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 a 12-hour, instructor-led foundation 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.
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 representative, non-sensitive examples. Outputs are produced during the workshop for practice and review; anything participants keep is their own working material.
Useful AI requires ownership, evidence and boundaries
Participants learn to treat risk and review as part of the workflow, not as a disclaimer added after a pilot is already live.
NIST AI RMF is used as a voluntary risk-management reference, not as legal advice or a substitute for the organisation’s legal, privacy, security and compliance review.
Define the use and owner
State the intended task, affected users, accountable owner and decisions the system may or may not influence.
Protect information
Use only approved data and accounts. Apply the organisation’s confidentiality, retention and access rules before experimentation.
Measure in context
Choose quality, risk and adoption measures that reflect the real task; document what is not or cannot yet be measured.
Review and improve
Monitor failures, user feedback and changed conditions, then update controls or stop the use case when necessary.
How the course is tailored and delivered
Private delivery is shaped around the organisation’s maturity, approved tools, decision rights, workforce roles and current initiatives.
From first conversation to evaluation
Clarify the decision
Agree what participants must decide, design or implement after the programme.
Map the current state
Review roles, tools, policies, data constraints and existing AI or automation activity.
Select the cases
Choose realistic examples that are useful without exposing restricted information.
Define the handover
Confirm the artefact, owner, review path and next checkpoint expected after training.
Delivery formats
Public live-online cohort
Best for: Individuals following a published schedule with a mixed professional cohort
Live Zoom sessions with notes, assignments, lab instructions and three-month recording access through the Technovids LMS.
Private corporate programme
Best for: Teams that want organisation-specific cases and a shared working output
Delivered live online or onsite. Scope, cohort size, materials, support and commercials are confirmed in writing.
Delivery modes
Live online
Facilitated sessions with breakout work, shared artefacts and live review.
Onsite
Private corporate delivery at the client location, with the same structured exercises and review.
Discussion, practice and decisions—not a lecture marathon
Technovids training sessions in different group settings. The photographs do not attribute a particular client, course or outcome.
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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Photo 9 of 10: Classroom 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
Technovids assigns a trainer whose subject depth and facilitation experience fit the audience and 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 a specific delivery is confirmed with the schedule or written proposal.
Updated
Organisations we have trained, and participant perspectives
Permissioned feedback and published client relationships provide context on delivery. They do not guarantee a specific adoption or business result.
Organisations we have delivered training for
Practical training programmes delivered for learners, professionals and teams across AI, data, cloud, productivity and professional skills.
Participant experiences across workplace roles
What professionals found useful, practical and relevant to 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 published course. The written proposal records any agreed customisation, delivery details and commercials.
Are public cohorts available for Generative AI Fundamentals?
Public cohorts run live online when dates are published. An enquiry records your interest; a place is confirmed only when Technovids provides the cohort schedule and enrolment terms in writing.
What does a public cohort include?
The Technovids LMS provides notes, assignments, lab instructions and recordings for three months after the cohort. Live sessions are hosted through Zoom.
Is a certificate included?
Learners who meet the stated completion requirements receive a Technovids completion certificate with a unique ID that can be verified on the Technovids website. It is not a vendor certification.
What equipment or accounts do participants need?
Participants provide a suitable laptop and the accounts named in the cohort information. Tool subscriptions, consumption charges or paid API use are not included unless stated 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.