Role-Based Programme
RB0381

Ollama for Quality Management

Private AI Workflows for Quality Analysis, Documentation & Continuous Improvement

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Duration
8 Hours
Level
Basic
Delivery
Instructor-Led
Format
Workshop

Programme Objectives

  • Build foundational proficiency in using Ollama and locally hosted language models for quality-management activities.
  • Apply private AI workflows to analyze SOPs, inspection records, non-conformances, audit information, and quality documentation.
  • Use structured prompting to support defect analysis, root-cause investigation, corrective-action planning, and quality reporting.
  • Explore local document-analysis and knowledge-retrieval approaches for sensitive quality information.
  • Apply responsible AI practices covering data confidentiality, traceability, validation, document control, model limitations, and human quality approval.

Tools covered

OllamaLocal LLM RuntimeOllama CLIModel LibraryModelfileOllama APIEmbedding ModelsLocal Document AnalysisPrompt Templates

Who should attend

  • Quality Managers
  • Quality Assurance Professionals
  • Quality Control Professionals
  • Quality Engineers
  • Quality Analysts
  • Quality Systems Professionals
  • Process Quality Professionals
  • Continuous Improvement Professionals
  • Operational Excellence Professionals
  • Internal Quality Auditors
  • Quality Coordinators
  • Process Improvement Professionals
  • Quality Team Leads

Prerequisites & Participant Readiness

  • Basic understanding of quality-management principles
  • Familiarity with SOPs, inspections, defects, non-conformances, audits, or corrective actions
  • Basic experience working with quality documents or operational data
  • Basic awareness of generative AI concepts is helpful
  • Familiarity with command-line tools is beneficial but not mandatory
  • No advanced programming expertise required
  • No previous Ollama experience required

TOC Modules

Concepts
  • Understanding local language models and Ollama's role in private AI processing
  • Comparing locally hosted AI with externally hosted AI services
  • Identifying quality-management use cases for documentation, analysis, audit, and reporting
  • Understanding model limitations and human quality accountability
Practical activities
  • Exploring an approved Ollama environment and available models
  • Running basic quality-related prompts locally
  • Comparing outputs from selected models
  • Building Quality Task → Local Model → AI Output → Quality Review workflow

Scenarios

Confidential Quality Defect to CAPA Support

Defect Records → Ollama Local Analysis → Recurring Pattern → Investigation Questions → Root-Cause Validation → Corrective Action → Verification Plan

Participants analyze sensitive quality information in a local AI environment, identify patterns requiring investigation, and structure corrective actions while keeping final root-cause and CAPA decisions under qualified quality ownership.

Internal Quality Audit to Management Review

Quality Procedures + Audit Evidence → Local Document Analysis → Potential Gaps → Auditor Validation → Action Plan → Quality Summary → Management Review

Participants use Ollama to review approved quality documents and audit evidence, organize potential findings, and create a management-ready quality summary with clear traceability to validated sources.

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