What is RLHF? Reinforcement learning from human feedback for AI alignment
This article explains how reinforcement learning from human feedback (RLHF) is used to train language models that better reflect human preferences, including practical steps and evaluation techniques.
Learn how LLM-as-a-judge works, when to use it (and when not to), common bias and failure modes, and research-backed best practices for building reliable evaluation systems.
Reinforcement learning: A guide to AI’s interactive learning paradigm
On this page What is reinforcement learning? The goal Online vs offline RL Taxonomy Core methods Benchmarks, metrics, and frameworks Advances and trends Successful applications…
Current best practices for training LLMs from scratch
Explore the foundational considerations for training large language models (LLMs) from scratch, including key trade-offs, pitfalls, and decision-making frameworks.
Retrieval-Augmented Generation (RAG) is a powerful technique in AI that combines large language models with real-time access to external data sources, allowing for more accurate,…
Explore various RAG techniques, from basic to advanced, and discover how chunking, indexing, and query transformation can elevate your AI's performance in complex use cases.
