Model adaptation

Ayar

Research and data preparation

Research and data preparation for model evaluation and adaptation. Connects examples to source rules, expected tool behavior and organization-specific tasks.

Explore the interface concept Application scope

Information foundation: Approved rules and development examples
Operational outcome: Task- and source-linked training data
Human responsibility: Data selection and evaluation decisions

Inside the application

Application workflow

Selected use cases, source information and operational outputs.

Examples with known sources

Each example relates to a target tool, source rule and expected behavior.

AyarScenario / 01

Interface conceptResearch / conceptual

Training example

Structure definitionDATASET

1inputSource rule

2contextTask input

3reviewExpected tool

4outputExpected behavior

Conceptual field mapping, not executable code.

Decision boundary

Data selection and evaluation decisions

Approved rules and development examples

OutputTask- and source-linked training data

Planned expansion

Examples from knowledge, code, sessions and change history, along with additional training methods and adaptation runs, are part of the research plan.

AyarScenario / 02

Interface conceptResearch / conceptual

Research scope

Knowledge vault

Code examplesDefined dependency

Session historyDefined dependency

Adaptation planDefined dependency

Processing and control structure

Decision boundary

Data selection and evaluation decisions

Approved rules and development examples

OutputTask- and source-linked training data

Evaluate change on the same tasks

The aim is to assess tool selection and BPM rule adherence alongside training results.

AyarScenario / 03

Interface conceptResearch / conceptual

Model evaluation

Dimensions to evaluate

No result data

01Reference task

02Tool choice

03Rule compliance

04Error for review

Shows comparison criteria. Numerical charts require verified data and a defined method.

Decision boundary

Data selection and evaluation decisions

Approved rules and development examples

OutputTask- and source-linked training data

Application approach

Scope and operating approach.

The scope, operating approach and boundaries of this work.

Examples with known sources

Each example relates to a target tool, source rule and expected behavior. The initial working scope prepares supervised training examples from prompts.

Planned expansion

Examples from knowledge, code, sessions and change history, along with additional training methods and adaptation runs, are part of the research plan.

Evaluate change on the same tasks

The aim is to assess tool selection and BPM rule adherence alongside training results. A training and evaluation plan does not imply a completed production model.

Research scope: application behavior and business outcomes require pilot validation.

In everyday work

Reliable examples before adaptation

Evaluating better BPM behavior requires clear expectations about the rule and tool a model should use. Ayar begins with traceable training examples.

AyarInterface design concept

Model adaptation

Reliable examples before adaptation

Fictional sample data

01Example source

Approved rules and development examples

Source ruleBPM development rule

Expected behaviorCorrect tool and field selection

StatusData preparation and research

02Dataset preparation

Example sourceSource rule

Establish source and permitted use.

Dataset preparationTarget tool

Keep the same task and scope.

Evaluation planValidated example

Do not claim success without results.

03Evaluation plan

Illustrative review state

Data selection and evaluation decisions

Dataset expansion and adaptation runs are separate research stages. Planned examples from the knowledge vault, code and session history are not confused with the initial scope.

OutputTask- and source-linked training data

Example source

Examples with known sources

This component shows incoming information together with its identity and provenance. Supervised examples are prepared from approved prompt sources. Each connects to a source rule, target tool and expected behavior.

Dataset preparation

Planned expansion

This component explains how records are reviewed rather than displaying a result in isolation. Dataset expansion and adaptation runs are separate research stages. Planned examples from the knowledge vault, code and session history are not confused with the initial scope.

Evaluation plan

Evaluate change on the same tasks

This component distinguishes the user’s decision from the next work record. Evaluation should consider task outcomes and BPM rule adherence, not training loss alone. A research plan is not presented as a finished production model.

The whole operation

Information, decisions and outcomes stay connected.

These distinctions show the information the application receives, where it needs the user and what it leaves for the next operation.

Starting information: Approved rules and development examples
Human decision: Data selection and evaluation decisions
Resulting structure: Task- and source-linked training data

How do we assess its impact?

These are evaluation dimensions, not measured performance results. Comparisons use the same task types, data scope and human-review conditions.

Example traceability
Task-level rule adherence
Training and evaluation separation

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Ayar

Examples with known sources

Planned expansion

Evaluate change on the same tasks

Examples with known sources

Planned expansion

Evaluate change on the same tasks

Data selection and evaluation decisions

Examples with known sources

Planned expansion

Evaluate change on the same tasks

Information, decisions and outcomes stay connected.

How do we assess its impact?

Hafıza

Yerel

Fabrika

Discuss your project with us.