1.5-day foundation · No technical background required
Generative AI Fundamentals
Understand generative AI well enough to ask better questions, reject unsuitable use cases and participate confidently in adoption decisions—without learning model mathematics or code.
- Duration
- 1.5 days · 12 hours
- Audience
- Managers and non-technical professionals
- Purpose
- Understand capabilities, limits and fit
- Prerequisite
- No coding or AI background
What is the Generative AI Fundamentals course?
Generative AI Fundamentals is 12 hours of instructor-led generative AI training for business, designed for managers and non-technical professionals. It explains how large language models generate responses, where tools can assist knowledge work, why hallucinations and data risks occur, and how to assess a use case before adoption. Participants leave with a prioritised opportunity map and a practical responsible-use checklist—not a promise that every task should be automated. As a GenAI fundamentals course, it also suits senior teams looking for generative AI for leaders without technical detail.
Is this the right course for you or your team?
Who it is for
- Managers and department heads beginning an AI initiative
- HR and L&D teams planning workforce AI literacy
- Business professionals who need a durable mental model before tool training
- Cross-functional teams evaluating generative-AI opportunities
Situations it fits
- The conversation swings between hype and fear
- Teams use AI without understanding limitations
- Leaders need to compare use cases before funding them
- A policy exists but employees do not know how to apply it
Prerequisites
- No technical background required
- Bring one recurring work task or decision to assess
- Use only approved or neutral examples in exercises
When a different course fits better
Choose a more specialised programme when the objective is execution rather than literacy.
- ChatGPT & Claude for Business Teamsfor hands-on prompting and workplace workflows
- AI Implementation for Teamsfor leadership teams building an organisational rollout plan
- Production AI Engineeringfor developers building and operating AI systems
The questions a foundation course should answer
The course replaces vague AI language with practical distinctions participants can use at work.
““How does it know that?””
Fluent output is mistaken for stored knowledge or verified reasoning.
What you practise
Use a plain-language model of training, prompting, context and probabilistic generation.
““Is this a good AI use case?””
Tasks are selected because they sound impressive rather than because success can be defined.
What you practise
Assess task pattern, data sensitivity, error cost, review path and measurable value.
““What can go wrong?””
Hallucination, bias, privacy and over-reliance are discussed only after deployment.
What you practise
Map foreseeable failures and human checks before a pilot begins.
““What should we do next?””
A long tool list produces no adoption decision.
What you practise
Prioritise a small opportunity, define ownership and write a testable next step.
What participants will be able to do
Practise the skills below through course exercises, then check the results and explain how you would apply them to your work.
Explain generative AI plainly
Describe what an LLM does, how context affects output and why responses can be wrong.
Separate capability from hype
Identify tasks suited to drafting, extraction, transformation or assistance—and tasks needing another approach.
Evaluate a use case
Assess value, data, error cost, human review and adoption requirements before recommending a pilot.
Recognise common risks
Identify hallucination, bias, confidentiality, intellectual-property and over-reliance concerns.
Ask better vendor questions
Question data handling, access, evaluation, monitoring, ownership and failure handling.
Create a practical next step
Produce a prioritised opportunity map with an owner, boundary and success measure.
Applied to each team’s real work
Leadership and strategy
Opportunity selection, investment questions, risk ownership and decision rights.
HR and L&D
AI literacy, role impact, learning pathways and responsible-use communication.
Functional managers
Task mapping, pilot selection, human review and adoption expectations.
Risk, legal and IT partners
Shared terminology for data, access, evaluation and control discussions.
What participants learn to reason about
The programme demonstrates current tools, but the learning outcomes are deliberately portable across providers.
Large language models
Core concept
A non-mathematical model of tokens, training, prompts, context and generated output.
Covers
- Why responses vary
- Context and instruction effects
- Why confidence is not verification
What to verify
Can the participant explain why an answer may be plausible and unsupported at the same time?
Use-case selection
Core decision skill
A repeatable way to compare tasks before choosing a pilot.
Covers
- Value and frequency
- Data sensitivity and error cost
- Human review and success measures
What to verify
Does the proposed use case have a clear owner, boundary and measurable task outcome?
Responsible use
Core operating skill
Practical controls for approved data, review, disclosure and escalation.
Covers
- Information boundaries
- Human accountability
- Failure reporting and monitoring
What to verify
Can a participant say what must be checked, by whom, before output is used?
1.5 days · 12 live hours
Detailed curriculum
4 modules, each pairing short instruction with guided practice. Sequence and examples can be adjusted after the training-needs discussion; the core outcomes stay the same.
Module 13 hours What generative AI is—and is not
- AI, machine learning and generative AI distinctions
- How LLM output is generated
- Capabilities, hallucinations and context limits
Practical activityExplain an AI response to a colleague without anthropomorphising the model.
Module 23 hours Business tasks and use-case selection
- Drafting, extraction, transformation and assistance patterns
- Function-specific examples
- Value, feasibility, risk and review criteria
Practical activityPrioritise several work tasks and reject at least one unsuitable use case.
Module 33 hours Responsible use and governance basics
- Confidentiality and approved accounts
- Bias, hallucination and human accountability
- Govern, map, measure and manage as risk questions
Practical activityWrite boundaries and review steps for one proposed use case.
Module 43 hours From literacy to a practical next step
- Pilot scope and success measures
- Stakeholders and decision rights
- Learning, communication and review plan
Practical activityPresent a concise opportunity map and 90-day next-step outline.
Need a learning plan around your goals?
Share your experience, stack and goals. The proposal sets out the suitable modules.
Exercises and practical outputs
Short explanations alternate with demonstrations, decision exercises and peer review.
How every module runs
Understand
Build a durable mental model without unnecessary mathematics.
Compare
Examine suitable and unsuitable uses side by side.
Challenge
Locate missing evidence, risky data and weak ownership.
Decide
Recommend a bounded next step rather than a generic AI ambition.
Exercises use realistic examples with no sensitive data. What participants make in the workshop is for practice and review, and anything they keep is their own working material.
Useful AI needs an owner, evidence and clear limits
Your people learn to build risk checks and review into the work itself, not to add a warning once a pilot is already live.
We use the NIST AI Risk Management Framework as an optional reference. It is not legal advice and does not replace your own legal, privacy, security and compliance review.
Name the task and the owner
Say what the task is, who it affects, who is responsible and which decisions the tool may or may not influence.
Protect information
Use only approved data and accounts. Follow your company’s rules on confidentiality, retention and access before you try anything.
Measure the real task
Pick quality, risk and take-up measures that fit the real task, and write down what you cannot measure yet.
Review and improve
Watch for failures, user feedback and changes, then tighten controls or stop using the tool when you need to.
How the course is tailored and delivered
We shape private training around how far along you are with AI, your approved tools, who decides what, the roles involved and the projects already under way.
From first conversation to evaluation
Agree the decision
Agree what your people must decide, design or put in place after the programme.
See where you are now
Look at roles, tools, policies, data limits and any AI or automation work already running.
Pick the examples
Choose realistic examples that are useful without exposing restricted information.
Agree the handover
Confirm the document, its owner, who reviews it and the next check-in after training.
Delivery formats
Public live-online cohort
Best for: Individuals joining a published batch with people from other companies
Live Zoom sessions, with notes, assignments, lab guides and three months of recordings on the Technovids LMS.
Private corporate programme
Best for: Teams that want examples from their own company and a shared piece of work
Live online or at your office. Scope, group size, materials, support and price are confirmed in writing.
Delivery modes
Live online
Trainer-led sessions with group work, shared documents and live review.
Onsite
Private training at your office, with the same exercises and review.
Discussion, practice and decisions—not a lecture marathon
Technovids training sessions with different groups. The photos do not name a client, course or result.
Photo 1 of 10: Hands-on session 
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Photo 5 of 10: Classroom session 
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Photo 7 of 10: Computer-lab session 
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Course details at a glance
The published course parameters on one page. Final scope, dates and fees are confirmed in the written proposal.
Discuss your training goals| Course | Generative AI Fundamentals |
|---|---|
| Duration | 12 live hours across 1.5 days or an agreed equivalent schedule |
| Audience | Managers, HR/L&D, functional professionals and cross-functional teams |
| Prerequisite | No coding, mathematics or prior AI experience required |
| Output | Opportunity map, risk questions and a bounded next-step plan |
| Delivery | Public live online; private live online or onsite |
Who may deliver this programme
We choose a trainer whose subject knowledge and teaching experience suit your group and the agreed scope.
Pankaj Rana
Co-founder & Lead Instructor, Technovids Consulting
Pankaj is a co-founder and the lead instructor at Technovids Consulting. His programme work spans practical AI adoption, AI engineering, evaluation, responsible production AI, data analytics and corporate learning design, with an emphasis on hands-on exercises connected to workplace tasks.
Expertise:
- Practical AI adoption
- Prompting and evaluation
- Responsible AI
- Corporate learning design
Junaid Khateeb
AI & Machine Learning Trainer, Strategist and Consultant
Junaid is an AI and machine-learning trainer, strategist and consultant with more than 22 years of training experience. His work combines enterprise AI education, business-centred adoption, responsible AI practices and applied solution design, helping professional audiences connect AI concepts to real organisational needs.
M.E. in Computer Engineering, specialising in Artificial Intelligence · author of programming books on Python, Java and C++
Expertise:
- Enterprise AI and ML training
- AI strategy and adoption
- Responsible AI
- Applied AI solution design
Pradeep Varadarajan
Technical Trainer & Curriculum Architect
Pradeep is a technical trainer and curriculum architect with more than 20 years of experience across enterprise technology, cloud infrastructure and advanced AI systems. He designs practitioner-focused programmes, labs and assessments covering AI, LLM systems, Python and machine learning, translating complex technical subjects into structured learning experiences.
Bachelor of Engineering in Electrical Engineering · instructor-led, virtual and blended delivery
Expertise:
- AI and LLM systems
- Curriculum architecture
- Python and machine learning
- Hands-on lab design
The trainer for your sessions is confirmed with the dates or in the written proposal.
To book a trainer for your team, call +91 86183 46384 or email corporate@technovids.com.
Updated
Organisations we have trained, and participant perspectives
Feedback shared with permission, and clients who agreed to be named, show how we deliver. They are not a promise of any particular result.
Organisations we have delivered training for
Hands-on training for people and teams in AI, data, cloud, productivity and workplace skills.
Participant experiences across workplace roles
What people we trained found useful in their day-to-day work.
- Gauraw DeshmukhData Engineer · Capgemini
Technovids AI training was very practical and well structured. The sessions helped me understand how AI concepts can be applied in real data engineering and business workflows, instead of only learning theory. The trainer explained the topics clearly with relevant examples, which made the learning experience valuable and easy to follow.
- PriyanshaSoftware Developer
The AI training from Technovids helped me gain a better understanding of Generative AI, prompt engineering, and practical AI use cases for software development. The course was clear, interactive, and focused on real-world applications. It gave me confidence to explore AI tools more effectively in my work.
- Sidda RajuData Engineer
Technovids delivered the AI training in a very practical and industry-relevant way. As a data engineer, I found the examples and hands-on discussions useful for understanding how AI can improve data workflows, automation, and decision-making. The sessions were well planned and easy to understand.
- Akshat KabraData Engineer
The AI training program by Technovids was insightful and professionally delivered. It covered important AI concepts in a simple and practical manner, with examples that connected well with data and analytics use cases. The sessions helped me build a stronger foundation in applying AI in real business scenarios.
- MangeshSoftware Engineer · Siemens
Technovids conducted the AI training in a very effective and engaging manner. The sessions were practical, easy to follow, and focused on how AI can be used in real software engineering and business workflows. It was a valuable program for understanding the current direction of AI adoption in the industry.
- ReshuSoftware Developer
Technovids AI training was a very useful learning experience. The course explained AI concepts in a practical way and showed how developers can use AI tools to improve productivity, problem-solving, and application development. The trainer's approach was simple, clear, and focused on industry use cases.
Common questions before planning this course
Answers about the course as published. Your written proposal records any changes we agree, how it is delivered and the price.
Are public cohorts available for Generative AI Fundamentals?
Public batches run live online when dates are published. An enquiry records your interest. Your place is confirmed only when Technovids sends the batch schedule and enrolment terms in writing.
What does a public cohort include?
The Technovids LMS gives you notes, assignments, lab guides and recordings for three months after the batch. Live sessions run on Zoom.
Is a certificate included?
Learners who meet the completion requirements get a Technovids certificate with a unique ID you can check on the Technovids website. It is not a vendor certification.
What equipment or accounts do participants need?
Bring a suitable laptop and the accounts named in the batch details. Tool subscriptions, usage charges and paid API use are not included unless we say so in writing.
How is this different from ChatGPT training?
This programme develops AI literacy, use-case judgement and responsible-adoption language. ChatGPT & Claude for Business Teams spends more time using assistants for specific workplace tasks and prompt workflows.
Is this suitable for senior leaders?
Yes when leaders need a shared foundation before strategy or implementation work. A private executive version can emphasise investment, governance and operating-model decisions.
Does the course teach coding?
No. It uses demonstrations and decision exercises. Technical architecture is explained only far enough to support better business and governance questions.
Plan your next step with this course
Tell us whether you are enquiring for yourself or a team, which development tasks matter and what access you have. We will confirm suitable delivery options and commercial details in writing.
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