Verifiedon 2026.7.1
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Practical outcome
What this gives you
- Classify a workload against explicit quality, privacy, latency, and budget constraints
- Build a repeatable five-case benchmark and preserve its decision evidence
- Define capacity, quota, routing, fallback, and regression controls for a bounded route
- Produce a workload-specific model decision record with an owner and review date
Preflight
Before you start
- CLI Essentials and Operator Diagnostics
- A working non-production OpenClaw installation
- A representative but non-sensitive workload
Syllabus
Work through the map
- Classify the workload before choosing a model20 min
- Map the local inference boundary and GPU constraints20 min
- Build a benchmark that can reject your favourite model20 min
- Measure context, latency, throughput, and concurrency separately20 min
- Route by policy and enforce a budget20 min
- Test fallback, regression, and the model decision record20 min
- Final assessmentAfter coursework
Course details and supporting evidence
What you will build
This course replaces model tribalism with a decision record another operator can inspect. You will define a bounded workload, run a five-case benchmark, map data and capacity boundaries, set a route policy and quota, exercise fallback in non-production, and schedule the next review.
Course map
- Workload classification — define the result, hard constraints, owner, and stop condition before looking at candidates.
- Hosted/local boundary and GPU constraints — map request movement and record the capacity assumptions a local route must survive.
- Benchmark design — compare candidates with fixed fixtures and a scorecard that can disqualify your favourite.
- Context and performance — separate queueing, processing, first useful output, completion, and concurrent load.
- Cost controls and routing — turn observations into a named policy, budget ledger, quota response, and escalation path.
- Fallback and regression — test failure behaviour and submit the decision record with a dated review.
Assessment
The three checks cover benchmark design, budget/quota reasoning, and fallback policy. The capstone is a model decision record. It is assessed on measurement quality (30%), appropriate choice (25%), controls (25%), and review/fallback evidence (20%). A record fails if it hides exclusions, lacks a hard constraint, or treats an untested fallback as reliable.
Publication scope
This course publishes a provider-neutral decision and measurement method for OpenClaw 2026.7.1. It makes no universal performance, quality, privacy, or savings claim; learners must supply workload-specific measurements and current provider or runtime evidence.