Brolly Academy | Generative AI
Generative AI for Beginners
Your First Week With Chat Tools, a Simple Prompt Framework, 8 Examples, Safe Use, a First Project and a 30-Day Plan (2026)
A hands-on starter guide for 2026: what you need, how to use chat tools in your first week, a simple prompt framework with 8 before-and-after examples from Indian student and office life, your first images, safe-use rules, a no-code project, your first Python API call, free courses and a 30-day plan.
Generative AI for beginners: first steps with chat tools, a 5-part prompt framework, 8 examples, safe use, a first Python mini-project and a 30-day plan.
By Brolly Academy Team · Brolly Academy · Updated 24 October 2026
Table of Contents
1. Generative AI for beginners at a glance
3. Your first week with chat tools
4. Prompt-writing basics: a simple 5-part framework
5. 8 before-and-after prompt examples for Indian situations
6. Image generation: first steps
7. 10 beginner tasks to try this month
8. Safe-use rules for beginners
9. Your first no-code project: a job-description resume matcher
10. Your first Python mini-project: one API call
Quick answer
To learn generative AI as a beginner, start by using a free chat assistant every day for real tasks, learn to write clear prompts, then add one free course and one small project; you need no maths or coding to begin, and Python only when you want to build your own apps. Most beginners can get comfortable with the tools in a week and finish a first project in about a month. The fastest progress comes from doing, not reading: write prompts, compare answers, check facts and keep notes of what works.
- Week 1: sign up for one or two chat assistants and use them for study, email and planning
- Week 2: learn a prompt framework (role, task, context, format, limits) and practise before-and-after rewrites
- Week 3: try image generation and build a no-code project, such as a resume helper
- Week 4: take a free course, and if you code, make your first Python API call
- Always: keep personal and company data out of prompts, and check every fact
Key takeaways
- Using generative AI needs no maths or coding. Building with it needs Python, APIs and, later, ideas like embeddings and RAG.
- Better prompts beat better tools. A clear task, context, example and format fix most weak answers.
- AI tools can be confidently wrong. Treat every answer as a draft you must check.
- Free resources are enough to start: Microsoft's 21-lesson GitHub course, Hugging Face's free LLM course and Google's free tiers cover the basics.
- A small finished project teaches more than ten unfinished videos. Aim for one project in your first 30 days.
1. Generative AI for beginners at a glance
Short answer: Your path has five stages, from using chat tools to building a tiny app, and only the last two need code.
For the theory (how models are trained and how generative AI differs from older AI), read our explainer on what generative AI is. This guide is the hands-on part.
Timings assume about an hour a day; with 30 minutes, double them.
2. What you need to start
Short answer: A phone or laptop, an internet connection, an email account and curiosity; maths and coding are not needed to use generative AI, and coding only matters once you want to build your own tools.
Generative AI creates new text, images, audio, video or code from a request you type, called a prompt. Chat tools run on a large language model (LLM), trained on huge amounts of text to predict the next token (a word or part of a word). Big general models like these are called foundation models, because many apps are built on them.
Anyone who can type a clear question can start: students, freshers, developers, testers, analysts, marketers, teachers and people in non-IT jobs.
What you do not need
- No maths to use it. Maths becomes useful only if you later study how models are trained.
- No coding to use it. Chat apps work in plain English, and many also understand Hindi, Telugu and other Indian languages.
- No expensive laptop. The tools run in the cloud, so a basic laptop or even a smartphone is enough. A powerful GPU matters only if you later run open models on your own machine.
What coding adds
Coding lets you do things the chat window cannot: call a model from your own program through an API (a way for programs to talk to a service), process 500 reviews or PDFs in one go, build a chatbot that answers from your own documents (RAG, explained below), or connect the model to tools such as search or email, which turns it into an agent.
Pick your track now. You can switch later.
12 words every beginner should know
Courses also mention RLHF (reinforcement learning from human feedback, which teaches chat models to follow instructions using human ratings) and GANs (generative adversarial networks, an older image method where two networks compete). You do not need either to start.
3. Your first week with chat tools
Short answer: Sign up for one or two free chat assistants, then use them every day for real tasks from your own life, starting simple and adding detail.
Step 1: choose one or two tools
As of October 2026, the main general assistants are ChatGPT (OpenAI), Google Gemini, Claude (Anthropic), Microsoft Copilot, Meta AI and Perplexity (an answer engine that shows sources). Google's Gemini subscriptions page for India shows a "Free of charge" tier and a paid Google AI Pro plan at ₹399 a month (Google, October 2026). Plans and limits change often, so check each tool's own pricing page before you pay for anything. For a full comparison by category, see our guide to generative AI tools.
Start with one assistant you can open on phone and laptop, and add a second after a few days so you can compare answers. When two tools disagree, you know where to double-check.
Step 2: sign up safely
Use your personal email unless your employer has approved the tool, open the privacy settings (many tools let you turn off training on your chats), and never share passwords, Aadhaar, PAN, bank details or OTPs.
Step 3: your first 7 days of prompts
Keep talking: say "shorter" or "go deeper" when needed, and start a fresh chat for each new topic so old messages do not confuse the model.
4. Prompt-writing basics: a simple 5-part framework
Short answer: A good prompt tells the AI who to be, what to do, the background, the output format and the limits; you will rarely need all five, but adding two or three fixes most weak answers.
Prompt engineering simply means writing instructions that get useful output. You do not need tricks. Use this checklist, which we call R-T-C-F-L:
- Role: who the AI should act as. "You are a placement trainer for engineering freshers."
- Task: the exact job, with an action verb. "Rewrite", "summarise", "compare", "list", "explain".
- Context: the facts it cannot guess. Your degree, your audience, your deadline, the data.
- Format: how the answer should look. Table, 5 bullets, 100 words, email with subject line, code only.
- Limits: what to avoid or include. "Use simple English", "no jargon", "Indian rupees", "under 150 words".
Three habits that lift every prompt
- Show an example. If you want a certain style, paste one sample. Giving no examples is called zero-shot prompting; giving a few is few-shot prompting. Examples work well for tone, formats and labels, and they guide only that request; they do not retrain the model.
- Ask for steps. For maths, logic or planning, add "think step by step and show your working". It helps you spot mistakes.
- Iterate. Your first prompt is a draft. Read the answer, then add what was missing: "Good, now make it more formal and add a closing line."
A prompt template you can copy
Role: You are a [role].
Task: [action verb] [what].
Context: [who it is for, key facts, my situation].
Format: [table “” not found /]
.
Limits: [tone, language, what to avoid].
5. 8 before-and-after prompt examples for Indian situations
Short answer: Each weak prompt below gets a generic answer; each better version adds role, context, format or limits, and that is what makes the answer usable.
Try both versions of each example in your chat tool and compare. Replace the bracketed parts with your own details, and leave out anything private.
The better prompts name the audience, add facts the AI cannot guess and fix the shape of the answer. Example 8 also shows a safety habit: remove account numbers before pasting. For Excel and code answers, test on a few rows of your own data first; AI often gets edge cases wrong, such as exactly 75 marks in example 4.
6. Image generation: first steps
Short answer: Image tools turn a text description into a picture; describe the subject, style, setting, lighting and layout, then refine with follow-up prompts.
Most image generators use diffusion: the model starts with random noise and removes it step by step until a picture that matches your words appears. Popular options in October 2026 include the image features in ChatGPT (built on OpenAI's GPT-Image models), Ideogram, Adobe Firefly, Midjourney and the open Stable Diffusion family. Free limits differ and change often, so check each tool's current plan. Our tools guide compares them.
A simple image prompt formula
Subject + action + setting + style + lighting + format
- Weak: "A tea shop."
- Better: "A small roadside chai stall in Hyderabad at dawn, steam rising from glass cups, a vendor pouring tea, warm golden light, realistic photo style, wide 16:9 frame."
Five first exercises
- A college fest poster background with space at the top for the title
- Three thumbnail ideas for a YouTube video called "Python in 10 minutes"
- A logo idea for a home tuition centre, then 3 colour variations
- A flat diagram for your notes, such as "how a network sends data"
- A Diwali or Sankranti greeting with no text (add text yourself later, since image tools are weak at exact lettering)
Never upload other people's photos without consent or create images that pretend to be a real person, and check the tool's terms before using images in paid client work.
7. 10 beginner tasks to try this month
Short answer: Practise on real tasks you already do; these ten cover writing, learning, data, planning and creativity.
- Summarise a long chapter into one page of notes, then ask for 10 quiz questions.
- Tailor your resume summary to a job description (remove contact details first).
- Draft three versions of a tough email, such as a fee extension request, and pick the best.
- Build a monthly budget table for a fresher on a stipend in Hyderabad.
- Learn one Excel function a day (XLOOKUP, IF, COUNTIFS) on your own small dataset.
- Turn meeting notes into action items with owner and due date columns.
- Plan a 2-day trip on a budget, then verify train times and prices on official sites.
- Paste an error message from your code and ask why the fix works.
- Make a 5-slide seminar outline with a speaker note for each slide.
- Practise an interview or spoken English by asking the AI to role-play and correct you.
After each task, write one line in a notes file: what prompt worked, and what did not. In 30 days you will have your own prompt library.
8. Safe-use rules for beginners
Short answer: Keep personal and company data out of prompts, check every fact, label AI help honestly and follow your college or employer rules.
The biggest limit is hallucination: a confident answer that is false, including made-up references, laws, statistics or links. It happens because the model predicts likely words; it does not look facts up unless the tool adds search or your documents.
The safe-use checklist
- No personal data. Never paste Aadhaar, PAN, bank or card numbers, passwords, OTPs, medical reports or someone else's phone number.
- No company secrets. Do not paste client code, customer lists, contracts or internal documents into a personal account. Use only tools your employer has approved, under its rules.
- Know what free tiers do with your data. For example, Google's Gemini API pricing page says content sent on the free tier is used to improve Google's products (Google, October 2026). Read the terms of any tool before you send it anything sensitive.
- Check facts. Verify numbers, dates, names, laws and medical or money advice on an official source. Ask the tool for sources, then open them yourself.
- Test code and formulas on sample data before real use.
- Be honest about AI help. Follow college rules; at work, you own what you send.
- Reject bias and deepfakes. Discard answers that stereotype people, and never fake images, audio or video of real people.
9. Your first no-code project: a job-description resume matcher
Short answer: Use a chat assistant to compare your resume with a real job description, find gaps and rewrite your points; it takes about two hours and gives you something useful to show.
This project uses only a chat tool and a document. It teaches you multi-step prompting, which is the same thinking you will later use in code.
Steps
- Collect inputs. Copy one real job description for a role you want, such as "Junior Data Analyst" or "Associate Software Engineer". Copy your resume text and delete your name, phone, email and address.
- Extract requirements. Prompt: "List the skills, tools and experience this job description asks for, in a table with columns Requirement, Must-have or Nice-to-have."
- Match. Prompt: "Compare my resume with that table. Add a column showing Strong, Partial or Missing for each row, with a one-line reason."
- Rewrite. Prompt: "Rewrite my 4 weakest resume points to match the job, using only facts that are already in my resume. Do not invent skills or numbers."
- Plan the gaps. Prompt: "For the Missing items, suggest a 2-week learning plan with free resources and one small proof-of-skill task for each."
- Review by hand. Check every rewritten line is true. Remove anything the AI made up.
- Save your prompts. The five prompts are now a reusable workflow. Run it for three job descriptions and note which skills appear every time. If you learn Python later, this flow becomes your first coded app.
10. Your first Python mini-project: one API call
Short answer: Install Google's Python library, create a free API key in Google AI Studio, and send your first prompt from code in under 15 lines.
If you know basic Python (variables, functions, print), you can call a model from your own program. We use Google's Gemini API here because its Python quickstart is short, and the Gemini API pricing page lists a Free tier, with gemini-3.8-flash shown as "Free of charge" on that tier (Google, October 2026). The same idea works with OpenAI, Anthropic and other providers; only the library and model name change.
Setup
- Install Python 3 from the official site and open a terminal.
- Install the library: run pip install -U google-genai (as given in Google's quickstart, October 2026).
- Get an API key. Google's quickstart says Google AI Studio creates a project and API key for new users, which you copy from the API keys page.
- Save the key as an environment variable named GEMINI_API_KEY. Never paste the key into your code or upload it to GitHub.
The code
Simplified example (based on Google's Gemini API quickstart, October 2026):
from google import genai
# The client reads your key from the GEMINI_API_KEY environment variable
client = genai.Client()
prompt = "Explain what an API is to a BCA student in 3 short bullet points."
interaction = client.interactions.create(
model="gemini-3.8-flash",
input=prompt,
)
print(interaction.output_text)
The client connects using your key, interactions.create sends your prompt to the named model, and output_text holds the reply.
Turn it into a mini-project
Add one upgrade at a time: use input() so the user types the question; wrap it in a loop until you type "exit"; read a notes file and ask for 10 quiz questions; or label a list of 20 customer reviews as positive, negative or neutral. Test only with sample data, since free-tier content may be used to improve Google's products. For more ideas, see our list of generative AI projects for beginners.
11. Free generative AI courses and resources
Short answer: Microsoft's Generative AI for Beginners course on GitHub, Hugging Face's free LLM course, Google Cloud's free Generative AI Leader learning path, AWS's foundational AI Practitioner path and DeepLearning.AI's short courses cover everything a beginner needs.
Microsoft: Generative AI for Beginners
This is the course behind the "microsoft generative ai for beginners" searches. The GitHub repository has a setup lesson plus 21 lessons, each marked "Learn" (concepts) or "Build" (concepts plus code), with code in Python and TypeScript where possible and translations into more than 50 languages (GitHub, October 2026). Topics run from prompt engineering and responsible AI to chat apps, vector search, image generation, RAG, agents and fine-tuning. The Build lessons need a model service, such as Azure OpenAI Service, Microsoft Foundry Models, the OpenAI API or Foundry Local, which may cost money depending on your choice.
Hugging Face: LLM Course
The Hugging Face LLM Course says it is completely free and without ads, but it requires a good knowledge of Python and is better taken after an introductory deep learning course (Hugging Face, October 2026). Take it in month two or three, not week one.
Google Cloud: Generative AI Leader learning path
Google Cloud offers a no-cost learning path of five short courses, about 7 to 8 hours in total, that prepares you for its Generative AI Leader certification, which is aimed at people in any job role, with or without technical experience (Google Cloud, October 2026). The exam itself is paid; our Generative AI Leader certification guide covers it.
AWS: AI Practitioner
AWS Certified AI Practitioner is a foundational exam of 65 questions in 90 minutes, priced at $100, and it does not require you to build AI or ML solutions (AWS, October 2026). Compare options in our generative AI certification guide.
DeepLearning.AI
DeepLearning.AI publishes short courses on prompting, agents and building with LLMs, plus "Generative AI for Everyone" for non-technical learners. Check each course page for the current price.
Learning in Telugu or Hindi
Ask any chat assistant to explain a concept in Telugu or Hindi, then keep the technical terms in English, since job descriptions use them. Microsoft's GitHub course also lists translations, so check whether your language is included.
12. Your 30-day beginner plan
Short answer: Spend about an hour a day: week one on chat tools, week two on prompting, week three on images and a no-code project, and week four on a free course plus your first API call.
Non-coders can replace days 26 to 29 with a second no-code project, such as a study-notes assistant or a social media content calendar for a local business.
13. Common beginner mistakes
Short answer: Most beginners trust answers too much, write vague prompts, collect courses without practising, and paste private data; each has a simple fix.
14. What to learn next
Short answer: Non-coders should go deeper into prompting and AI tools for their job; coders should move from API calls to embeddings, vector databases, RAG and agents, following a structured syllabus.
If you do not code: go deeper into prompting for your field (campaigns, HR policies, reports, teaching material), use the AI features inside tools you already use, and consider a foundational certificate such as Google Cloud's Generative AI Leader or AWS AI Practitioner.
If you code: follow this order, where each step builds on the one before:
- Python basics: functions, lists, dictionaries, files
- APIs: calling models, handling errors, controlling output format
- Prompting in code: system instructions, few-shot examples, structured output
- Embeddings and vector databases: searching text by meaning
- RAG: answering from your own PDFs with frameworks such as LangChain or LlamaIndex
- Fine-tuning basics: when to fine-tune, and methods like LoRA
- Agents: letting the model call tools for multi-step tasks
- Deployment and evaluation: putting an app online and checking its answers
Why this order? In 12 Indian generative AI job descriptions we read on 24 October 2026, Python appeared in all 12 and RAG in 11, with LangChain, vector databases, prompt engineering and evaluation close behind. Our generative AI syllabus lays these topics out module by module. For the full phase-by-phase path with timelines, read our generative AI roadmap. If you want to turn this into a career, our guide on how to become a generative AI engineer explains the role, skills and salaries.
15. How Brolly Academy helps
Short answer: Brolly Academy's 3-month Generative AI course in Hyderabad takes you from basics to LLMs, RAG, LangChain and agents, with classroom, live online and self-paced options, and coding is not mandatory to join.
If you want a guided path after this starter guide, the Generative AI Training in Hyderabad program at Brolly Academy covers it in 3 months (Brolly Academy course page, October 2026):
- Month 1: Python, machine learning and deep learning basics
- Month 2: generative AI, LLMs, NLP and computer vision, including GANs, VAEs and diffusion models
- Month 3: AI agents, prompt engineering and MLOps, with deployment on AWS, GCP and Azure
- Final weeks: capstone project, certificate and placement preparation
The 16 modules run from prompt engineering and embeddings to chunking, RAG with LangChain, vector databases (Chroma DB, Weaviate, FAISS, Pinecone), fine-tuning with LoRA and QLoRA, evaluation, LLMOps and LLMs on Amazon Bedrock and Azure OpenAI. Module 16 covers everyday tools such as ChatGPT, Gemini, Google NotebookLM, Copilot, Ideogram and Stable Diffusion.
The lead trainer is Mr. Dinesh, a Generative AI Specialist and Cloud Architect with 12+ years in AI, ML and generative AI, with co-trainers Ms. Madhumitha and Tabassum. Coding is not mandatory, no CS degree is required, a free 3-day demo is offered and EMI is available on the Classroom and Online plans. You get a Brolly Academy completion certificate after the capstone; no vendor certification is included.
Start generative AI with a guided plan
Tried the prompts in this guide and want structured practice? Join Brolly Academy's free 3-day demo of the Generative AI course in Hyderabad, available in classroom and live online batches, and see how the program moves from prompting to LLMs, RAG and agents over 3 months.
Frequently asked questions
How do I start learning generative AI as a beginner?
Start by using a free chat assistant every day for real tasks such as study notes, emails and planning. In the second week, learn a simple prompt framework covering role, task, context, format and limits. Then try image tools, build one no-code project and take one free course. If you can code, finish your first month with a small Python API call. Doing small tasks daily teaches faster than watching long videos.
Can I learn generative AI without coding?
Yes. Using generative AI needs no coding at all, because chat assistants and image tools work in plain English and many Indian languages. You can write prompts, create content, analyse documents and build no-code workflows such as a resume matcher. Coding becomes useful only when you want to build your own apps, process many files at once, answer from your own documents with RAG or connect a model to other tools as an agent.
Is Python required for generative AI?
Python is not required to use generative AI tools, but it is the main language for building with them. If you want to call models through APIs, build chatbots, work with embeddings and vector databases or apply for engineering roles, learn Python. In 12 Indian generative AI job descriptions we read in October 2026, Python appeared in every one. Basic Python, such as variables, functions, lists and files, is enough for your first API project.
What maths do I need for generative AI?
You need no maths to use generative AI tools or to write good prompts. Maths starts to matter only if you study how models are trained or want research and model-building roles. Most beginners and many application builders work with APIs, prompts and RAG rather than equations. Learn the tools and Python first, and add maths later only if your path moves towards training models.
How long does it take to learn generative AI basics?
With about an hour a day, most beginners get comfortable with chat tools in one week and can finish a first project in around 30 days. Working professionals with less time may need twice as long. Moving from basics to building RAG apps and agents takes longer, usually a few months of steady practice. The 30-day plan in this guide covers the starting stage, and a structured syllabus or roadmap covers what comes after.
Is Microsoft's Generative AI for Beginners course free?
The course material is openly available on GitHub, with a setup lesson plus 21 lessons marked Learn or Build, and code in Python and TypeScript where possible. However, the Build lessons need access to a model service such as Azure OpenAI Service, Microsoft Foundry Models, the OpenAI API or Foundry Local, and using those services may cost money depending on which one you choose. Reading the lessons themselves costs nothing.
What is the best free generative AI course for beginners?
It depends on your track. Developers and students who code should start with Microsoft's Generative AI for Beginners on GitHub. Non-technical learners can take Google Cloud's no-cost Generative AI Leader learning path of five courses, about 7 to 8 hours. Once you know Python well, Hugging Face's LLM Course is free and goes deeper into open models. Pick one, finish it and build a project before starting another.
Which generative AI tools should a beginner try first?
Start with one general chat assistant you can use on phone and laptop, such as Google Gemini, ChatGPT, Claude, Microsoft Copilot or Meta AI. Google's India subscriptions page showed a free tier for Gemini in October 2026. Add a second assistant after a few days so you can compare answers. Then try one image tool, such as Ideogram, Adobe Firefly or ChatGPT's image features, and check current plans before paying.
What is prompt engineering, and should beginners learn it first?
Prompt engineering means writing clear instructions that get useful answers from an AI model. Yes, beginners should learn it first, because it improves every tool you use and needs no coding. A simple method is to give the AI a role, a clear task, the context it cannot guess, the format you want and any limits. Adding an example and asking follow-up questions fixes most weak answers.
What is the difference between zero-shot and few-shot prompting?
Zero-shot prompting means asking the AI to do a task without giving any examples. Few-shot prompting means including a few sample inputs and outputs in your prompt so the model copies the pattern, style or labels you want. Few-shot works well for tone, formats and classification tasks. The examples only guide that one request; they do not permanently change or retrain the model.
What are hallucinations in generative AI?
A hallucination is a confident, well-written answer that is false or made up, such as fake references, wrong dates, invented statistics or broken links. It happens because language models predict likely words rather than look facts up, unless the tool adds search or your documents. Always check numbers, names, laws and medical or money advice on official sources, and test any code or formula before using it.
Is it safe to share personal data with AI chat tools?
No, you should not share personal data. Never paste Aadhaar, PAN, bank or card numbers, passwords, OTPs, medical reports or other people's details into a chat. Some free tiers use your content to improve products; Google's Gemini API pricing page says this for its free tier. Read each tool's privacy settings, turn off training on your chats where possible and use only employer-approved tools for office work.
Can a non-IT person learn generative AI?
Yes. Marketers, HR staff, teachers, B.Com graduates, business owners and other non-IT learners can use generative AI well without any coding. Focus on prompting, AI tools for your own work, image generation and no-code projects. A foundational certificate such as Google Cloud's Generative AI Leader, which is aimed at people in any job role, or AWS Certified AI Practitioner can add a credential to your learning.
What laptop do I need to learn generative AI?
A basic laptop or even a smartphone is enough to start, because chat assistants and most image tools run in the cloud through a browser or app. For Python and API projects, any laptop that runs Python and a code editor comfortably will do. You need a powerful GPU only if you later run open models locally on your own machine, which is not a beginner task.
What should I learn first, machine learning or generative AI?
Beginners can start with generative AI tools and prompting first, because they give quick, practical results without theory. If you plan to become an engineer, add Python next, then learn API calls, embeddings, RAG and agents. Machine learning and deep learning foundations become important once you study fine-tuning or model behaviour in depth, so add them before that stage rather than on day one.
Is generative AI hard to learn?
Using generative AI is easy; most people write useful prompts within a few days. Building with it is moderately hard and needs Python, APIs and concepts like embeddings, vector databases and RAG, learned step by step. The difficulty grows gradually, so you can stop at the level that suits your job. The common trap is jumping to agents and advanced frameworks before you are comfortable with prompting and basic Python.
What are good first generative AI projects for beginners?
For non-coders, a resume matcher that compares your resume with a job description, a study-notes assistant or a social media content calendar are good first projects. For coders, start with a single Python API call, then add user input, a question loop, quiz generation from your notes, or sentiment labelling for a list of reviews. Keep projects small, finish them and save them on GitHub without your API keys.
How do I make my first API call to a generative AI model?
Install Python, run the command to install Google's google-genai library, create an API key in Google AI Studio and save it as the GEMINI_API_KEY environment variable. Then create a client, send your prompt with the interactions create method and a model such as gemini-3.8-flash, and print the output text. The Gemini API pricing page listed a free tier in October 2026, which suits practice with sample data.
Can I learn generative AI in Telugu or Hindi?
Yes. You can ask most chat assistants to explain any concept in Telugu or Hindi, which makes the early stage much easier. Microsoft's Generative AI for Beginners course on GitHub also lists translations into more than 50 languages, so check whether yours is included. Keep the technical terms such as prompt, token, RAG and embedding in English, because job descriptions and documentation use them.
What are the basic concepts every beginner should know?
Start with prompt, large language model, token, context window, multimodal and hallucination, because they explain how chat tools behave. Next learn embedding, vector database and RAG, which explain how AI answers from your own documents. Then fine-tuning, LoRA, diffusion and agents, which explain custom models, image generation and AI that takes actions. You do not need deep theory for any of these at the start.
What are the most common mistakes beginners make with generative AI?
The most common mistakes are trusting every answer, writing vague one-line prompts, giving up after the first reply, pasting private or office data, collecting courses without practising and chasing every new tool. Coders also hard-code API keys and jump to RAG or agents too early. Fix these by checking facts, using a prompt framework, iterating, removing personal data and finishing one project before starting the next.
What should I learn after the basics of generative AI?
Non-coders should deepen prompting for their field and consider a foundational certificate. Coders should follow a clear order: Python, API calls, prompting in code, embeddings and vector databases, RAG with frameworks such as LangChain or LlamaIndex, fine-tuning basics with LoRA, agents, and finally deployment and evaluation. A structured syllabus or roadmap helps you cover these without gaps, one project at a time.
Can I get a job after a generative AI beginner course?
A beginner course alone rarely leads straight to an engineering job. In the Indian job descriptions we read in October 2026, employers asked for Python, RAG, LangChain, vector databases and evaluation skills, usually shown through projects. Use the beginner stage to build habits, then learn those skills and publish two or three working projects. For non-IT roles, strong AI tool skills can already improve your current work and your resume.
Does Brolly Academy's Generative AI course need coding experience?
No. Brolly Academy's course page says coding is not mandatory and no CS degree is required, so beginners from any stream can join. The 3-month course starts with Python, machine learning and deep learning in the first month before moving to LLMs, RAG, LangChain and agents. It runs in classroom and live online batches, with a self-paced video plan, and offers a free 3-day demo.
What are Brolly Academy's Generative AI course fees?
Brolly Academy's course page listed three plans in October 2026: Video Recording at ₹14,999, Online Course at ₹35,000 and Classroom Training at ₹40,000, with EMI available on the Classroom and Online plans. All plans follow the 3-month syllabus of 16 modules. You receive a Brolly Academy course completion certificate after the course and capstone project; no vendor certification is included in the fee.
Learn with Brolly Academy
Review the syllabus, practical projects and batch timings, and book a free demo before you enrol.
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View CourseHugging Face Course
Work hands-on with open models, transformers and datasets on Hugging Face, the core toolkit behind many GenAI projects.
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