Collaborative AI agents split a task across multiple agents that coordinate toward a shared outcome. How they work, what implementing them involves, and when a single agent is enough instead.
Training your own AI model on your own data means a different process than starting from scratch. The steps, how long it takes, what it costs, and when it's worth it.
LLM-powered autonomous agents plan, act and adjust with minimal human input. The core components behind them, how they work, and what deploying one actually involves.
Model serving is what turns a trained model into something an application can actually call. How it works, how it differs from deployment, and how to do it reliably at scale.
LLM agent architecture is the arrangement of components (planning, memory, tools) that determines what an agent can do. The common patterns, and how to choose.
LLM infrastructure is the compute, storage and serving stack that makes training and running large language models possible. Its components, how to build it, and what it costs.
AI orchestration coordinates multiple models, agents and tools into one working system. How it works, its core components, and when an organization actually needs it.
AI routing sends each request to the model or tool best suited to handle it, instead of one model for everything. How it works, common strategies, and where implementations go wrong.
AI is changing how mineral exploration targets get generated and ranked. How AI-powered methods compare to traditional ones, what data they run on, and where they still fall short.
Testing generative AI applications means covering risks static software testing doesn't touch: hallucination, non-determinism, unsafe output. The methods, tools and challenges involved.