LLM Fundamentals
Table of Contents
Introduction
Large Language Models (LLMs) like GPT, Claude, and LLaMA are reshaping how we build intelligent systems. This guide provides a concise foundation for understanding LLM architecture, training, and applications.
By the end, you'll understand not just what LLMs are, but how they work under the hood and how to leverage them effectively in your projects.
Who Is This Guide For?
AI engineers, builders, and researchers who want to understand LLMs deeply enough to build effective applications, debug issues, and make informed model selection decisions.
1. Foundations
Definition: LLMs are massive neural networks trained on large-scale text corpora to predict sequences. They're sophisticated pattern-matching machines that have learned the statistical regularities of human language.
Core Abilities
- Content Generation: Text, code, dialogue, creative writing
- Summarization & Classification: Distilling and categorizing content
- Reasoning & Planning: Breaking down complex problems step-by-step
- Translation: Converting between languages and formats
Scaling Properties
LLMs scale with parameters (model size), data (training corpus), and compute. This scaling unlocks emergent capabilities:
- In-context learning: Learning tasks from just a few examples
- Chain-of-thought reasoning: Breaking down complex problems
- Zero-shot generalization: Handling tasks they weren't explicitly trained for
The "emergent capabilities" of LLMs often surprise researchers. As models scale, they suddenly acquire abilities that smaller models lack—like solving math problems or writing functional code.
2. Transformer Architecture
The transformer is the backbone of all modern LLMs. Understanding its components helps demystify how these models process and generate text.
Key Components
1. Tokenization
Convert text into subwords using BPE, WordPiece, or SentencePiece. Allows models to handle any text, even unseen words.
2. Embeddings + Positional Encoding
Map tokens to high-dimensional vectors with position information. Tells the model what words are present and where they appear.
3. Attention Mechanism
The secret sauce. Allows models to focus on relevant parts of input when processing each token. Uses Queries, Keys, and Values.
4. Transformer Block
Core building block repeated many times: Attention → Feed-Forward MLP → Residual connections + LayerNorm.
Model Variants
Encoder-only (BERT)
Understanding and classification
Decoder-only (GPT)
Text generation and completion
Encoder-Decoder (T5)
Translation and summarization
Don't get overwhelmed by attention math. The key insight: attention allows models to dynamically focus on relevant information, like re-reading important parts of a sentence to understand it.
3. Training & Adaptation
Training and adapting LLMs involves several stages, each improving capabilities for specific use cases.
Training Pipeline
Training Objective
Predict next token (causal LM) or fill masked tokens (masked LM). This simple objective at scale leads to remarkable capabilities.
Fine-tuning Methods
- SFT: Supervised fine-tuning on task examples
- LoRA: Efficient updates using small matrices
- Preference Alignment: RLHF, DPO to align with human preferences
Prompting Strategies
- Zero-shot: Direct instruction without examples
- Few-shot: Provide examples to demonstrate the task
- Chain-of-Thought: Guide step-by-step reasoning
Efficiency Techniques
Compression
- Distillation: Transfer to smaller models
- Quantization: Reduce precision
- Pruning: Remove connections
Architecture
- MoE: Activate only relevant parts
- FlashAttention: Faster computation
- Sparse methods: Process key tokens
Start with prompting before jumping to fine-tuning. Modern LLMs are so capable that clever prompting often achieves what previously required fine-tuning.
4. Applications
LLMs have transformed what's possible in AI applications:
Text Generation
- Creative writing and storytelling
- Technical documentation
- Code generation and completion
Understanding & Analysis
- Semantic search and retrieval
- Document classification
- Information extraction
Sequence Tasks
- Language translation
- Text summarization
- Format conversion
Reasoning & Agents
- Multi-step question answering
- Task planning and decomposition
- Tool use and API integration
Retrieval-Augmented Generation (RAG)
One of the most powerful patterns. RAG combines LLM generation with external knowledge retrieval, allowing models to access up-to-date information and cite sources.
5. Quick Reference
Summary of key concepts:
| Concept | Summary |
|---|---|
| LLM | Large neural net trained on massive text corpora |
| Transformer | Parallel attention-based architecture |
| Architectures | Encoder (BERT), Decoder (GPT), Encoder-Decoder (T5) |
| Training | Predict missing or next tokens |
| Adaptation | SFT, LoRA, RLHF |
| Efficiency | Distillation, Quantization, MoE |
| Applications | Generation, search, reasoning, agents |
Next Steps
- Hands-on: Experiment with OpenAI or Anthropic APIs
- Build: Create a simple chatbot or text classifier
- Dive deeper: Explore specific architectures (GPT, BERT, T5)
- Stay updated: Follow research from major AI labs
LLMs are tools, not magic. Understanding fundamentals helps you use them effectively and recognize both their capabilities and limitations.