Context engineering

• You need consistent outputs across many similar user journeys or markets.
• A model starts drifting after fine-tuning or platform updates and you must regain control fast.
• You want predictable UX without retraining a model for every change in product copy or policy.

Context engineering is the steering wheel for LLM behavior. The PM decides what information is mandatory, nice-to-have, or off-limits. Better context cuts hallucination, support costs, and review cycles while keeping latency in check. It also defines how easily designers and PMs can ship copy or policy updates without code changes, affecting release velocity and compliance risk.

Map every user-facing flow to a small set of context blueprints (instruction, business rules, persona, recency block, examples). Keep each block versioned and independently editable. Measure token weight per block to avoid performance hits, and guard with automated regression prompts. In 2026, pair context blocks with lightweight evals (accuracy + tone) that run on every content change before deploy.

A support copilot for a B2B SaaS uses a context blueprint: 1) system safety rules, 2) company-specific escalation policy, 3) user’s latest tickets, 4) product changelog from the last 7 days, 5) style guide. After adding a new premium tier, PM updates only the policy block; nightly evals show deflection rate holding at 41% while average latency stays under 900 ms.

• Stuffing every possible fact into the prompt instead of prioritizing relevance and freshness.
• Allowing design or legal to edit live prompts without guardrails or automated checks.
• Ignoring token cost/latency impact when adding new blocks, causing silent UX regressions.