01 What students build
LangFlow is a drag-and-drop canvas for LLM pipelines. Flow 1 (QA Buddy) wires Chat Input straight into a Groq model with a QA-only persona. Flow 2 (Bug Classifier) inserts a Prompt Template between input and model: it defines four severity levels (CRITICAL, HIGH, MEDIUM, LOW), seven categories (UI/UX, FUNCTIONAL, PERFORMANCE, SECURITY, DATA, INTEGRATION, INFRASTRUCTURE), and forces a structured response with reproduction confidence and similar known issues. Both export as shareable JSON flows.
Visual Flow Canvas
Every component is a node on a canvas: chat input, prompt template, Groq model, chat output. Rewire the pipeline by dragging edges, no code needed.
Prompt Template Node
The classifier's brain is a single {bug_report} template encoding severity rules, category definitions, and the exact output sections to fill.
Structured Triage
Output is not free text: severity, category, priority, steps-clear check, environment check, and frequency come back in fixed sections you can route onward.
02 Architecture
03 Live demo (simulated with sample data)
This is a UI walkthrough with pre-baked sample data so you can feel the classifier without running LangFlow. In the course you import the two JSON flows into LangFlow and connect your own Groq API key.
04 Inputs & outputs
What goes in
- Raw bug reports pasted into the chat panel (any format)
- QA and test-automation questions for the QA Buddy flow
- Groq API key wired into the model node
- Tunable prompt template: severity rules, categories, output sections
What comes out
- Severity verdict: CRITICAL / HIGH / MEDIUM / LOW with rationale
- Category out of 7 (FUNCTIONAL, PERFORMANCE, SECURITY, …)
- Reproduction confidence: steps clear, environment, frequency
- Similar known issues and suggested next owner
- Exportable flow JSON your whole team can import