Capital Skill Works

AI & Automation

Practical AI, Automation & Agentic AI

Learn how to use modern AI tools, automation platforms and AI agents to solve real-world business and productivity problems.

Duration
12 weeks / 60 teaching days (3-month intensive)
Level
Beginner to Applied Intermediate
Learning mode
Physical
Course fee
PKR 27,000
Monthly fee
PKR 9,000 per month
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Illustration of a workspace with a data map and automation diagram on screen

Course overview

12 weeks / 60 teaching days (3-month intensive). Practical model: 90% hands-on lab / 10% focused theory.

Capstone: A working business AI workflow/agent with defined inputs/outputs, tool use, reliability checks, human approval where needed, evaluation set, security notes and demo.

Completion standard: Minimum 80% attendance and submission of at least 80% of daily practical evidence. Pass the final capstone using the instructor QA rubric; incomplete templates alone do not count as a capstone. Demonstrate the final work live and answer practical troubleshooting questions. Submit an organized portfolio/case study that explains the problem, process, final output and lessons learned.

What you will learn

  • Prompt AI systems with clear context, constraints, examples and evaluation criteria.
  • Use AI for research, office tasks, data, content and structured decision support.
  • Build no-code automations and AI workflows with error handling and human review.
  • Explain agent concepts, tools, memory, RAG, guardrails and evaluation in practical terms.
  • Design and demonstrate a useful business AI solution with documentation and risk controls.

Who should join

  • Students
  • Office workers
  • Freelancers
  • Business operators
  • Non-developers who want applied AI skills

Tools and technologies

  • Current leading AI assistants
  • Spreadsheets/docs
  • N8n or equivalent automation platform
  • GitHub/Copilot concepts
  • Approved connectors
  • Sandbox data

Complete curriculum

1. Week 1 — Modern AI Foundations

Five practical lessons, ending with the Week 1 integrated challenge: Create a personal prompt workspace.

  1. 1. What generative AI can and cannot reliably do

  2. 2. LLMs, context windows and multimodal AI mental model

  3. 3. Privacy, safety and responsible use

  4. 4. Fact-checking, citations and source verification

  5. 5. Create a personal prompt workspace

2. Week 2 — Prompt Engineering

Five practical lessons, ending with the Week 2 integrated challenge: Prompt engineering practicum.

  1. 1. Clear instructions, context and constraints

  2. 2. Examples and output formatting

  3. 3. Decompose complex tasks

  4. 4. Critique, iteration and self-review

  5. 5. Prompt engineering practicum

3. Week 3 — AI for Research and Learning

Five practical lessons, ending with the Week 3 integrated challenge: Turn documents into action plans.

  1. 1. Turn a goal into research questions

  2. 2. Summarize and extract structured information

  3. 3. Comparisons and decision matrices

  4. 4. Use AI as a tutor and study assistant

  5. 5. Turn documents into action plans

4. Week 4 — AI for Office Work and Data

Five practical lessons, ending with the Week 4 integrated challenge: Presentation and meeting preparation.

  1. 1. Professional writing and editing

  2. 2. Spreadsheet formulas and data assistance

  3. 3. Data cleaning and categorization

  4. 4. Analysis, charts and insight extraction

  5. 5. Presentation and meeting preparation

5. Week 5 — AI Content and Media

Five practical lessons, ending with the Week 5 integrated challenge: Multimedia mini-project.

  1. 1. Ideation and content-calendar workflow

  2. 2. Long-form content workflow

  3. 3. Image prompting

  4. 4. AI image editing and variation

  5. 5. Multimedia mini-project

6. Week 6 — No-Code Automation Foundations

Five practical lessons, ending with the Week 6 integrated challenge: Automation mini-project.

  1. 1. Triggers, actions and data flow

  2. 2. No-code automation builder basics

  3. 3. Data mapping between apps

  4. 4. Conditions, branching and error handling

  5. 5. Automation mini-project

7. Week 7 — n8n and AI Workflows

Five practical lessons, ending with the Week 7 integrated challenge: AI workflow project.

  1. 1. Nodes, credentials and executions

  2. 2. Webhooks and form-triggered workflows

  3. 3. Connect a language model

  4. 4. Structured outputs and reliability

  5. 5. AI workflow project

8. Week 8 — Agentic AI

Five practical lessons, ending with the Week 8 integrated challenge: Agent evaluation and failure testing.

  1. 1. Agents versus chatbots and fixed workflows

  2. 2. Tools and tool selection

  3. 3. Memory and context management

  4. 4. Human-in-the-loop approvals

  5. 5. Agent evaluation and failure testing

9. Week 9 — GitHub and AI Development Literacy

Five practical lessons, ending with the Week 9 integrated challenge: Build an AI-assisted mini tool.

  1. 1. GitHub basics for non-developers

  2. 2. Copilot prompting and code explanation

  3. 3. AI coding-agent task and review workflow

  4. 4. Documentation generation

  5. 5. Build an AI-assisted mini tool

10. Week 10 — Knowledge Systems and RAG

Five practical lessons, ending with the Week 10 integrated challenge: Create an evaluation set.

  1. 1. Knowledge-base source quality

  2. 2. RAG mental model

  3. 3. Build a document Q&A assistant

  4. 4. Guardrails, permissions and access

  5. 5. Create an evaluation set

11. Week 11 — Business AI Solutions

Five practical lessons, ending with the Week 11 integrated challenge: Client AI solution workshop.

  1. 1. Customer-support use case

  2. 2. Sales and lead-management use case

  3. 3. Admin and operations use case

  4. 4. ROI, cost and risk assessment

  5. 5. Client AI solution workshop

12. Week 12 — Final AI Capstone

Five practical lessons, ending with the Week 12 integrated challenge: Final demo, portfolio case study and skills review.

  1. 1. Solution architecture and success criteria

  2. 2. Build the core workflow or agent

  3. 3. Add reliability, logging and approval controls

  4. 4. Evaluation, security and documentation

  5. 5. Final demo, portfolio case study and skills review

Upcoming batches

No batch is published for this course yet

Contact Admissions to discuss the next available batch or your preferred schedule.

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Practical learning and projects

  • Create a personal prompt workspace
  • Prompt engineering practicum
  • Turn documents into action plans
  • Presentation and meeting preparation
  • Multimedia mini-project
  • Automation mini-project
  • AI workflow project
  • Agent evaluation and failure testing
  • Build an AI-assisted mini tool
  • Create an evaluation set
  • Client AI solution workshop
  • Final demo, portfolio case study and skills review

Prerequisites

  • No specific admission requirement currently.

Who teaches this

Your instructor

Instructor information for this course will be announced. Admissions can tell you who is scheduled to teach an upcoming batch.

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