📊 If you can't measure your AI model's performance, you can't optimize it. ⚡

18d • News

Without evaluation, building AI applications is mostly just guesswork and opinions. 🧪

📍 This past Saturday in our Decoding Data Science *AI Residency (Cohort-10)* masterclass with Mohammad Arshad Sir, we tackled one of the most critical parts of building real-world **AI: LLM Evaluation and Observability.**

It’s easy to get a chatbot to answer one or two questions well in a notebook. But how do you know it won't hallucinate or deliver overly wordy answers when deployed to real users?

💡 To test this, I built and evaluated a specialized Recipe & Food Safety Assistant using #LangSmith. Here is what I put into practice/experiments:

⏩ Building Golden Datasets: Created a grounded benchmark set of 34 targeted Q&As covering FDA food safety rules and cooking guidelines.

⏩ LLM-as-a-Judge: Set up custom evaluators to automatically grade responses on two key metrics—Correctness and Conciseness.

⏩ Observability & Experiments: Traced full execution paths to track latency (P50/P99) and token costs across multiple experiment runs with models like gpt-4.1-nano.

🔹 Iterating on system prompts and measuring the exact impact was eye-opening. Seeing the correctness score jump from 86% to 91% across runs proved that systematic measurement beats random prompting every single time.

✅ Building connected, context-aware AI is one thing—making sure it behaves predictably in production is where the real engineering happens.

Excited to take these evaluation workflows into multi-agent systems next! 🚀

Grateful for the continuous hands-on learning in the DDS AI Academy! 💻

DDS Business Circle