Understanding Large Language Models: Architecture, Concepts, and Modern Systems

Overview

A structured 3-day workshop that provides engineers and technical professionals with a clear, conceptual understanding of how large language models work. The workshop focuses on architectures, core mechanisms, and practical trade-offs rather than deep implementation.

Participants will learn how transformer-based models process text, how attention mechanisms enable sequence understanding, how models are trained and served at scale, and how the modern open-source ecosystem is structured. The approach emphasizes clarity and architectural understanding over coding exercises.

Learning Objectives

By the end of this workshop, participants will be able to:

• Explain how transformer-based language models process text from tokenization through generation
• Describe the role of embeddings, attention mechanisms, and transformer blocks in model architectures
• Compare encoder-only, encoder-decoder, and decoder-only architectures and their use cases
• Reason about context windows, scaling laws, and their engineering implications
• Explain pretraining objectives, data requirements, and compute economics
• Describe decoding strategies and their effect on generated text quality
• Compare fine-tuning approaches including LoRA, instruction tuning, and RLHF
• Evaluate models from the current open-source ecosystem for practical applications
• Navigate the LLM landscape with confidence and make informed technical decisions

Target Audience

This workshop is designed for:

• Software engineers wanting to understand LLM internals without building from scratch
• Technical leads evaluating LLM technologies for their teams
• Data engineers and ML practitioners seeking architectural clarity
• Technical professionals who work with or around LLM systems
• Anyone with a technical background who wants a structured understanding of modern AI systems

Prerequisites

Participants should have:

• General software engineering or technical background
• Comfort reading code examples (Python familiarity helpful but not required)
• Basic understanding of what neural networks are at a high level
• No prior LLM, deep learning, or PyTorch experience required

Workshop Format

The workshop combines:

• Instructor-led concept sessions with visual explanations
• Structured exercises focused on reasoning and analysis
• Architecture exploration and comparison activities
• Group discussions on trade-offs and design decisions
• Lightweight demonstrations illustrating key concepts

Participants leave with a strong mental model of how modern LLM systems are built, trained, and deployed.

Day 1 - Foundations: From Text to Transformer Architecture

Topics

• What large language models are and how they evolved from earlier approaches
• The transformer revolution and why it changed the field
• The end-to-end LLM pipeline: data, tokenization, embeddings, model, decoding
• Tokenization strategies: character, word, subword, and Byte Pair Encoding
• Why vocabulary design matters for model efficiency
• Token embeddings and positional encodings
• Encoder-only, encoder-decoder, and decoder-only architectures
• Transformer blocks: attention, feed-forward networks, normalization, residual connections
• How GPT-style models compose these components
• Model configurations and how architectures scale

Activities

• Map the LLM pipeline from raw text to generated output
• Compare architecture families and their design trade-offs
• Explore tokenization strategies on real text
• Trace data flow through a transformer block
• Analyze scaling configurations of real models

Day 1 Outcome

Participants understand the full architecture of a transformer-based language model, from how text enters the system as tokens to how stacked transformer blocks process representations.

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Day 2 - Core Mechanisms: Attention, Training, and Generation

Topics

• Self-attention: queries, keys, values, and attention scores
• Causal masking for autoregressive language models
• Multi-head attention and why multiple heads capture diverse patterns
• Context windows: engineering constraints and extension techniques
• Pretraining objectives: next-token prediction and why it works
• Training data requirements, scale, and quality considerations
• Compute economics and scaling laws
• Autoregressive text generation and the decoding loop
• Temperature, top-k, top-p sampling, and beam search
• Inference infrastructure: KV caching, batching, quantization
• Latency versus throughput trade-offs in serving

Activities

• Compute attention scores and causal masking by hand
• Analyze multi-head attention and context window trade-offs
• Estimate pretraining costs and data requirements
• Explore how next-token prediction produces capable models
• Compare decoding strategies and their effects on output
• Reason about serving infrastructure constraints

Day 2 Outcome

Participants understand how attention works mechanistically, why pretraining produces capable models, and how inference and serving systems are designed.

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Day 3 - Modern Landscape: Adaptation, Ecosystem, and Practice

Topics

• Fine-tuning versus prompting: when and why to adapt models
• Supervised fine-tuning and instruction tuning
• RLHF and alignment: from base models to useful assistants
• Parameter-efficient methods: LoRA, adapters, and QLoRA
• Task-specific adaptation for classification and generation
• The open-source model ecosystem: LLaMA, Mistral, Gemma, Qwen, Phi
• Model selection criteria for real-world applications
• Deployment options: self-hosted, API-based, edge
• Limitations, failure modes, and responsible use
• Where the field is heading: reasoning, agents, multimodal

Activities

• Choose the right adaptation strategy for different scenarios
• Understand the RLHF alignment pipeline
• Evaluate models for real-world application scenarios
• Map the open-source LLM ecosystem
• Build a comprehensive LLM knowledge map
• Identify personal next learning paths

Day 3 Outcome

Participants can evaluate and choose models, understand how they are adapted for specific tasks, and navigate the modern LLM ecosystem with confidence.