Brief safely
Sanitized context and one concrete reliability decision.
01Applied AI prototype sprint
Build the smallest prototype that can resolve the important assumption.
This sprint fits when the team has narrowed the technical question enough to justify implementation. The prototype is evaluated against an explicit baseline and leaves a clear integration or research-to-production boundary.
Reproduce
Turn traces, examples, or eval runs into replayable evidence.
Define gates
Separate blockers, warnings, thresholds, and ownership.
Leave a record
Connect the evidence to a ship, revise, or stop call.
02Scope ledger
What enters the engagement—and what leaves it.
A strong fit
01The important technical assumption and target workflow are already defined.
02The team needs a focused implementation, comparison, and evaluation surface.
03The goal is to establish feasibility and the boundary for a larger engineering path.
What your team keeps
01Technical design, baseline, and comparisonReusable within the agreed project scope.
02Working prototype or integration pathReusable within the agreed project scope.
03Evaluation surface for the prototypeReusable within the agreed project scope.
04Integration boundary and engineering handoffReusable within the agreed project scope.
05Known risks and follow-up recommendationsReusable within the agreed project scope.
03Working sequence
From uncertain behavior to an inspectable decision.
The sequence is deliberately legible: agree the boundary, reproduce what matters, then make the release rule explicit.
Confirm the research question, system boundary, baseline, and success criteria.
Build the smallest prototype capable of testing the assumption.
Compare behavior and attach evaluation coverage and known limits.
Hand off the engineering implication, integration boundary, and next decision.
04Open method
Inspect the reasoning before you engage.
These notes expose the decision framework behind the work. They are methodology, not disguised client proof.
01Technical noteHow to Decide Whether an AI Workflow Ships, Changes, or StopsA decision framework for AI teams that need evidence before shipping, revising, or stopping an agent, RAG, or model workflow.02Technical noteWhat an Evaluation Plan Should ContainThe minimum useful contents of an AI evaluation plan: target behavior, risks, inputs, replay rows, thresholds, and decision owner.
09Safe first step
Bring the decision your team needs to defend.
Describe the applied ai prototype sprint context in sanitized terms. We will use the first exchange to confirm fit, evidence available, and the safest next step.
No credentials, production data, customer records, or private repository access in the first brief.
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