Large Language Model Course (LLM) — Fine-Tuning, RAG & RLHF

2 Months Featured Specialization

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.

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Program fee

₹40,000

One-time payment

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.

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.

Transformer Fundamentals

Understand how modern LLMs are built — attention, embeddings, positional encodings, and the architecture behind BERT, GPT, LLaMA, Mistral, and DeepSeek.

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.

Alignment & Evaluation

Align models with RLHF and DPO, build preference datasets, evaluate using BLEU/ROUGE/Perplexity, and implement hallucination reduction strategies.

Retrieval & Memory (RAG)

Design retrieval-augmented pipelines with LangChain and LlamaIndex. Choose embeddings, vector stores, and implement re-ranking with citation-grounded answers.

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.

Scaling & MoE

Learn Mixture-of-Experts routing to scale capacity without linear cost. Reason about trade-offs in memory, throughput, latency, and quality.

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 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 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

Course Curriculum

LLM fundamentals
Module 1: Internals
  • LLM Fundamentals
  • Tokenization and Embeddings
  • Attention variants
Module 2: Data and training
  • Data curation
  • Training runs
  • Reproducibility
Adaptation
Module 3: Fine-tuning
  • Fine-tuning and PEFT
  • LoRA and QLoRA
  • Preference alignment
Module 4: RAG skills
  • Chunking and retrieval
  • Re-ranking
  • Grounding
Serve and evaluate
Module 5: Production
  • Scalable Inference
  • Evaluation Metrics
  • Cost-per-token control

Frequently asked questions

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

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

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.

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.

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.

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