We turn samples, labels, and prompts scattered across chat into a spec a colleague can take over and spot-check.
What You Get
A specification that labelers and engineers can use at the same time: label definitions, positive and negative examples, prompt templates, evaluation sets, and known failure types.
Who It's For
Teams doing classification, extraction, support, or content generation whose prompts only live in chat threads
Product owners outsourcing labeling and worried about drifting annotation standards
Companies that want a small evaluation before deciding between fine-tuning and a general model
Deliverables
Task definition: inputs, outputs, and judgments that are out of bounds
Annotation spec, boundary samples, and QA sampling ratio
Prompt templates split by scenario, with variable docs
Evaluation sets and failure case records
Handover notes: how to add a label, how to reject dirty data
How We Work
01
Define success with real samples
Start from the client's actual inputs and unacceptable outputs — not a blank slogan.
02
Trial-label a small batch
Write the spec, trial-label, and remove ambiguities before scaling up.
03
Freeze the templates
Prompts, variables, and refusal conditions live next to the evaluation set.
04
Hand over next-step advice
Keep labeling, revise prompts, or open a separate model training scope.
Not Included
Guaranteeing an unverified business metric for the model
Labeling personal data without authorization
Prompts intended to deceive users