Role-Based Programme
RB0382

Ollama for Quality Management

Build Private AI Workflows for Quality Analysis, QMS Knowledge & Continuous Improvement

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Duration
16 Hours
Level
Intermediate
Delivery
Instructor-Led
Format
Capability Training

Programme Objectives

  • Develop functional proficiency in applying Ollama-based AI workflows across quality documentation, non-conformance analysis, CAPA, audits, and continuous improvement.
  • Select and evaluate suitable AI models based on quality, privacy, performance, infrastructure, and business requirements.
  • Build controlled QMS knowledge workflows using embeddings, semantic retrieval, and approved quality documentation.
  • Create structured and reusable AI-assisted workflows for defect classification, issue analysis, quality records, and action management.
  • Apply model validation, information security, traceability, human review, and governance before operational adoption.

Tools covered

OllamaOllama Model LibraryLocal & Cloud ModelsOllama APIEmbeddingsStructured OutputsTool CallingWeb Search Integration

Who should attend

  • Quality Managers
  • Quality Assurance Managers
  • Quality Engineers
  • Quality Analysts
  • Quality Systems Professionals
  • Quality Control Professionals
  • CAPA Professionals
  • Quality Auditors
  • Supplier Quality Professionals
  • Continuous Improvement Professionals
  • Operational Excellence Professionals
  • Quality Technology / Automation Professionals
  • Quality Team Leads

Prerequisites & Participant Readiness

  • Working understanding of quality-management processes
  • Familiarity with SOPs, quality records, defects, non-conformances, CAPA, and audits
  • Basic understanding of generative AI and prompting is recommended
  • Basic awareness of APIs, command-line tools, or technical integrations is helpful
  • Access to representative non-sensitive quality documents and records for practice is beneficial
  • No previous Ollama course completion required

TOC Modules

Concepts
  • Understanding Ollama as an environment for running and integrating multiple AI models
  • Comparing local model execution with optional cloud-model execution
  • Mapping AI opportunities across Detect → Analyse → Investigate → Correct → Verify → Improve
  • Understanding model size, capability, compute requirements, privacy, and response-quality trade-offs
Practical activities
  • Installing or accessing Ollama and running a representative quality-management task
  • Mapping Quality Process → AI Use Case → Model Requirement → Human Validation

Scenarios

Quality Records to Root-Cause & CAPA Workflow

Non-Conformance Records → Ollama Structured Classification → Trend Analysis → QMS Evidence Retrieval → Root-Cause Hypotheses → Quality Validation → CAPA → Effectiveness Criteria → Closure Review

Participants combine structured outputs, quality evidence, and controlled model reasoning to accelerate investigation while keeping root-cause confirmation, corrective-action approval, and final closure under qualified quality ownership.

Controlled QMS Knowledge to Quality Assurance Assistant

Approved SOPs & Procedures → Embeddings → Semantic Retrieval → Ollama Model → Source-Grounded Quality Response → Human Verification → Tool-Assisted Action → Audit Trail

Participants design a private quality-assurance assistant that retrieves relevant controlled documentation and produces structured support for quality teams while enforcing source validation, access controls, and human approval before consequential actions.

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