🚀 Train the Next Generation: Capture RLHF Datasets from Agent Logs
🎓 Class 7: Create Fine-Tuning Datasets with ZeroDB’s /rlhf/log
Mastering the AI-Native Stack: Build Faster, Smarter with AI-Powered Tools
This session teaches you how to turn real-world agent interactions into high-quality fine-tuning datasets using ZeroDB’s /rlhf/log API.
Whether you're building feedback loops, ranking agent responses, or collecting supervised training data, you’ll learn how to structure and log experiences that power better model performance.
🧠 What You’ll Learn
✅ What RLHF is and why it matters for agents
✅ How to structure reward-based and ranking logs
✅ How to capture prompt-response-reward triplets
✅ How to build a data flywheel for model improvement
✅ How to store, query, and export data for training
🧑💻 Live Coding
Log a completion + score with
/rlhf/logAdd session IDs and metadata to training data
Export RLHF logs into supervised fine-tuning format
Preview example RL datasets with agent context
⚙️ Tools We’ll Use
🔁 ZeroDB
/rlhf/logendpoint🧠 AINative Studio + RL dashboard
🧪 OpenAI-compatible fine-tuning formatter
📁 CSV/JSONL export for model training
👥 Who Should Attend
LLM fine-tuning engineers
AI researchers
Product teams capturing user feedback
Autonomous agent platform builders
🗓 When?
Wednesdays — 1 hour, hands-on
Includes dataset walkthroughs, logging schema guides, and export demos
🎯 Why Join This Class?
📈 Capture production signals to train smarter agents
📊 Build datasets without manual annotation
🧬 Drive RLHF experiments with real user context
🚀 Go from agent logs to fine-tuned models in weeks
✅ Class Takeaways
Template RLHF logger (Python / JS)
Export script for training-ready datasets
RLHF reward schema best practices
Access to
/rlhf/logdashboard and SDK snippet
📌 Sign Up Now
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