LLMs (Large Language Models)
LLMs (Large Language Models) is a free 4-week beginner course explaining how large language models are trained, what tokens and context windows are, and which prompting techniques actually change output quality. Learners cover temperature, function calling, streaming, and choosing between prompting, RAG, and fine-tuning, plus common LLM limitations and failure modes.
Understand how large language models work, how they're trained, and how to use them effectively.
What you'll learn
Course details
Duration
4 weeks
Best for
Anyone who wants to understand what's actually happening behind the chat window
This course is free
Course Curriculum
A module-by-module breakdown of what you'll cover in LLMs (Large Language Models).
01Basic
7 topicsWeek 1
- What is a large language model?
- Tokens, context windows, and why they matter
- Tokenization in depth: how byte-pair encoding works
- Base models vs instruction-tuned (chat) models
- Embeddings: turning text into numbers
- Prompting basics: system, user, and assistant roles
- Project: Build a role-based prompt playground
02Intermediate
7 topicsWeek 2
- Temperature, sampling, and controlling output variability
- Prompting techniques: few-shot examples and chain-of-thought
- Structured output: JSON mode and schema-constrained generation
- Function calling and tool use
- How the context window actually works: position and attention
- Fine-tuning with LoRA and PEFT
- Project: Build a tool-using assistant with controlled output
03Advanced
6 topicsWeek 3
- Turning a business request into a testable contract
- Validating generated structures and authorizing tool execution
- How instruction-tuned models are trained: SFT and RLHF
- Why long contexts cost more: attention and the KV cache
- Quantization: making models smaller and faster
- Evaluation, ownership, and release management for LLM apps
04Pro
7 topicsWeek 3
- Choosing between prompting, RAG, and fine-tuning
- Streaming responses and managing perceived latency
- Decoding strategies in depth: greedy, beam search, and nucleus sampling
- Long-context strategies and prompt injection basics
- Model routing: balancing cost, speed, and quality
- Evaluating LLMs beyond public benchmarks
- Project: Build a cost-aware, streaming LLM assistant
05AI Pro
3 topicsWeek 4
- Using Claude, ChatGPT, and Perplexity to design and debug LLM applications
- Verifying AI-suggested prompts and architecture decisions
- Project: Use AI to redesign a prompt, then verify with an evaluation set
Tools & Technologies
Hands-on practice with the same tools used by working professionals.
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Communication & Soft Skills
Built into every course, alongside the technical curriculum.
Communication Skills
Practice explaining technical work clearly to teammates, managers, and clients.
Practical AI Skills
Hands-on training in using AI tools well — prompting, everyday workflows, and where AI actually helps versus where it doesn't.
Teamwork & Collaboration
Work in small project teams using real workflows like stand-ups and code reviews.
Problem Solving
Build the habit of breaking down ambiguous problems into clear, solvable steps.
Time Management
Learn to plan, prioritize, and deliver project work against realistic deadlines.
Presentation Skills
Present your project work and results with confidence, as you would to a client.
Resume & Profile Building
Build a strong resume, LinkedIn profile, and project portfolio that recruiters notice.
Interview Preparation
Mock interviews and portfolio reviews to get you ready for real job applications.
Career & Placement Support
100% of students enrolled in LLMs (Large Language Models) get full access to placement support — real tools and guidance, not just a promise. This is support and access, not a guaranteed job outcome.
Resume Builder
A resume auto-built from your real completed projects and certificates, ready to export.
Shareable Portfolio
A public portfolio page showcasing your approved projects, with a link you can share with employers.
Interview Preparation
Guidance on presenting your projects and experience clearly in real interviews.
Peer & Mentor Community
Ongoing access to our learner community for questions, feedback, and support after you finish.
What Your Certificates Look Like
Real, verifiable certificates with your name, LLMs (Large Language Models), and a QR code anyone can scan to confirm they're genuine — one for completing the course, and a separate one if you take the internship track. Earning a certificate is free. To view, download and share it, there's a one-time ₹100 release fee per certificate — the same for free-course, paid-course and internship certificates.
Course Completion Certificate
Data24ZoneCertificate of Completion · Free Course
Illustrative recipient
Your Name Here
Example certificate for completion of
LLMs (Large Language Models)
- Certificate No.
- D24-SAMPLE
- Mode
- Self-Paced
- Duration
- 4 weeks
- Example issue date
- On completion
Sample only — not a real, issued certificate.
Verification is available only on issued certificates.
Internship Completion Certificate
Data24ZoneCertificate of Internship
Illustrative recipient
Your Name Here
Example certificate for completion of
LLMs (Large Language Models)
- Certificate No.
- D24-SAMPLE
- Mode
- Internship
- Duration
- 4 weeks
- Example issue date
- On completion
Sample only — not a real, issued certificate.
Verification is available only on issued certificates.
Frequently Asked Questions
What will I learn in the LLMs (Large Language Models) course?
You'll learn: How LLMs are trained and what they can/can't do; Tokens, context windows, and why they matter; Prompting techniques that actually change output quality; Common LLM limitations and failure modes.
What tools and technologies does the LLMs (Large Language Models) course cover?
This course covers: ChatGPT, Claude, Perplexity.
Is the LLMs (Large Language Models) course beginner-friendly?
Yes — this is a beginner-level course.
How long does the LLMs (Large Language Models) course take to complete?
The course is designed to be completed in approximately 4 weeks, depending on your pace.
What enrollment options are available for LLMs (Large Language Models)?
Project Training Track (₹1,499) — One defined learning project and 2 review sessions; no employment or stipend included. Self-Paced (₹1,999) — Learn anytime with the full recorded curriculum and community access.
Is there an internship option for LLMs (Large Language Models)?
Yes — the Internship track means working through real projects with our team and earning a separate Internship Certificate.
Will I get a certificate after completing LLMs (Large Language Models)?
Yes. Self-paced enrollment earns a course completion certificate once your final project is reviewed and approved. Internship enrollment earns an internship certificate once your internship projects are approved. Every certificate is publicly verifiable on our website and can be added directly to your LinkedIn profile. Earning it is free; releasing it to view, download and share is a one-time ₹100 fee per certificate.
Is placement support included with LLMs (Large Language Models)?
Yes — every enrolled student gets full access to our placement support: resume building, a shareable project portfolio, mock interview preparation, and guidance applying what you've built to real job applications. This is support and access, not a guaranteed job outcome — how quickly it leads to an offer depends on the market and your own effort.
