ChatGPT for TIA Portal: what works, where it fails
Yes, ChatGPT writes SCL for TIA Portal: explaining syntax, drafting a block scaffold, translating an STL snippet, the browser chat is entirely enough for that. It does not know your project, though, and that is exactly where the line runs: for real project code you need an assistant with access to tags, data types and blocks, for example AnyAutomation Studio, where GPT runs just like Claude or Gemini, only with project context instead of copy and paste.
The table below shows where that line runs in daily work.
Task by task
| Task | ChatGPT in the browser | Integrated assistant |
|---|---|---|
| Explain SCL syntax and language concepts | Good | Good |
| Scaffold for a new block | Usable, symbol names invented | Real tag names and data types from the project |
| Explain an existing block | Only what you paste in | Reads the block straight from the project |
| Convert STL to SCL | Snippet by snippet | Whole block, interface and comments preserved |
| Get code into the project | Copy and paste | Openness import with diff and confirmation |
| Fix compile errors | You retype the error | Compile against the real project |
| Prove the result | Not provided for | SCL unit tests on PLCSIM Advanced or a real S7 PLC |
| Confidentiality | Plant code in a third-party chat | Local model possible, the project stays on your machine |
What works well with ChatGPT
For everything that needs no project knowledge, ChatGPT is a usable tool: SCL syntax questions, explaining language concepts (timers, edge detection, indirect addressing), a first block draft as a scaffold, or a rough translation of STL snippets to SCL. As a sparring partner for structural questions ("how would you build a sequence for this plant?") it gives solid answers too. For learning and for small, self-contained tasks the browser chat is often faster than any tool that needs setting up.
Where the copy-paste workflow fails
The root problem: ChatGPT does not know your project. The typical failure modes follow from that:
- Invented symbol names and data types. Generated code references tags that don't exist in your project, or guesses at UDTs. Every block needs rework before it even compiles.
- Copy-paste as a media break. Code out of TIA, into the browser, back, paste, retype errors, back into the browser. Manageable for one block, painful for twenty.
- No feedback loop. ChatGPT sees neither compiler errors nor cross-references. It only corrects what you carry back manually.
- No proof. Whether the block does what it should only shows on the plant, because no test runs in a browser chat.
- Confidentiality. Copying plant code into a third-party browser chat is simply not allowed in many companies.
The custom GPTs in the GPT store ("TIA Expert" and the like) change little: better prompts, same fundamental limits, because they don't see your project either.
Why the compiler belongs in the loop
That is not a matter of taste, it has been measured. The research pipeline LLM4PLC put grammar checkers, compilers and verification around the language model and raised the generation success rate from 47 to 72 percent, and expert-rated code quality from 2.25 to 7.75 out of 10. AutoPLC, which generates SCL for TIA Portal and ST for CODESYS and validates directly against the vendor IDE, reports over 90 percent compilation success on a 914-task benchmark. Agents4PLC builds the same closed loop of generation and verification with several agents.
That loop is exactly what a browser chat lacks. Not because the model is weaker, but because nobody checks the result except you, by hand.
The difference: project context
An integrated assistant flips the picture. The AI for TIA Portal in AnyAutomation Studio reads blocks, tag names and data types over the official Openness API (V15-V21), uses that to generate SCL that fits the plant, shows every change as a diff and imports only after confirmation. Errors flow back, unit tests on PLCSIM Advanced or a real PLC guard the result, and Git records what changed. If the cloud is off limits, the model runs locally via Ollama, LM Studio, vLLM or SGLang.
You do not have to give up ChatGPT for that: Studio is multi-provider, so GPT connects by sign-in or with your own API key, just like Claude, Gemini or a local model. Which model suits which job is in Which LLM for TIA Portal?, and all tools side by side are in the comparison of AI tools for PLC programming.
What project context still does not solve
Context solves the naming problem, not the responsibility problem. Language models still occasionally invent system blocks or parameters, assumptions about cycle time, peripherals and alarm OBs belong checked, and safety-related logic is not generated: F-logic stays manual work with formal acceptance. A diff belongs in front of every import, a test behind it. More in the guide to AI in PLC programming.
Frequently asked questions
Can ChatGPT write PLC code for TIA Portal?
Yes, for standard logic in SCL and for scaffolds. Without project access it guesses symbol names and data types, the code rarely compiles on the first try, and the path into the project is copy and paste. Fine for learning and small blocks, not for project work.
Can ChatGPT access my TIA project?
The browser chat cannot. Access to blocks, tags and data types comes only from a tool that talks to TIA Portal over the official Openness API. AnyAutomation Studio does that for V15 to V21 and can use GPT as its model.
Are the custom GPTs for TIA Portal worth it?
They improve the prompts and the vocabulary, not the access. Even a "TIA Expert" in the GPT store does not see your project, checks no compile and runs no test. The fundamental limits stay the same.
Am I allowed to send plant code to ChatGPT?
Your internal approval decides that, not the tool. Where cloud chats are blocked, a local model via Ollama, LM Studio, vLLM or SGLang remains: inference runs on your own hardware and the code never leaves the machine.
What does the alternative with project context cost?
AI chat in AnyAutomation Studio is included from the Basic plan, CHF 10 per month or CHF 110 per year; Pro is CHF 30 or 330, Pro+ CHF 50 or 550. Your ChatGPT subscription or your own API key comes on top, a local model costs nothing extra. 30 days free to try.