In this hands-on episode, we implement a complete AI Observability & Evaluation system using Langfuse: tracing every prompt, detecting hallucinations with LLM-as-a-Judge, and measuring real accuracy with Precision, Recall & F1 Score.

This is Episode 35 of the AI-Driven Cyber Security Complete Course 2026 — Phase 4: Securing AI Systems (LLM Security).

In this project:
Building an AI app with the OpenAI API
Tracing prompts, responses, latency & activity with Langfuse
LLM-as-a-Judge — using AI to detect hallucinations automatically
Ground Truth, Support Score & Threshold — explained practically
Building a Confusion Matrix: True/False Positives & Negatives
Measuring Precision, Recall & F1 Score
Comparing thresholds & seeing their impact on hallucination detection

Full end-to-end build & test — not just an AI app, but a real system to monitor its accuracy & reliability. Built in Kali + VS Code.

By the end, you'll have a working AI evaluation system that proves whether your AI is accurate and trustworthy — a true production-grade, senior-level skill.

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CONNECT & LEARN
Website:
https://www.thetechzeen.com/

Source Code:
https://github.com/farzeen-ali/LangFuse

Full AI-Driven Cyber Security Playlist:
https://www.youtube.com/playlist?list=PLMnWzLCjYQQk

Lecture Notes:
https://github.com/farzeen-ali/AI-Driven-Cyber-Security

Cyber Security WhatsApp Community:
https://whatsapp.com/channel/0029VbCpEz6JkK75tDMYj43M

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