Plain-English definitions for the work we actually ship.

Generative AI is a class of machine learning models that produce new content, text, images, audio, video, code, in response to a prompt. In enterprise settings, it powers chatbots, document analysis, code assistants, and decision support, typically built on top of a foundation model like GPT, Claude, or Gemini.

MLOps is the engineering discipline that takes machine learning models from notebook prototypes to production systems. It covers CI/CD for ML, model registries, feature stores, monitoring, retraining, and the operational practices that keep AI working after launch.

Production AI is artificial intelligence deployed as a real system: running under SLA, monitored, secured, and operated by an on-call team, rather than a notebook prototype or proof-of-concept. The bar is the same as any production software: reliability, security, observability, and accountable ownership.

Retrieval-Augmented Generation (RAG) is an AI architecture that retrieves relevant documents from a knowledge base before generating an answer with a large language model. It grounds responses in your real data, reduces hallucinations, and lets you cite sources for every answer.

A vector database stores high-dimensional embeddings: numerical representations of text, images, or other data, and finds the nearest matches by similarity. It's the retrieval layer in most production AI systems, powering semantic search, RAG, recommendation engines, and any application that needs to ask 'what's most similar to this?'

Custom software development is the practice of designing, building, and operating software tailored to a specific organization's processes: instead of buying off-the-shelf SaaS or configuring an enterprise platform. Used when an organization's competitive advantage depends on a workflow that no vendor sells.

A senior software engineer is a technologist with at least six years of professional experience who can lead the design and implementation of production systems independently. Senior engineers own architectural decisions, mentor junior teammates, and operate the systems they build. They don't just write code to a spec.