Practical guides to models, tooling, and applied ML workflows.
Use coding agents safely from repository inspection through focused implementation, verification, and review.
Understand the agent loop, the role of tools and memory, and the controls that turn model output into verified work.
Turn an AI-assisted business process into a bounded, reviewable workflow with evidence, approvals, and safe stopping conditions.
Core techniques for writing effective prompts for LLMs.
Artificial intelligence is a family of systems that infer useful outputs from data rather than following only hand-written rules. A practical AI product combines a model with context, tools, evaluation, safeguards, and human ownership.
A large language model predicts plausible token sequences from patterns learned during training. It can manipulate language impressively, but it is not a database of guaranteed facts or a person with intentions.
Retrieval-augmented generation first finds relevant source passages and then asks a generative model to answer with that context. Retrieval improves grounding, but poor chunks, search, permissions, or evaluation still produce unreliable answers.
AI can accelerate software work by drafting code, tests, summaries, plans, and risk lists, but it should operate inside explicit context, review, security, and verification boundaries.