Learn how LLMs generate responses

Understand high-level architecture

Compare models using business criteria,

Match LLM strengths to real use cases, and improve output quality by fixing causes of incomplete or incorrect responses

Design clear prompts using zero-shot, few-shot, and role-based methods

Improve reliability with anchors and iterative refinement

Generate structured outputs, and fine-tune style and depth using generation controls

Categories of AI tools for business, selecting tools by value–simplicity–cost–safety,

Understanding simple integrations

Mapping tools to daily workflows, and staying updated on emerging tools and trends

Identify parts of a workflow to automate

Break them into clear steps

Build multi-step no-code automations with alerts and approvals, and apply them across HR, Ops, Sales, Product, Finance, and Marketing

Turn one long document into multiple usable assets

Create cross-functional communication outputs

Build automated presentations, and generate auto-updating decks with new data and insights

Structure and prepare data for dashboards

Connect multiple data sources

Build automated dashboards with KPI views

Connect dashboards to AI for insights

Identify patterns–trends–anomalies

Auto-generate weekly leadership summaries

Turn data into decisions and clear next steps

Pull and summarise market signals, auto-compare competitors and track weekly changes, turn multi-source data into trends–opportunities–risk alerts, and generate decision-ready summaries, scorecards, and opportunity maps

Create assistants that understand your role

Inject context using documents (PRDs, policies, SOPs, reports)

Set tone–style–memory for consistent responses

Explore how assistants support HR, Sales, Ops, and Customer Success

Turn policies, reports, and SOPs into a searchable knowledge base

Make AI give answers backed by documents

Organise documents for fast and accurate retrieval

Explore RAG use cases across HR, Ops, Sales, and Support

Connect AI to multiple sources (PDFs, Sheets, websites, dashboards)

Make AI read from all locations and produce one combined answer

Improve accuracy when information is split across multiple sources

Create multiple agents that work together on end-to-end workflows

Use MCP for safe file access and data fetching

Define clear agent roles

Set consistent tone and memory

Add guardrails for accuracy and permissions

Enable smooth handoffs between agents

Responsible AI principles & ethical risk identification

Bias–fairness–privacy safeguards

Data protection in practical AI usage

Output verification with human-in-the-loop controls

Building trustworthy and compliant AI workflows

Combine your AI assistant, knowledge base, and automations into one workflow

Solve a complete real-world business problem end-to-end

Create clear handover guides and SOPs

Present a final demo with a simple impact report