexible reasoning;
-
Code nodes = fixed behaviour.
Mix them up and the workflow becomes unstable. Use them in the right order and a non-coder can ship production automations.
LLM Node Inside the Workflow
n8n can call a model like ChatGPT or Gemini directly from a workflow. The LLM node takes input data and instructions (the prompt) and returns a structured response. It’s ideal for turning raw product data into analysis, extracting key fields, or generating client-facing text. This represents flexible logic, change the instructions, and the LLM instantly adjusts its behavior without any code edits. A strong LLM prompt for n8n should clearly name the task, the context, and the desired output format.
- Task: what to do;
- Source of truth: what data to use;
- Output format: how to return it;
- Rules: only use data provided, do not invent values;
- Clarification trigger: when to ask for missing or unclear information.
AI Writes Code Node Logic
n8n includes a Code node (JavaScript) for performing specific data transformations. These nodes are not flexible — once the code is written, it executes the same way every time. Instead of hand-writing JavaScript, you can ask AI to generate drop-in code, making it faster to build and adjust logic without manual scripting.
Ignoring the LLM vs Code split leads to three common failures:
- Quiet bad data: the AI guessed a value, no one noticed, and a downstream node broke;
- AI as rule engine: the AI was told to always format like this, but didn't.
- Code fear: no Code node was used, so each step received a slightly different structure.
Beginners often try to make the AI node do everything, fetch, interpret, reformat, and clean. That might work once, but it quickly breaks. Cleaning and reshaping should be handled in Code nodes, even if the AI wrote that code for you.
- LLM = judgment, wording, interpretation;
- Code = structure, shape, consistency.
To build reliable and maintainable AI-powered workflows in n8n, follow these key steps:
Tack för dina kommentarer!
