A practical, end-to-end guide to detecting and mitigating hallucinations in LLM outputs, from uncertainty signals to retrieval-based verification.
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 clear, visual walkthrough of Transformer architecture—from tokens and positions to multi-head attention, residuals, and FFNs.
Step-by-step QLoRA guide with concepts, setup, memory tips, and code to fine-tune LLMs using 4-bit quantization on a single GPU.
A practical blueprint for deploying autonomous AI agents to production—architecture, safety, reliability, evals, cost control, and ops patterns.
