Analyzing and Deploying Scalable LLM Architectures Training

Lesson 1 • Transformer Architecture Fundamentals

Covers attention mechanisms, encoder-decoder structures, and positional encoding. Establishes the architectural baseline required for all subsequent LLM analysis.

Lesson 2 • LLM Taxonomy and Model Families

Surveys encoder-only, decoder-only, and encoder-decoder model families and their canonical use cases. Enables informed model selection for specific deployment scenarios.

Lesson 3 • Pre-training Objectives and Data

Explains masked language modeling, causal language modeling, and next-sentence prediction. Links pre-training objective selection to emergent model capabilities.

Lesson 4 • Tokenization and Vocabulary Design

Examines byte-pair encoding, WordPiece, and SentencePiece tokenization schemes. Connects vocabulary choices to model capacity and downstream task performance.

Lesson 1 • Human Evaluation Protocols

Teaches annotation schema design, inter-annotator agreement, and preference ranking methods. Connects human judgment to ground-truth quality signals unavailable in automated metrics.

Lesson 2 • Safety and Bias Assessment

Examines toxicity classifiers, stereotype benchmarks, and red-teaming methodologies. Prepares practitioners to identify and document model risks before deployment.

Lesson 3 • Hallucination Detection and Factuality

Analyses sources of hallucination and methods for factuality scoring using retrieval and entailment models. Directly informs deployment decisions where accuracy is critical.

Lesson 4 • Evaluation Pipeline Automation

Builds automated evaluation harnesses using LLM-as-judge and regression testing. Enables continuous quality monitoring across model versions in production.

Lesson 1 • Constitutional and Rule-Based Alignment

Covers constitutional AI principles, critique-revision loops, and rule-based reward shaping. Provides scalable alignment techniques that reduce dependence on human labelers.

Lesson 2 • Supervised Fine-Tuning Fundamentals

Covers dataset formatting, learning rate scheduling, and catastrophic forgetting mitigation. Establishes the baseline adaptation workflow before introducing advanced alignment methods.

Lesson 3 • Reinforcement Learning from Human Feedback

Explains reward model training, PPO optimisation, and KL-divergence constraints in RLHF pipelines. Connects alignment objectives to measurable improvements in helpfulness and safety.

Lesson 4 • Parameter-Efficient Fine-Tuning Methods

Teaches LoRA, QLoRA, prefix tuning, and adapter layers as compute-efficient alternatives to full fine-tuning. Reduces hardware requirements while preserving adaptation quality.

Lesson 1 • Knowledge Distillation Pipelines

Teaches teacher-student training, intermediate layer distillation, and task-specific distillation objectives. Produces compact student models that retain teacher-level accuracy.

Lesson 2 • Pruning and Sparsity Methods

Examines structured and unstructured pruning, magnitude-based and gradient-based criteria, and sparse inference kernels. Reduces parameter count while preserving task performance.

Lesson 3 • Speculative Decoding and Batching

Explains speculative decoding with draft models, continuous batching, and paged attention for throughput optimisation. Reduces per-token latency without degrading output quality.

Lesson 4 • Hardware-Aware Optimisation

Covers GPU memory hierarchy, tensor parallelism for inference, and kernel fusion techniques. Aligns compression choices with target hardware constraints for maximum efficiency.

Lesson 1 • Chunking and Indexing Strategies

Examines fixed-size, semantic, and hierarchical chunking methods and their impact on retrieval precision. Connects document structure to retrieval quality in downstream generation.

Lesson 2 • RAG Evaluation and Iteration

Applies RAGAS and component-level metrics to measure retrieval and generation quality independently. Enables systematic diagnosis and improvement of RAG pipeline failures.

Lesson 3 • Vector Databases and Embedding Models

Covers dense retrieval with bi-encoder embeddings, approximate nearest neighbour indexing, and vector store selection. Establishes the retrieval backbone for all RAG architectures.

Lesson 4 • Context Window Management

Covers context compression, lost-in-the-middle mitigation, and dynamic context selection strategies. Ensures retrieved content is positioned for maximum LLM attention.

Lesson 1 • Data Parallelism and Gradient Synchronisation

Covers DDP, gradient accumulation, and all-reduce communication patterns for multi-GPU training. Provides the foundational parallelism strategy before introducing model-level techniques.

Lesson 2 • 3D Parallelism and Hybrid Strategies

Combines data, tensor, and pipeline parallelism into 3D configurations for trillion-parameter models. Provides a decision framework for selecting parallelism combinations given hardware topology.

Lesson 3 • Tensor and Sequence Parallelism

Explains column and row tensor splitting, sequence parallelism for long contexts, and activation memory reduction. Enables training of models that exceed single-GPU memory limits.

Lesson 4 • Pipeline Parallelism and Micro-Batching

Teaches GPipe and 1F1B pipeline schedules, bubble overhead reduction, and inter-stage communication. Scales training depth across nodes while maintaining hardware utilisation.

Lesson 1 • Observability and Reliability Engineering

Covers latency percentile tracking, error rate alerting, and chaos engineering for LLM serving systems. Ensures production reliability through proactive monitoring and failure testing.

Lesson 2 • Multi-Model and Multi-Tenant Serving

Examines model multiplexing, tenant isolation, and resource quota enforcement in shared inference clusters. Enables cost-efficient serving of multiple models and user groups.

Lesson 3 • Autoscaling and Load Balancing

Covers horizontal pod autoscaling, GPU-aware scheduling, and request routing strategies for LLM workloads. Maintains throughput SLAs under variable traffic without over-provisioning.

Lesson 4 • Caching and Prefix Reuse

Teaches KV-cache sharing, prompt prefix caching, and semantic caching layers to reduce redundant computation. Significantly lowers cost per request for repetitive workloads.

Lesson 1 • Multi-Agent Orchestration

Covers supervisor-worker patterns, peer-to-peer agent communication, and conflict resolution in multi-agent systems. Scales task completion through coordinated agent collaboration.

Lesson 2 • Agent Architectures and Reasoning Loops

Covers ReAct, plan-and-execute, and reflection-based agent loops and their failure modes. Provides the structural foundation for building goal-directed LLM agents.

Lesson 3 • Tool Use and Function Calling

Teaches structured function calling, tool schema design, and error handling for external API integration. Extends LLM capabilities beyond text generation to real-world action execution.

Lesson 4 • Memory Systems for Agents

Examines in-context, episodic, semantic, and procedural memory architectures for long-horizon tasks. Enables agents to maintain coherent state across extended multi-step interactions.