Week 1:

Objective: Define the visual-generation workflow and implement an initial capability for diagrams and static visuals.

Activities :

1. Visual Requirements: Define supported visual types, expected input formats, and output requirements.
2. Diagram Generation: Implement initial generation for flowcharts, conceptual diagrams, and structured visual layouts.
3. Static Visual Output: Enable generation and export of static visual assets for use in AI-generated materials.
4. Quality Review :Validate diagram readability, layout consistency, and alignment with the source content.

Deliverables :

• Initial diagram and static-visual generation workflow

• Support for flowcharts and conceptual diagrams

• Sample outputs and internal validation notes

Week 2: AI Memory Layer & Template Management

Objective : Create a reusable AI memory and template layer that helps retain and reuse approved structures, examples, and generation context.

Activities :

1. AI Memory Layer: Design and implement storage/retrieval for reusable context and approved reference material.
2. Template Library: Provide a library of templates and options for creating new templates.
3. Template Editing: Enable users to edit and update existing templates, subject to defined permissions.
4. Visual Knowledge Base : Organize visual references and reusable assets for retrieval during generation.
5. Course Samples & History: Store and retrieve sample courses and relevant generation history for reuse and review.

Deliverables:

• Initial AI memory layer with retrieval workflow

• Template library with create/edit options

• Visual knowledge base structure

• Course sample and history storage/retrieval

Week 3: Research Evidence, Trusted Sources & Plagiarism Reporting

Objective:

Improve research traceability by recording source evidence and providing plagiarism-check reporting within the agreed workflow.

Activities:

1. Evidence Capture: Record the supporting evidence gathered during the research phase and associate it with relevant generated content.
2. Trusted Sources: Maintain source references and source metadata so research outputs can be reviewed.
3. Source Traceability : Link claims or content sections to their supporting references where technically feasible.
4. Plagiarism Check : Integrate or implement the agreed plagiarism-check process and generate a report of its results.
5. Validation: Test evidence completeness, reference usability, and report consistency.

Deliverables

• Research evidence capture and source-reference workflow

• Trusted-source records linked to research outputs

• Plagiarism test report for checked content

• Validation results and known limitations

Week 4: Azure Infrastructure, Model Selection & Integration

Objective

Configure the Azure environment and integrate task-specific model selection with usable default settings.

Activities

1. Azure Infrastructure Setup: Configure the agreed Azure resources, environment settings, access, and deployment prerequisites.
2. Environment & Security Checks: Validate configuration, secrets/access handling, and connectivity for the deployed components.
3. Task-Specific Model Selection: Provide options to choose different supported models for different tasks.
4. Default Settings: Configure default model choices and task settings, with documented behavior when a model is unavailable.
5. Integration & QA Deliverables: Integrate the workstreams, test end-to-end workflows, resolve identified defects, and document limitations.

Deliverables:

• Azure infrastructure configured for the agreed environment

• Task-specific model selection options

• Default model settings and configuration documentation

• Integrated build, QA results, and deployment/configuration notes