AI safety spans containment of agentic systems, epistemic harms from sycophancy, skill atrophy from unreviewed code generation, and macro-level risks from rapid capability growth — each requiring different mitigations.

LLM engineering spans the full stack of building with large language models: training, inference optimization, agent architecture, harness design, and the operational tradeoffs that determine whether model capability translates into reliable software.

LLM fine-tuning adapts a pretrained model to a specific task or domain; current tooling ranges from from-scratch training guides to efficient local adapters to automated synthetic data pipelines that can beat larger models at a fraction of the cost.

LLM inference covers how language models generate tokens from a prompt — spanning hardware constraints, serving architecture, caching strategies, quantization, routing, and cost — and has become its own engineering discipline as scale and cost pressures intensify.

Multi-agent systems coordinate multiple LLM-backed agents to handle tasks too large or complex for a single context window, but empirical research shows failure rates of 41–87% in production, making coordination structure and verification as important as raw model capability.