Large Language Models: Complete Pro Course
The complete pro course on large language models. Architecture, training, major LLMs, prompting, RAG, fine-tuning, inference, evaluation, agents, multimodal, and enterprise deployment. 70+ hours, 6 projects.
Course Snapshot
| Course Code | AIM-606 |
| Duration | 70+ hours |
| Modules | 15 |
| Projects | 6 hands-on projects |
| Level | Advanced |
| Format | Self-paced + live sessions |
| Coverage | Architecture, Training, RAG, Fine-Tuning, Serving, Evaluation, Agents, Multimodal |
| Certificate | Yes, upon completion |
| Price | ₹7,999 (early bird ₹5,499) |
Course Overview
Large Language Models have become the foundational infrastructure of modern AI, powering everything from chatbots and search to code generation and scientific research. This course is the most comprehensive deep dive into LLMs available — covering architecture, training, evaluation, fine-tuning, deployment, and the open-source ecosystem. You will start by understanding the transformer architecture at a mathematical level, then trace the full training pipeline from pre-training on web-scale corpora to alignment with human preferences. You will review every major LLM family — GPT, Claude, Llama, Mistral, Gemini, and more — understanding their architectural choices, capabilities, and limitations. You will master advanced prompting techniques that go far beyond basic instructions, then build production RAG systems with sophisticated retrieval and reranking. You will explore fine-tuning strategies from full-parameter to parameter-efficient methods, and learn to serve models efficiently with quantization, batching, and speculative decoding. You will evaluate LLMs rigorously with both automated and human evaluation frameworks. You will build LLM-powered agents, work with multimodal models that process text, images, and audio, and deploy enterprise-grade systems with guardrails, observability, and cost controls. By the end, you will have six real-world projects and the expertise of a senior LLM engineer.
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Enroll Now — Limited Seats →Who This Course Is Built For
This course is designed for specific professional profiles. If you match any of these, you will get maximum value.
ML/AI Engineers
Engineers who work with LLMs daily and need deep technical understanding to build, fine-tune, and deploy production systems.
Software Architects
Architects designing AI systems who need to understand LLM capabilities, limitations, and trade-offs to make informed design decisions.
Data Scientists
Data scientists moving into NLP and generative AI who need a rigorous, end-to-end understanding of the LLM stack.
Technical Leaders
CTOs and tech leads evaluating LLM strategies for their organizations who need enough depth to guide engineering teams and vendor decisions.
This Course Is NOT For You If:
- Those without Python and basic ML knowledge — this is an advanced, technical course.
- People who only want to learn prompt engineering — see our Prompt Engineering Pro course instead.
- Anyone looking for a high-level, non-technical overview — every module involves code and math.
What You Will Learn
After completing this course, you will be able to:
- 1 You will be able to explain transformer architecture, attention mechanisms, and the training pipeline for large language models.
- 2 You will be able to compare major LLM families and select the right model for a given task based on capabilities, cost, and licensing.
- 3 You will be able to apply advanced prompting techniques including chain-of-thought, tree-of-thought, and self-consistency.
- 4 You will be able to build production RAG systems with hybrid retrieval, reranking, and evaluation pipelines.
- 5 You will be able to fine-tune LLMs using full-parameter, LoRA, QLoRA, and instruction-tuning strategies.
- 6 You will be able to serve LLMs efficiently with quantization, batching, KV cache optimization, and speculative decoding.
- 7 You will be able to evaluate LLMs with automated benchmarks, LLM-as-judge, and human evaluation frameworks.
- 8 You will be able to build LLM-powered agents that plan, use tools, and accomplish multi-step tasks.
- 9 You will be able to work with multimodal LLMs that process text, images, and audio in unified models.
- 10 You will be able to deploy enterprise LLM systems with guardrails, observability, cost controls, and compliance considerations.
Download Free Course Syllabus
Get the complete detailed syllabus with all modules, lessons, and project descriptions delivered to your inbox.
Download Free Syllabus →Complete Course Curriculum
15 modules with detailed lessons. Every lesson includes specific learning points.
MODULE 1 — LLM Landscape & Architecture
5 hours- Self-attention, multi-head attention, and scaled dot-product attention
- Positional encoding: sinusoidal, learned, RoPE, and ALiBi
- Feed-forward networks, layer normalization, and residual connections
- Encoder, decoder, and encoder-decoder architectures compared
- The evolution from GPT-1 through GPT-4o and beyond
- Scaling laws and how parameter count, data, and compute interact
- Emergent capabilities at scale: in-context learning, chain-of-thought
- The current LLM landscape: proprietary, open-source, and hybrid models
- Mixture of Experts (MoE) and sparse activation patterns
- Grouped-query and multi-query attention for inference efficiency
- Sliding window attention and long-context strategies
- State space models and alternatives to transformers
MODULE 2 — How LLMs Are Trained
5 hours- Data collection, deduplication, and quality filtering for pre-training
- Tokenization with BPE, SentencePiece, and tiktoken
- Next-token prediction and the causal language modeling objective
- Distributed training: data parallelism, tensor parallelism, and pipeline parallelism
- Supervised fine-tuning on instruction-response pairs
- The role of high-quality instruction data in model behavior
- Reinforcement Learning from Human Feedback (RLHF) overview
- Direct Preference Optimization (DPO) as a simpler alternative
- Constitutional AI and self-critique alignment methods
- Red teaming: finding and fixing model vulnerabilities
- Safety filters, refusal training, and detoxification
- The alignment tax and trade-offs between helpfulness and safety
MODULE 3 — Major LLMs Review
5 hours- GPT-4o family: architecture, capabilities, and API features
- Claude 3.5 Sonnet and Opus: long context, computer use, and analysis
- Gemini 1.5 Pro: multimodal capabilities and massive context windows
- Pricing, rate limits, and feature comparison across providers
- Llama 3 and 3.1: Meta’s open models and their ecosystem
- Mistral and Mixtral: efficient architectures and MoE variants
- Phi-3 and small language models for edge deployment
- Qwen, DeepSeek, and other international open-source models
- Decision framework: task type, latency, cost, and privacy requirements
- Benchmark interpretation and why leaderboards can mislead
- Licensing considerations for commercial and research use
- Build vs buy: when to fine-tune vs use an API
MODULE 4 — Capabilities & Limitations
4 hours- Text generation, summarization, translation, and code generation
- Few-shot and zero-shot learning from prompts alone
- Reasoning, planning, and multi-step problem solving
- Creative writing, brainstorming, and content ideation
- Hallucination: causes, detection, and mitigation strategies
- Context window limits and techniques for extending effective context
- Knowledge cutoffs and the need for retrieval augmentation
- Reasoning failures on math, logic, and temporal tasks
- Sources of bias in training data and model behavior
- Measuring and mitigating demographic and representational bias
- Copyright and intellectual property concerns with generated content
- Responsible AI practices and organizational governance
MODULE 5 — Prompting for LLM Engineers
5 hours- Chain-of-thought prompting and self-consistency for reasoning
- Tree-of-thought and graph-of-thought for complex problem solving
- ReAct prompting for reasoning and tool use
- Prompt chaining and decomposition for multi-step tasks
- JSON mode, function calling, and structured output enforcement
- Constraining outputs with grammars and regex patterns
- Prefix tuning and soft prompts for controllable generation
- System prompts, role prompts, and persona engineering
- Systematic prompt engineering: iteration, evaluation, and versioning
- A/B testing prompts with statistical significance
- Prompt compression for cost and latency reduction
- Automated prompt optimization with DSPy and OPRO
MODULE 6 — Retrieval-Augmented Generation
6 hours- The RAG pipeline: indexing, retrieval, generation, and citation
- Chunking strategies: fixed-size, semantic, and document-aware splitting
- Embedding models: selection, dimensionality, and domain adaptation
- Vector databases: Pinecone, Weaviate, Qdrant, and pgvector compared
- Hybrid search combining BM25 and dense vector retrieval
- Reranking with cross-encoders and LLM-based reranking
- Query transformation: expansion, decomposition, and HyDE
- Multi-vector and parent-child retrieval strategies
- Evaluating RAG with RAGAS, TruLens, and custom metrics
- Measuring retrieval quality: recall, precision, and MRR
- Measuring generation quality: faithfulness, relevance, and groundedness
- Optimizing end-to-end RAG pipelines for cost and latency
MODULE 7 — Fine-Tuning Overview
5 hours- Fine-tuning vs prompting vs RAG: decision frameworks
- Domain adaptation: when pre-trained models need specialization
- Cost-benefit analysis of fine-tuning vs API usage
- Data requirements and quality considerations for fine-tuning
- Full-parameter fine-tuning: when you need it and what it costs
- LoRA and QLoRA: parameter-efficient fine-tuning explained
- Instruction tuning and chat fine-tuning formats
- Continual pre-training for domain-specific knowledge injection
- Preparing and formatting training data for instruction tuning
- Using Hugging Face Trainer, TRL, and Axolotl for fine-tuning
- Monitoring training loss, evaluation metrics, and overfitting
- Evaluating fine-tuned models against base models
MODULE 8 — Inference & Serving
5 hours- Autoregressive generation and the KV cache
- Prefill vs decode phases and their performance characteristics
- Memory bandwidth bottlenecks in LLM inference
- Batching strategies: static, dynamic, and continuous batching
- Post-training quantization: INT8, INT4, and GPTQ
- Activation-aware quantization with AWQ and GGUF
- Speculative decoding and draft model acceleration
- Flash Attention and PagedAttention for memory efficiency
- vLLM: high-throughput serving with PagedAttention
- TGI (Text Generation Inference) from Hugging Face
- llama.cpp and Ollama for local and edge deployment
- TensorRT-LLM for NVIDIA-optimized inference
MODULE 9 — LLM APIs
4 hours- OpenAI API: chat completions, assistants, and embeddings
- Anthropic API: messages format and extended thinking
- Google Gemini API: multimodal input and generation
- Rate limits, retries, and error handling best practices
- Server-sent events and streaming token generation
- Async API calls for high-concurrency applications
- Backpressure handling and queue management
- Webhook and callback patterns for long-running generations
- Token counting and context window management
- Prompt caching and completion caching strategies
- Model routing: using cheaper models for simpler tasks
- Budget tracking and spend alerts for production systems
MODULE 10 — Evaluation
5 hours- Academic benchmarks: MMLU, HumanEval, GSM8K, and their limitations
- LM Evaluation Harness and the Open LLM Leaderboard
- Task-specific evaluation: custom metrics for your use case
- Statistical significance and confidence intervals in evaluation
- Using strong LLMs to evaluate weaker LLMs
- Pairwise comparison and reference-free evaluation
- Bias and limitations of LLM-as-judge approaches
- Building reproducible evaluation pipelines with promptfoo and DeepEval
- Designing human evaluation studies: rubrics and annotators
- Crowdsourcing evaluation with platforms like Scale and Surge
- Inter-annotator agreement and quality control
- A/B testing LLM outputs in production with real users
MODULE 11 — AI Agents
5 hours- ReAct, plan-and-execute, and reflection patterns for agents
- Tool calling and function calling with LLMs
- Planning strategies: forward planning, tree search, and Monte Carlo
- Memory systems: short-term, long-term, and episodic memory for agents
- LangChain and LangGraph for agent workflows
- CrewAI for multi-agent crew orchestration
- OpenAI Assistants API for managed agent infrastructure
- Choosing the right framework for your agent use case
- Evaluating agent trajectories and task completion rates
- Tool use accuracy and error recovery evaluation
- Safety guardrails: prompt injection defense and output validation
- Human-in-the-loop design for high-stakes agent actions
MODULE 12 — Multimodal LLMs
5 hours- Vision-language models: CLIP, BLIP, and LLaVA architectures
- Image tokenization and visual encoding strategies
- Cross-attention and fusion approaches for multimodal input
- Audio-language models: Whisper and audio understanding
- GPT-4o vision: image understanding, OCR, and visual reasoning
- Claude vision: document analysis and chart interpretation
- Gemini multimodal: video understanding and audio processing
- Open-source multimodal models: LLaVA and Qwen-VL
- Image captioning, VQA, and visual document understanding pipelines
- Video analysis: frame extraction, temporal reasoning, and summarization
- Audio transcription, speaker diarization, and voice applications
- Evaluating multimodal model performance across modalities
MODULE 13 — Enterprise Deployment
5 hours- Gateway patterns: routing, caching, and fallback across providers
- Private deployment: on-premise and VPC-hosted LLMs
- Hybrid architectures: combining API and self-hosted models
- Scalability: load balancing, auto-scaling, and queue management
- Input and output filtering: PII detection, toxicity, and topic guardrails
- Data residency and privacy compliance (GDPR, HIPAA, SOC 2)
- Audit logging and model provenance for regulated industries
- Model versioning, rollback, and A/B deployment strategies
- LLM observability: tracing, metrics, and logging with Langfuse
- Cost tracking and attribution across teams and projects
- Quality monitoring: drift detection and regression alerts
- Capacity planning and budget forecasting for LLM infrastructure
MODULE 14 — Open Source Ecosystem
4 hours- How Llama changed the landscape and democratized LLM access
- Fine-tuned variants: community models, merges, and specialized builds
- The role of Hugging Face as the central hub for open models
- Contributing back: datasets, model cards, and evaluation results
- Local deployment with Ollama, LM Studio, and llama.cpp
- Cloud deployment on RunPod, Modal, and Replicate
- Hardware requirements: GPU selection and memory planning
- Model formats: GGUF, safetensors, and ONNX for portability
- Fine-tuning community models for specific domains
- Model merging techniques: SLERP, DARE, and task arithmetic
- Creating and sharing fine-tuned variants on the Hub
- Building and maintaining a custom model registry for your organization
MODULE 15 — Capstone Projects
4 hours- Choosing a capstone that demonstrates end-to-end LLM engineering
- Scoping the project to cover multiple course modules
- Designing the architecture: model selection, retrieval, and serving
- Defining evaluation criteria and success metrics
- Building the full system from data to deployment
- Iterating on quality with evaluation-driven development
- Optimizing for latency, cost, and quality trade-offs
- Documenting architecture decisions and trade-off rationale
- Deploying the system to a production or demo environment
- Creating a technical writeup and architecture diagram
- Recording a demo walkthrough of the system in action
- Peer review and incorporating feedback for final polish
Real-World Projects & Portfolio Outcomes
6 portfolio-worthy projects that prove your skills to employers.
Advanced RAG System
Build a production-grade RAG system with hybrid retrieval, cross-encoder reranking, query transformation, and comprehensive evaluation using RAGAS metrics on a domain-specific document set.
Fine-Tuned Domain Model
Fine-tune an open-source LLM on a domain-specific dataset using LoRA or QLoRA, demonstrating measurable improvement over the base model on domain-specific evaluation tasks.
High-Throughput Inference Server
Deploy an open-source LLM with vLLM or TGI, implementing continuous batching, quantization, and monitoring to achieve target latency and throughput benchmarks.
LLM Evaluation Pipeline
Build a comprehensive evaluation pipeline that tests an LLM across multiple dimensions — accuracy, faithfulness, safety, and latency — using both automated and LLM-as-judge methods.
Multimodal Document Assistant
Create a multimodal assistant that understands text, images, and tables in documents, using a vision-language model to answer questions about uploaded PDFs and screenshots.
Enterprise LLM Gateway
Build an enterprise LLM gateway that routes requests across multiple providers, implements guardrails, tracks costs, provides caching, and includes observability and audit logging.
Tools, Technologies & Models Covered
LLM Models Covered
| GPT-4o | OpenAI | General-purpose LLM with vision and function calling |
|---|---|---|
| Claude 3.5 Sonnet | Anthropic | Long-context reasoning and analysis |
| Llama 3.1 70B | Meta | Open-source LLM for fine-tuning and self-hosting |
| Mistral Large | Mistral AI | Efficient European open model |
| Gemini 1.5 Pro | Multimodal with 2M token context | |
| Phi-3 Medium | Microsoft | Small language model for edge deployment |
| Qwen 2.5 | Alibaba | Multilingual open-source LLM |
| DeepSeek V2 | DeepSeek | MoE open model with strong reasoning |
Tools & Frameworks Covered
| PyTorch | Model training and fine-tuning | Deep |
|---|---|---|
| Hugging Face Transformers | Model loading and inference | Deep |
| TRL | Supervised fine-tuning and alignment | Deep |
| PEFT | Parameter-efficient fine-tuning | Deep |
| vLLM | High-throughput inference serving | Deep |
| LangChain | LLM application development | Intermediate |
| LangGraph | Agent workflow orchestration | Intermediate |
| RAGAS | RAG evaluation | Intermediate |
| Langfuse | LLM observability and tracing | Intermediate |
| Ollama | Local model deployment | Intermediate |
| Axolotl | Fine-tuning pipeline management | Intermediate |
| TensorRT-LLM | NVIDIA-optimized inference | Intermediate |
Book a Free Counselling Call
Not sure which course is right for you? Book a free 1:1 counselling call with our AI training advisors.
Book Free Counselling Call →Your Instructor
Dr. Vikram Iyer
Former LLM Research Lead at a Top AI Lab & Industry Consultant
12+ years in NLP, deep learning, and large language model research
Contributed to training models with 70B+ parameters
5,000+ engineers and researchers trained globally
Understanding LLMs deeply means understanding the entire stack — from attention math to serving infrastructure. Surface-level knowledge breaks the moment you hit production.
Course Team & Curriculum Design
Our teaching team includes two additional PhD-level researchers who co-developed the curriculum and lead advanced modules, plus three teaching assistants who provide daily support on Discord and during project weeks.
Learning Path & Prerequisites
Prerequisites
- Strong Python programming and comfort with data processing libraries
- Solid understanding of machine learning fundamentals: training, loss, gradients
- Familiarity with PyTorch or TensorFlow for model training
- Basic linear algebra and probability for understanding attention and training
- Experience with REST APIs and command-line tools
Recommended Learning Paths
ML Engineer building LLM applications
- Complete Modules 1-6 for architecture, prompting, and RAG mastery
- Focus on Module 8 (Inference) for serving models in production
- Use Module 9 (APIs) for integrating proprietary models
- Build the Advanced RAG System project as your first capstone
Researcher pushing LLM capabilities
- Deep-dive Modules 1-2 for architectural and training fundamentals
- Focus on Module 7 (Fine-Tuning) and Module 10 (Evaluation)
- Study Module 14 (Open Source) for reproducible research workflows
- Build the Fine-Tuned Domain Model project to demonstrate research skills
Architect deploying enterprise LLM systems
- Master Modules 1-4 for understanding LLM capabilities and limitations
- Prioritize Module 8 (Serving) and Module 13 (Enterprise Deployment)
- Study Module 12 (Multimodal) for modern enterprise use cases
- Build the Enterprise LLM Gateway as your capstone project
What Comes After This Course
Pricing & Enrollment
Choose the plan that fits your learning goals. All plans include a 7-day money-back guarantee.
| Self-Paced | ₹7,999 | All 15 modules, 6 projects, community access, certificate |
|---|---|---|
| Early Bird | ₹5,499 | Same as Self-Paced, limited time pricing |
| Cohort + Mentorship | ₹19,999 | Live sessions, 1:1 mentorship, project reviews, priority support |
What Is Included
- Lifetime access to all 15 modules and future updates
- 6 hands-on projects with detailed walkthroughs
- Downloadable code templates, training scripts, and evaluation suites
- Community Discord with instructors, TAs, and peers
- Certificate of completion recognized by partner companies
- Access to a private library of LLM architecture decision records
30-day money-back guarantee — if you complete the first three modules and don’t feel this course delivers, get a full refund, no questions asked.
Frequently Asked Questions
How is this different from the Hugging Face course?
The Hugging Face course focuses on the Hugging Face ecosystem and tooling. This course covers LLMs comprehensively — architecture, training, all major model families, evaluation, serving, agents, multimodal, and enterprise deployment — regardless of which tools or providers you use.
Do I need a GPU for this course?
Most modules can be completed using cloud APIs and Google Colab. The fine-tuning and inference modules benefit from GPU access, but we provide Colab notebooks and cloud deployment guides so you can follow along without owning hardware.
Is this course too advanced for me?
If you have solid Python skills, understand basic ML concepts, and have used LLM APIs before, you are ready. The first two modules build foundations, and the course progressively increases in depth. If you are new to ML entirely, start with our Prompt Engineering Pro course first.
Will I learn to train LLMs from scratch?
Training from scratch requires massive compute that most learners don’t have. This course teaches the full training pipeline conceptually and provides hands-on fine-tuning experience, which is what most practitioners actually need. We cover pre-training theory thoroughly in Module 2.
Which LLM providers does the course cover?
We cover OpenAI, Anthropic, Google, and major open-source models (Llama, Mistral, Phi, Qwen, DeepSeek). The skills you learn are provider-agnostic, so you can apply them to any current or future LLM.
Does the course cover multimodal models?
Yes. Module 12 is dedicated to multimodal LLMs, covering vision-language models, audio understanding, and building multimodal applications. One of the capstone projects involves building a multimodal document assistant.
How much API cost should I budget for the course?
Budget approximately ₹3,000-₹5,000 for API calls during the course. We provide extensive cost optimization guidance, caching templates, and model routing strategies to keep your spending minimal while still completing all exercises.
How often is the course updated?
The LLM field evolves rapidly. We update the course every quarter to cover new models, techniques, and best practices. The lastUpdated field reflects the most recent revision date.
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