Sep 30 (edited) • I Made this
Example of Conditional Workflow
🚀 Building Smarter Customer Review Workflows with LangGraph + LLMs🚀
Handling customer reviews isn’t just about responding quickly—it’s about responding intelligently. Some reviews are full of praise 🙌, while others highlight real frustrations 😤. Both deserve thoughtful, tailored responses.
I recently built a workflow using LangGraph + OpenAI models that automates this process end-to-end:
🔹 Step 1: Sentiment Detection
The system first identifies whether the review is positive or negative.
🔹 Step 2a: Positive Reviews
If it’s positive, the LLM crafts a warm thank-you message and encourages the customer to leave feedback on our website.
🔹 Step 2b: Negative Reviews
If it’s negative, the system runs a diagnosis layer, extracting:
1. Issue type (UX, Bug, Performance, etc.)
2. Tone (angry, calm, frustrated…)
3. Urgency (low, medium, high)
Using these insights, the LLM generates an empathetic, problem-solving reply that matches the user’s sentiment and urgency.
💡 The result? A workflow that not only saves time but also elevates customer experience by ensuring responses are context-aware and personalized.
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Example of Conditional Workflow
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