Generative AI Applications for Business Training Course

Participants can create structured prompts that produce usable and reviewable business outputs.

• Prompt anatomy: role, task, context, constraints, examples and output format
• Applying zero-shot, few-shot and stepwise reasoning prompts appropriately
• Structured outputs using JSON schemas, tables and validation rules
• Separating system instructions, user requests and reference materials
• Iterative prompt refinement using test cases and acceptance criteria
• Prompt libraries with versioning, ownership and approved use conditions

Participants can design AI-supported research processes with traceable sources and verification checks.

• RAG architecture using embeddings, vector search, reranking and grounded responses
• Comparing public web search, enterprise search and curated knowledge bases
• Source citation checks for authority, currency, coverage and traceability
• Chunking strategies, metadata fields and access controls for retrieval quality
• Research workflow separating discovery, verification, synthesis and attribution
• Fact-checking generated claims against primary sources and internal records

Participants can specify human-reviewed AI workflows for common operational and knowledge tasks.

• Process decomposition identifying handoffs, exceptions, controls and automation candidates
• Designing human-in-the-loop workflows for high-consequence business decisions
• Copilots for email drafting, meeting preparation and document summarisation
• Document intelligence for contracts, policies, invoices and customer correspondence
• Multimodal analysis of forms, images, slides and recorded meetings
• Workflow specifications defining inputs, prompts, outputs, reviewers and escalation

Participants can use generative AI for analysis while checking calculations, assumptions and evidence.

• Natural-language analysis of spreadsheets, dashboards and operational data
• Code generation for SQL, Python and repeatable data transformations
• Analytic prompt patterns specifying metrics, filters, calculations and assumptions
• Validating AI-generated formulas, queries, charts and management narratives
• Scenario analysis with documented drivers, sensitivities and decision thresholds
• Data minimisation techniques for confidential datasets and personal information

Participants can produce and review AI-assisted business content against organisational quality standards.

• Brand voice guides translated into reusable instruction sets and examples
• Generating proposals, reports, policies and executive briefing materials
• Editing outputs for audience, evidence, tone, accessibility and legal review
• Image generation controls covering copyright, consent, provenance and disclosure
• Presentation development using narrative structures, slide outlines and visual prompts
• Content quality rubrics for accuracy, usefulness, originality and approved claims

Participants can apply practical governance controls to reduce business, security and compliance risks.

• NIST AI RMF functions: Govern, Map, Measure and Manage
• OWASP Top 10 risks for large language model applications
• Threat modelling prompt injection, data exfiltration, jailbreaks and insecure outputs
• Privacy impact assessment triggers for personal, confidential and regulated data
• Copyright, intellectual property and licensing checks for generated content
• Approval gates, audit logs and incident reporting for AI workflows

Participants can compare AI tools and define an appropriate pilot scope for business teams.

• Comparing foundation model providers, deployment options and commercial terms
• Build-buy-configure decisions using capability, cost, integration and risk criteria
• API, no-code and office-suite integration patterns for business teams
• Token consumption, rate limits and unit-cost estimation methods
• Vendor due diligence covering data retention, training use and security
• Pilot scope statements defining users, tasks, measures and exclusions

Participants can test AI-enabled workflows and measure their performance against defined standards.

• Evaluation datasets built from representative tasks, edge cases and failures
• Test criteria measuring factuality, relevance, safety, consistency and latency
• Red-team scenarios targeting misuse, harmful outputs and control bypasses
• Human review sampling plans with error taxonomy and escalation paths
• Before-and-after measurement of cycle time, rework, quality and adoption
• Experiment design distinguishing model effects from process and training effects

Participants can prepare a practical implementation plan for an approved generative AI use case.

• Stakeholder mapping for sponsors, users, risk owners and technology teams
• Role-based enablement plans covering prompts, controls and escalation routes
• Operating procedures for approved tools, data handling and output review
• Use-case portfolio prioritisation using value, feasibility, risk and readiness
• 90-day implementation roadmap with milestones, owners, measures and governance
• Personal application plan connecting one workflow to measurable business outcomes