Our AI and software practice treats LLM applications like any other production surface: they need SLOs, ownership, and evaluation—not only prompt craft. The same portfolio themes you would expect from serious MLOps apply: data boundaries, reproducibility, and monitoring that explains why a bad answer happened.
Value-driven scope. Start from workflows with measurable lift—support deflection, internal search, document drafting with human review—not from “chat with our entire knowledge base” as a science project.
Secure data handling. PII redaction, residency constraints, and tenant isolation are non-negotiable design inputs. Retrieval layers must respect authorization from source systems, not only from the UI.
Evaluation that engineers run. Golden datasets, automatic regression on prompts, and online checks for toxicity or leakage should live in CI alongside unit tests. If only researchers can run evals, they will not run at release time.
Agent-in-the-loop patterns. For high-stakes domains, pair automation with explicit human checkpoints and telemetry on override rates—those signals tell you when the model or the process is drifting.
RAG, tool use, and fine-tuning each have a place; the portfolio discipline is picking the smallest combination that meets the bar, then hardening. Fancy models rarely compensate for messy ingestion or missing observability.
Related: AI strategy & MLOps, DevOps consulting, and resources.