Reasoning models are rewriting what AI can do. DeepSeek-R1, OpenAI's o3 series, and Anthropic's Claude-the frontier systems of 2025 and 2026-don't just predict the next token. They think. They self-correct, backtrack, and decompose complex challenges into logical steps to outperform systems trained purely on prediction.

Reasoning Model Engineering is the practitioner's guide to this new paradigm. It covers the theory of inference-time compute scaling, the practical engineering of chain-of-thought systems, and the hard-won lessons of production deployment-latency management, cost control, evaluation, and safety.

• Theoretical Foundations: RLHF, process reward models, and Monte Carlo Tree Search.
• Prompt & Agentic System Design: Engineering prompts, tools, and contexts that unlock peak reasoning performance.
• Production Architectures: Deploying high-concurrency, latency-sensitive systems.
• Cost & Latency Optimization: Strategies for long-context, long-thinking-chain models.
• Evaluation & Safety: Frameworks that measure reasoning quality rather than simple output accuracy.