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Generative AI

Generative AI for Working Professionals

A weekend upskilling course for working professionals who want to use generative AI safely and practically in business, product, operations, analytics, HR, marketing, and software workflows.

Request batch details and a fee quote. No enrolment commitment.

8 weeks
Weekend live
6 modules · 31 topics

What you'll achieve

Prompt systems, AI workflows, RAG concepts, LLM APIs, evaluation, governance, and role-specific productivity projects.

Who this course is for

  • Working professionals who want to use AI in daily work without becoming AI engineers
  • Managers, analysts, marketers, HR teams, operations teams, and product professionals
  • Developers and tech leads who need LLM API, RAG, evaluation, and governance context

Job roles to target

AI Workflow LeadBusiness AnalystProduct ManagerOperations ManagerAI Adoption ChampionTech Lead

Tools and skills covered

ChatGPTClaudePrompt systemsLLM APIsRAG conceptsEvaluation checklistsAI governanceWorkflow mapping

Plan your course before enrolling

Duration: 8 weeks. Format: Weekend live.

Ask admissions for the details specific to your course and batch:

  • Written fee quote, applicable taxes and payment schedule
  • Next start date, class days, timings and teaching location
  • Entry requirements, weekly practice time and laptop/software needs
  • Trial eligibility, cancellation terms and placement-support scope

Batch dates and fee amounts are not listed here. Review the details with admissions before making a payment. Read the refund policy.

Full Syllabus

1

Module 1: Generative AI Foundations

  • LLMs explained simply
  • Use-case selection
  • Limitations and hallucinations
  • Privacy and policy basics
2

Module 2: Prompt Systems

  • Role prompts
  • Context design
  • Reusable prompt libraries
  • Structured outputs
  • Review checklists
3

Module 3: AI for Daily Workflows

  • Research
  • Email and documentation
  • Meeting summaries
  • Reporting
  • Decision support
  • Automation handoffs
4

Module 4: LLM APIs and RAG Concepts

  • API basics
  • Embeddings overview
  • Vector database concepts
  • Retrieval-augmented generation
  • Knowledge base assistants
5

Module 5: Evaluation and Governance

  • Output quality checks
  • Bias and risk
  • Human review
  • Cost control
  • AI usage policy
  • Responsible adoption
6

Module 6: Capstone Workflow

  • Use-case brief
  • Prototype workflow
  • Stakeholder demo
  • Measurement plan
  • Rollout checklist

What You'll Build

These are the projects you'll complete during the course — each one is deployable, portfolio-ready, and designed to demonstrate real skills to hiring teams.

Note: Projects and tools may be updated between batches based on industry trends, trainer expertise, and current hiring patterns. The goal remains the same — you leave with work you can show employers.

Department AI Workflow Playbook

Map one team workflow, redesign it with AI support, and define quality checks plus human approval steps.

AI toolsWorkflow mappingPrompt library

Internal Knowledge Assistant Prototype

Design a RAG-style assistant plan for policies, FAQs, SOPs, or training material with source-grounding rules.

RAG conceptsEmbeddingsKnowledge base design

AI Governance Checklist

Create a practical policy checklist covering privacy, output review, permitted tools, and risky use cases.

AI governanceRisk reviewProcess design

Generative AI for Working Professionals training in Bengaluru

This course is built for Bengaluru fresher hiring patterns: fundamentals, hands-on projects, interview explanation, and portfolio proof. Learners can attend from Bengaluru and discuss batches, fees, and placement-readiness with the admissions team.

Format

Weekend live

Duration

8 weeks

Support

Projects + interview prep

Bengaluru fresher salary and hiring context

This is an upskilling course rather than a fresher placement track. The value is higher productivity, AI adoption capability, better internal projects, and stronger role relevance as teams adopt AI.

Salary ranges are directional and depend on background, project quality, communication, interview performance, and employer requirements.

What recruiters usually check

  • Can identify useful AI use cases
  • Can design reusable prompt systems
  • Understands RAG and source-grounding basics
  • Can evaluate AI output
  • Can discuss governance and privacy risks

Related career guides

Interview Prep

What Bangalore Hiring Managers Actually Look For (I Asked 12 of Them)

Straight from the people who decide whether you get the job — what matters, what doesn't, and what annoys them about fresher candidates.

Frequently asked questions

Is this course only for developers?

No. It is built for working professionals across business, operations, product, analytics, marketing, HR, and technology. Developers get extra value from the LLM API and RAG concepts.

Will I build an AI product?

The course focuses on practical AI workflows and prototypes. Learners create a department workflow playbook, knowledge assistant plan, and governance checklist.

Why should experienced professionals learn generative AI now?

AI is becoming part of daily work. Professionals who can apply it safely, measure impact, and guide teams will have an advantage over those who only use generic prompts.

Ready to start Generative AI for Working Professionals?

Get batch details, fees, and a personalized learning plan from our admissions team.

Admissions open

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