4h • General Discussion
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A lot of people think customer support automation is just:
“Connect ChatGPT → create workflow → done.”
After building and delivering multiple chatbots and AI voice assistants, I learned something the hard way:
The workflow is the easy part.
The real difference comes from LLM engineering.
Because support automation fails very quickly when the AI:
• gives incorrect refund answers
• misunderstands product information
• hallucinates policies
• loses conversation context
• pulls the wrong data from different systems
That’s why simply connecting an LLM into a workflow is not enough.
A proper AI support system needs:
• strong prompt architecture
• semantic search
• RAG pipelines
• hallucination prevention
• smart routing
• context memory
• structured business knowledge
And honestly, this is where skilled LLM engineers matter a lot more than people realize.
The companies getting real results are not just building “AI chatbots”
they’re building systems that actually understand business workflows and customer intent.
Alongside building these systems, I’ve also been spending a lot of time improving my own skills in LLM engineering and understanding how these models behave inside real business environments.
That layer changes everything.
AI workflows alone don’t solve support problems.
Well-engineered AI systems do.
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