LLM Inference Control Plane Checklist (2026)

Use this to turn “we added an LLM” into a system you can operate.

1) Routing policy (write it down)
- List your top request types (ex: summarize, draft, classify, support reply, code assist).
- Assign each type a risk level (low/medium/high) and an output contract (free text vs structured JSON vs tool calls).
- Define at least one fallback per type (stronger model, alternate provider, or human review).
- Decide upgrade/downgrade triggers: latency SLO breach, verifier failure, missing context, safety flag.

2) Context strategy (RAG + tools)
- Choose allowed sources for retrieval (explicit allowlist). Ban everything else by default.
- Define freshness rules (what must be real-time vs can be cached).
- Create a tool registry: tool name, purpose, required inputs, permission scope, logging requirements.
- Add a “no-context” path for tasks that should never touch internal data.

3) Output contracts (no vibes in production)
- For every workflow that touches systems of record, require structured output with a schema.
- Validate outputs automatically. If validation fails, retry once with a stricter instruction, then escalate.
- Record prompt/version + schema version for every response.

4) Evals as a gate (treat changes like releases)
- Maintain a golden set per workflow: real examples, expected structure, and known failure cases.
- Run regression evals before promoting any of: model change, prompt change, tool change, retrieval change.
- Track a small set of operator metrics: schema pass rate, tool-call success rate, fallback rate, human-review rate.

5) Safety & compliance controls
- Decide what you must log (and what you must never log). Set retention explicitly.
- Add PII redaction where appropriate (inputs and outputs).
- Define refusal and escalation behavior for high-risk categories (legal, medical, finance, harassment, self-harm).
- Ensure auditability: trace IDs that connect user request → retrieved docs → tool calls → model output.

6) Observability & incident response
- Implement traces that capture: route taken, model/provider, latency, tokens (if available), verifier results, fallback reason.
- Set alerts on: sudden fallback spikes, verifier failure spikes, provider error spikes, latency regressions.
- Create a rollback switch: pin a known-good model/prompt/toolchain quickly.
- Write an incident playbook: who gets paged, how to disable features, how to communicate.

7) Cost controls (engineer for a budget)
- Put per-user or per-workspace caps on expensive routes.
- Use caching where safe (prompt/result caching for deterministic tasks, and retrieval caching with TTL).
- Batch where possible (classification, embedding, offline summarization).
- Prefer small/open models for low-risk, high-volume tasks; reserve frontier models for hard cases.

8) Vendor strategy (avoid single points of failure)
- Document which workflows depend on which provider features (tool calling, structured output, embeddings).
- Keep an exit path: a second provider or self-host option for at least one critical workflow.
- Test policy differences: refusals, allowed content, logging defaults, region availability.

Deliverable (one-day version):
- A single text file that lists request types, routing rules, verifiers, and fallbacks.
- One golden set with 25–50 examples for your highest-volume workflow.
- A dashboard with three charts: latency by route, fallback rate, verifier pass rate.

If you can produce those three artifacts, you’re no longer “prompting.” You’re operating.