Context Engineering Is the New Core Discipline in Healthcare AI

The term "prompt engineering" dominated AI discourse for two years. In healthcare, it was always insufficient. What matters in regulated environments is not how you phrase a question to an LLM — it is the entire context architecture surrounding that model: what data it can access, what constraints it operates under, what audit trail it produces, and what happens when it is wrong.

Context engineering treats the AI system as an orchestration problem, not a prompting problem. For healthcare payers processing millions of claims, the context window must include member eligibility history, provider contract terms, clinical policy references, and CMS regulatory requirements — all structured, all versioned, all auditable. A clever prompt cannot substitute for a missing data pipeline.

The shift matters because healthcare AI failures are rarely model failures. They are context failures. A prior authorization decision that ignores a member's existing treatment history is not a hallucination problem — it is an integration problem. The model never had the information it needed to make the right call.

Within HealthNext, the healthcare practice of LockedIn Labs, every AI workflow begins with context architecture: the information the model needs, its authoritative source, the retrieval path, and the validation gate before an output reaches a person or triggers an action. This is the discipline behind production healthcare AI.

The memory layer is where that context discipline becomes governance. Once a payer workflow needs to remember intake history, prior review notes, or unresolved exceptions across sessions, the question stops being about convenience and starts becoming a decision about retention windows, source authority, deletion paths, and reviewer access. For the broader enterprise framing behind that shift, see LockedIn Labs' companion briefing on AI memory and retention contracts.