01
Transformer Fundamentals
Understand how modern LLMs are built — attention, embeddings, positional encodings, and the architecture behind BERT, GPT, LLaMA, Mistral, and DeepSeek.
Large Language Model Course (LLM) — Fine-Tuning, RAG & RLHF
A Large Language Model Course teaches how modern AI models such as GPT-4/4o, Llama 3/4, Mistral, Qwen, and DeepSeek are built, fine-tuned, evaluated, and deployed. You’ll study transformer architecture, LoRA/QLoRA fine-tuning, RLHF alignment, retrieval-augmented generation (RAG), and production deployment patterns used in real LLM systems.
Built for engineers and product builders who want to go beyond simple API calls and actually shape model behavior. You’ll work with modern open-source LLM families such as Llama, Mistral,Qwen, and DeepSeek, build RAG pipelines with evaluation and retrieval strategies, align outputs using DPO/RLHF techniques, and deploy high-performance inference services with monitoring and observability.
8-Week ProgramLive + RecordedDual Certificate₹40,000 (One-time)
📚 Part of Complete AI Specialization:
GenAI Foundations → LLM Course (Current) → LLMOps → Agentic AI
Book a Session
Inquire about the Large Language Model Course
What is a Large Language Model Course?
A Large Language Model Course (LLM course) is an engineering-focused program that teaches how transformer language models work and how to adapt them for real products using fine tuning large language models, retrieval-augmented generation (RAG), alignment, evaluation, and deployment. If you're comparing a large language models course across providers, prioritize hands-on labs, measurable evaluation, and production deployment patterns.
- Build and debug LLM systems beyond prompt-only workflows
- Fine-tune open source LLM models with LoRA/QLoRA
- Ship RAG pipelines with grounded answers and evaluation
What Skills Do You Learn in an LLM Course?
- Transformer internals, attention variants, and context-window trade-offs
- LLM engineering workflows: data curation, training runs, and reproducibility
- RAG course skills: chunking, embeddings, retrieval, re-ranking, and grounding
- RLHF training concepts (DPO/RLHF) and preference-based alignment
- LLM evaluation: test sets, regression harnesses, and hallucination checks
- LLM deployment: vLLM/TGI serving, observability, and cost-per-token control
Examples of Large Language Models
Clear entity examples help teams reason about capabilities, constraints, and deployment options.
- GPT-4 / GPT-4o
- LLaMA 3
- Mistral
- Qwen
- DeepSeek
How Large Language Models Are Trained
- Tokenization: convert text to token IDs and build vocabularies
- Transformers: learn next-token prediction with attention
- Fine-tuning: SFT + PEFT (LoRA/QLoRA) for domain behavior
- Alignment: RLHF/DPO-style preference optimization
- Evaluation: quality, safety, robustness, and cost/latency constraints
Skills You Will Gain in This Large Language Model Course
From Transformer internals to LoRA/QLoRA fine-tuning, RLHF alignment, RAG systems, and production deployment — every skill is practiced hands-on.
Foundation (Transformers & NLP)
Transformer architecture & attention mechanismsMulti-head attention, GQA, MQA, MLA variantsTokenization, embeddings & positional encodingsBERT vs GPT vs LLaMA model families
Fine-Tuning & Alignment
LoRA / QLoRA parameter-efficient fine-tuningSFT with clean dataset pipelinesRLHF & DPO preference alignmentHyperparameter sweeps & model card reporting
Evaluation & Safety
BLEU / ROUGE / Perplexity evaluationHallucination detection & reductionGuardrails & safety filtersGolden-set regression harnesses
Retrieval & Memory (RAG)
LangChain / LlamaIndex retrieval pipelinesEmbedding models & vector stores (Qdrant, FAISS)Re-ranking, citation grounding & source attributionHybrid search and semantic chunking strategies
Deployment & Observability
vLLM / TGI model servingLangSmith tracing & cost-per-token dashboardsGuardrails, rate limiting & tenant isolationCI/CD for model releases with eval gates
Scaling & Architecture
Mixture-of-Experts (MoE) routing & trade-offsDistributed training on multi-GPU setupsQuantization (GPTQ, AWQ) for cost/latency controlContext window optimization & long-context patterns
All skills are assessed through a capstone project: fine-tune, evaluate, deploy, and observe a real open-source LLM end-to-end.
Program Highlights
What makes this LLM course different from online tutorials and theoretical lectures.
Hands-On Fine-Tuning (LoRA/QLoRA)
Complete end-to-end labs on LLaMA/Mistral/DeepSeek with parameter-efficient training, dataset curation, and reproducible reports.
RLHF / DPO & Evaluation
Alignment methods, preference data pipelines, and quality measurement (BLEU, ROUGE, Perplexity) to reduce hallucinations and improve safety.
Capstone: Build Your Own LLM App
Fine-tune, evaluate, and deploy an open-source LLM on domain data. Publish a model card and demo — perfect for interviews and portfolios.
Deployment & Career Support
Serve with vLLM/TGI, add LangSmith tracing and cost controls. Get dual certification, resume review, and mock interview preparation.
Program Overview of Large Language Model Course
Six core pillars — Transformer theory through production deployment — each taught with hands-on labs and real open-source models.
02
Efficient Fine-Tuning
Perform parameter-efficient fine-tuning (LoRA, QLoRA, SFT) on domain data. Run hyperparameter sweeps, curate clean datasets, and produce reproducible model cards.
03
Alignment & Evaluation
Align models with RLHF and DPO, build preference datasets, evaluate using BLEU/ROUGE/Perplexity, and implement hallucination reduction strategies.
04
Retrieval & Memory (RAG)
Design retrieval-augmented pipelines with LangChain and LlamaIndex. Choose embeddings, vector stores, and implement re-ranking with citation-grounded answers.
05
Production Deployment
Serve models with vLLM and TGI, add LangSmith tracing, enforce guardrails, and manage cost-per-token and throughput SLAs for reliable AI features.
06
Scaling & MoE
Learn Mixture-of-Experts routing to scale capacity without linear cost. Reason about trade-offs in memory, throughput, latency, and quality.
6
Core pillars
8-Week
Program
4
Open-source models
100%
Hands-on labs
Who Is This Large Language Model Course For?
For engineers and builders aiming at LLM roles — from fine-tuning and alignment to RAG systems, deployment, and evaluation.
ML / LLM Engineering Track
ML & LLM Engineers
End-to-end LLM engineering with production deployment
- Fine-tune with LoRA/QLoRA, RLHF/DPO, and measure quality metrics
- Build RAG pipelines with LangChain, vector stores, and re-ranking
- Serve with vLLM/TGI and trace with LangSmith
Software Development Track
Software Developers
Go beyond API calls and ship AI-powered features
- Fine-tune BERT/GPT on domain data with clean pipelines
- Integrate RAG with LangChain into existing services
- Add observability and guardrails for reliable LLM features
Research & Architecture Track
Researchers & Architects
Design, experiment, and publish model findings
- Design controlled experiments and publish reproducible model cards
- Study MoE trade-offs, safety, and hallucination reduction strategies
- Evaluate alignment across RLHF/DPO methods
AI Product Track
Founders & Product Builders
Prototype domain copilots and scale to production
- Build domain chatbots and copilots fast with PEFT
- Cut cost with quantization (GPTQ, AWQ) before scaling
- Scale serving with vLLM/TGI and autoscaling policies
Data Engineering Track
Data Engineers
Pipelines for fine-tuning, embeddings, and retrieval
- Build fine-tuning dataset pipelines with deduplication and quality filters
- Manage embedding generation, vector DBs, and retrieval scoring
- Set up experiment tracking with W&B and MLflow at scale
Career Transition Track
Students & Career Switchers
From foundations to portfolio-grade LLM projects
- Foundations → tokenization/attention → PEFT → RLHF/DPO → RAG → deploy
- Build portfolio projects with model cards and demo apps
- Earn a dual LLM engineering certificate backed by capstone assessment
Learning Outcomes for Large Language Model Course
Graduate with LLM engineering skills across Transformer theory, LoRA/QLoRA fine-tuning, RLHF/DPO alignment, RAG systems, and production deployment.
01
Master Transformer Fundamentals
Understand attention, multi-head attention, embeddings, and positional encodings. Read and reason about modern LLM internals — BERT, GPT, LLaMA, Mistral.
TransformersAttentionEmbeddingsBERT vs GPT
02
Fine-Tune Efficiently with LoRA / QLoRA
Perform parameter-efficient fine-tuning on domain data, run hyperparameter sweeps, and produce reproducible reports and model cards.
LoRAQLoRAPEFTHyperparameter Tuning
03
Align Models with RLHF / DPO
Build preference datasets and align models with RLHF and DPO to improve helpfulness, safety, and task adherence. Evaluate and reduce hallucinations.
RLHFDPOSafetyHallucination Reduction
04
Scale with Mixture-of-Experts (MoE)
Learn MoE routing and expert activation to increase capacity without linear cost. Reason about trade-offs in throughput, latency, and quality.
MoERoutingLatencyThroughput
05
Build RAG & LLM Apps
Design retrieval pipelines, choose embeddings/vector stores, and implement evaluation loops for enterprise-grade RAG chatbots and copilots.
RAGLangChainLlamaIndexVector DB
06
Deploy & Observe Production LLMs
Serve models with vLLM/TGI, add tracing and observability via LangSmith, enforce guardrails, and manage cost-per-token and throughput SLAs.
vLLMTGILangSmithGuardrails
07
Train & Scale in Production
Use distributed training on GPUs, optimize memory/throughput with quantization, and apply cost-aware strategies for sustainable LLM operations.
Distributed TrainingGPUsQuantizationCost Optimization
Why Learn Large Language Models Now?
LLMs are redefining software. Upskill from Transformer fundamentals to LoRA/QLoRA fine-tuning, RLHF alignment, and evaluation to stay ahead.
195%
Global GenAI Learning Growth
YoY enrollments
107%
India GenAI Enrollments
YoY increase
12/min
GenAI Enrollments
Global pace
Growing
AI Wage Premium
Market signal
Real Industry Impact
Build copilots, RAG systems, and domain chatbots. Learn exactly what companies hire for today.
Hands-On, Not Just Theory
Fine-tune BERT/GPT with LoRA/QLoRA, practice RLHF/DPO, and measure output with BLEU/ROUGE/Perplexity.
Career Upside & Portability
Globally transferable skills. Graduate with a capstone GPT-style model, evaluation report, and certification.
Source: Coursera Global Skills Report 2025, PwC 2025 Global Workforce Survey
🎯 Complete AI Specialization Path
Prerequisites: GenAI Course
Master Transformers & prompting first
Current: LLM Engineering
You are here → Fine-tuning & RLHF
Next: LLMOps Course
Production deployment & scaling
Large Language Model Tools and Frameworks
This Large Language Model Course is taught with production-relevant tools so you can implement fine-tuning, RAG, evaluation, and deployment end-to-end.
Hugging Face Transformers
PyTorch
PEFT (LoRA / QLoRA)
LangChain
LlamaIndex
vLLM
TensorRT-LLM / TGI
Qdrant / FAISS
Large Language Model Course Curriculum
A step-by-step path from transformer basics to real apps with retrieval, memory, and evaluation. Learn to adapt models to your data, ship reliable APIs, and monitor quality in production.
What Sets Our Large Language Model Course Apart?
See how our Large Language Model training delivers real-world fine-tuning, RLHF, and AI engineering skills versus generic online courses.
| Feature | Our Large Language Model Course ✔ | Other AI Courses ✘ |
|---|---|---|
| LLM Engineering Depth | ✔Covers Transformers, BERT, GPT, LoRA/QLoRA fine-tuning, RLHF, and model alignment. | ✘Limited to prompt writing or API usage without true engineering depth. |
| Hands-On Fine-Tuning Projects | ✔Train real models with LoRA/QLoRA using BERT, LLaMA, and GPT architectures. | ✘Mostly theoretical examples or copied notebooks. |
| RLHF & Alignment Modules | ✔Learn RLHF, DPO, and safety evaluation with simulated feedback data. | ✘Skip human-feedback training entirely. |
| Toolchain & Frameworks | ✔Hugging Face, PyTorch, PEFT, LangChain, Gradio, LangSmith — real production stack. | ✘Basic OpenAI APIs with no infrastructure exposure. |
| Capstone: Build Your Own GPT | ✔Final project: fine-tune and deploy a GPT-style model end-to-end with evaluation. | ✘No complete hands-on project. |
| GPU-Optimized Learning | ✔Works seamlessly with Colab GPUs or budget hardware using LoRA/QLoRA. | ✘Requires high-end GPUs or expensive cloud credits. |
| Career-Ready Certification | ✔Certified by School of Core AI with portfolio-ready projects and interview prep. | ✘Generic certificates only — no real project validation. |
| Curriculum Updates | ✔Updated every 2 months with the latest LLM architectures and open-source tools. | ✘Outdated modules, rarely refreshed. |
🎯 Complete Your AI Specialization Journey
🧠
GenAI Fundamentals
Transformers & Prompting
🎯
LLM Engineering
Current: Fine-tuning & RLHF
🚀
LLMOps Deployment
Production & Scaling
🤖
Agentic AI Systems
Autonomous Agents
Your LLM Learning Curve
Follow a structured path from AI fundamentals to production LLM engineering — each stage builds on the last.
01Foundations
Build Strong AI Foundations
Data Science Course
Master Python, statistics, and core ML algorithms. These fundamentals make LLM training dynamics intuitive and help you understand what's happening inside the model.
Explore Data Science Foundations02Fine-Tuning
Master GenAI & LLM Fine-Tuning
Generative AI Course
Go hands-on with Transformers, advanced prompting strategies, and LoRA/QLoRA fine-tuning. Learn to align and evaluate real models for production.
Advance to GenAI Specialization03Current Course
Master LLM Engineering (You Are Here)
Large Language Model Course
Deep LLM engineering: architecture, efficient fine-tuning with LoRA/QLoRA, RLHF/DPO alignment, RAG systems, and evaluation frameworks.
Continue Current Journey04Deployment
Deploy, Scale & Monitor LLMs
LLMOps Course
Ship production-grade systems: inference optimization, vLLM/TGI serving, distributed tracing, and cost control at enterprise scale.
Master LLM Deployment05Advanced
Build Autonomous AI Agents
Agentic AI Course
Create intelligent agents that plan, reason, and execute complex tasks autonomously. Multi-agent orchestration and real-world deployment.
Advance to Agentic AIComplete all 3 stages to become a certified LLM Engineer — from fundamentals to fine-tuning to production deployment.
Large Language Model Course Fees & Enrollment
One all-inclusive fee for 8 weeks of live training, guided projects, capstone assessment, and a verifiable dual certificate.
Admissions open•Next live batch: 15th–30th•Limited seats per cohort
One-time payment
₹40,000
8-Week Program • Live + Recorded • Capstone
Duration: 8-Week Program
Format: Live + Recorded
Projects: Hands-on labs + capstone
Certificate: Dual, verifiable
₹40,000 includes live training, guided projects, capstone, dual certificate, and placement support — no hidden charges.
What you'll get
- Live instructor-led sessions on Transformers, fine-tuning, RAG, and deployment.
- Hands-on labs: LoRA/QLoRA fine-tuning, RLHF/DPO, RAG with LangChain, vLLM serving.
- Capstone project: fine-tune, evaluate, deploy, and demo a domain-specific LLM.
- Career support: resume review, portfolio guidance, mock interviews, and referrals.
- Lifetime recordings + future module updates access.
Best for working developers: plan for ~8–10 hrs/week (live sessions + lab time).
Testimonials from Successful Learners
What our learners have to say about the Large Language Model Course.
“The LLM course was a turning point in my career. I went from basic AI understanding to building a production customer service chatbot. The hands-on fine-tuning and optimization modules gave me real practical skills.”
AM
Arjun Mehta
ML Engineer
“This course helped me understand how LLMs apply to real-world problems — recommendation systems, advanced search engines. The practical guidance gave me confidence to work with LLMs on any project.”
PS
Priya Sharma
AI Developer
“The right balance of theory and practice. I can now summarize documents and answer complex questions with LLMs. The fine-tuning techniques made it easy to optimize models for specific use cases.”
RK
Rahul Kapoor
NLP Engineer
“Switching to ML engineering was a major shift, and this course made it achievable. I built a text summarization tool using fine-tuned models, and the course's quantization techniques improved real-world performance.”
VC
Vikram Choudhury
Data Scientist
“I used what I learned to build document classification and data extraction systems, streamlining our team's information flow. The deployment and scaling insights directly impacted our solutions.”
AD
Ananya Desai
AI Product Manager
“I built a customer support system that handles complex queries after this course. The practical focus on fine-tuning and real-world implementation improved our efficiency and response times dramatically.”
NR
Nandini Reddy
Software Engineer
Frequently Asked Questions About the Large Language Model Course
Got More Questions?
Talk to Our Team Directly
Contact us and our academic counsellor will get in touch with you shortly.
🚀 After the Large Language Model Course, continue your journey:
