A practical, data-driven guide comparing prompting vs. fine-tuning for LLM apps, with decision checklists, trade-offs, and implementation tips.
A clear, practical guide to RLHF—how human preferences train models, the pipeline, pitfalls, and modern variants like DPO and RLAIF.
A practical, end-to-end guide to designing, deploying, and operating embedding-based similarity search in production.
A clear, visual walkthrough of Transformer architecture—from tokens and positions to multi-head attention, residuals, and FFNs.
A practical, end-to-end tutorial for generating, evaluating, and governing synthetic data for ML using Python, SDV, and sdmetrics.
Step-by-step QLoRA guide with concepts, setup, memory tips, and code to fine-tune LLMs using 4-bit quantization on a single GPU.
