

π Train the Next Generation: Capture RLHF Datasets from Agent Logs
βπ Class 7: Create Fine-Tuning Datasets with ZeroDBβs /rlhf/log
βMastering the AI-Native Stack: Build Faster, Smarter with AI-Powered Tools
βThis session teaches you how to turn real-world agent interactions into high-quality fine-tuning datasets using ZeroDBβs /rlhf/log
API.
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Whether you're building feedback loops, ranking agent responses, or collecting supervised training data, youβll learn how to structure and log experiences that power better model performance.
βπ§ What Youβll Learn
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What RLHF is and why it matters for agents
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How to structure reward-based and ranking logs
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How to capture prompt-response-reward triplets
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How to build a data flywheel for model improvement
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How to store, query, and export data for training
βπ§βπ» Live Coding
βLog a completion + score with
/rlhf/log
βAdd session IDs and metadata to training data
βExport RLHF logs into supervised fine-tuning format
βPreview example RL datasets with agent context
ββοΈ Tools Weβll Use
βπ ZeroDB
/rlhf/log
endpointβπ§ AINative Studio + RL dashboard
βπ§ͺ OpenAI-compatible fine-tuning formatter
βπ CSV/JSONL export for model training
βπ₯ Who Should Attend
βLLM fine-tuning engineers
βAI researchers
βProduct teams capturing user feedback
βAutonomous agent platform builders
βπ When?
βWednesdays β 1 hour, hands-on
Includes dataset walkthroughs, logging schema guides, and export demos
βπ― Why Join This Class?
βπ Capture production signals to train smarter agents
π Build datasets without manual annotation
𧬠Drive RLHF experiments with real user context
π Go from agent logs to fine-tuned models in weeks
ββ Class Takeaways
βTemplate RLHF logger (Python / JS)
βExport script for training-ready datasets
βRLHF reward schema best practices
βAccess to
/rlhf/log
dashboard and SDK snippet
βπ Sign Up Now
βSpaces are limited β claim your ZeroDB vector storage workshop seat! π Reserve Your Spot