LLM Routing & Evals Starter Pack (2026)

Purpose
Turn “which model should we use?” into an instrumented system with thresholds, fallbacks, and stable quality. This is written for a small team shipping an LLM feature in production.

1) Define your task map (one page)
- List 3–5 task types your product actually runs (e.g., extraction, summarization, tool-call planning, classification, long-context Q&A).
- For each task, define the output contract:
 - Format (JSON schema vs prose)
 - Allowed/required fields
 - Max length / tone constraints
 - Refusal rules (what it must not do)
- Assign a “risk tier” per task: low (internal), medium (customer-facing), high (compliance/security impact).

2) Pick routes (start with two per task)
For each task type, define:
- Route A (cheap/default): the lowest-cost model you believe can pass.
- Route B (escalation): a stronger model or alternate vendor.
- Explicit escalation triggers (no vibes): schema invalid, tool call fails dry-run, policy violation, low confidence, timeout, user reports.

3) Build acceptance tests (minimum viable eval set)
- Create a small “nasty set” per task: real examples that previously failed or are likely to fail.
- For each example, define pass/fail checks:
 - JSON parses and validates schema
 - No prohibited claims (e.g., invented citations)
 - Tool calls contain required args; endpoints exist
 - Refusal behavior triggers correctly
- Add regression discipline: every prompt change and model change must run this suite.

4) Instrumentation you need before scaling spend
Log these fields for every request:
- task_type, tenant_id (or workspace), policy_version
- chosen_model, vendor, prompt_version
- fallback_used (Y/N) and fallback_reason
- validator_results (pass/fail + error codes)
- latency bucket and retry/timeout events
Note: avoid storing raw sensitive text; prefer hashes or redacted excerpts.

5) Caching and cost control (the easy wins)
- Cache deterministic transforms (classification, formatting, extraction where possible).
- Cache retrieval results separately from generation.
- Add hard ceilings: max context length you allow; max tool calls per request; max retries.

6) Reliability drills (treat vendors like dependencies)
- Simulate vendor failures: timeouts, 429s, partial outages.
- Ensure graceful degradation paths: return partial results, ask user to confirm, queue async completion, or switch vendors.

7) A weekly operator routine (60 minutes)
- Review top 3 routes by volume.
- Review top fallback reasons.
- Add 5 new eval examples from real failures.
- Decide one routing change (tighten default, adjust escalations, improve validator) and ship it behind a flag.

If you do only one thing this month: implement explicit fallback reasons and log them. That single change turns routing from a belief into a system you can improve.