“fine-tuning”
Feedback Loops: Turning Human Edits into Agent Improvements
Every time a human rewrites your agent's output, you throw away a training signal. A feedback loop turns those edits into datasets, evals, and measurable model improvements.
Golden Trajectories: Curating Human-Approved Agent Runs
Build golden trajectory datasets by curating human-approved agent runs. Use these validated paths to fine-tune agents, create evals, and improve reliability.
Fine-Tuning Small Models for Edge Deployment
Big models don\u2019t fit on edge hardware, but small ones can learn your domain. A practical guide to fine-tuning Gemma-class small models and deploying them on-device.
Synthetic Data Generation for Testing and Fine-Tuning
Real data is scarce, private, and expensive to label. Learn how to generate high-quality synthetic data with LLMs — for unit tests, evals, and fine-tuning datasets.
Fine-Tuning vs. RAG: When to Use Which Technique
Fine-tuning changes how the model behaves; RAG changes what it sees. Use a checklist and a small offline eval to decide between the two — and when to combine them into a hybrid.
Hyper-Personalized SLMs: Local Fine-Tuning of Gemini 3 Nano Models
A model that runs on-device still has to be trained somewhere. Learn to fine-tune Nano SLMs locally with LoRA, QLoRA, MeSP, and GradCut — without ever shipping user data to the cloud.
Fine-Tuning 101: How to Teach a Small LLM New Tricks with Your Own Data
A beginner-friendly guide to fine-tuning smaller Large Language Models on custom datasets for domain-specific tasks.