Phase 1: Foundation~5 minbeginner

🔬Research & Vision

Day 0: Where It All Begins

Understanding the problem space, defining objectives, and surveying the landscape of language AI research.

Why LLMs?History of NLPKey Research PapersProblem Definition

Why Build a Large Language Model?

Before diving into the technical details, let's understand why LLMs matter. Large Language Models represent one of the most significant breakthroughs in artificial intelligence — they can understand and generate human language with remarkable fluency, reason about complex problems, and assist with tasks ranging from writing code to explaining quantum physics.

Think of it like this...

Imagine if you could distill the knowledge from millions of books, websites, and conversations into a single system that can answer questions, help you write, and even code. That's essentially what an LLM is — a compressed representation of human knowledge that can be queried through natural language.
175B+
Parameters (GPT-3)
45TB
Training Data
$100M+
Training Cost
10,000+
GPUs Required

The Journey to Modern LLMs

Language AI didn't appear overnight. It evolved over decades of research, with each breakthrough building on previous work. Click on any event to learn more:

2020

GPT-3

175B params, few-shot learning, emergent abilities

Foundational Research Papers

Understanding LLMs means understanding the key papers that shaped the field. These four papers are essential reading for anyone serious about language AI:

NeurIPS 2017100K+ citations

Attention Is All You Need

Vaswani et al.

Introduced the Transformer architecture

NAACL 201880K+ citations

BERT: Pre-training of Deep Bidirectional Transformers

Devlin et al.

Bidirectional pre-training for NLU

NeurIPS 202030K+ citations

Language Models are Few-Shot Learners

Brown et al.

GPT-3: scaling + in-context learning

NeurIPS 202210K+ citations

Training language models to follow instructions (InstructGPT)

Ouyang et al.

RLHF for instruction following

What Can LLMs Do?

Modern LLMs have become surprisingly versatile. Here are some of the key applications driving the AI revolution:

💬

Conversational AI

ChatGPT, Claude, customer support bots

✍️

Content Generation

Writing, marketing copy, documentation

💻

Code Assistance

GitHub Copilot, Cursor, code review

🔍

Search & Retrieval

Semantic search, RAG systems

📊

Data Analysis

Natural language to SQL, insights

🌐

Translation

Real-time translation, localization

📚

Education

Tutoring, personalized learning

🏥

Healthcare

Medical documentation, diagnosis support

💡
Emerging Capabilities
LLMs are increasingly being used for agentic workflows — where the model plans multi-step tasks, uses tools, and executes actions autonomously. This represents a shift from "AI as assistant" to "AI as autonomous agent."

Defining Your Objectives

Before building an LLM, you need to answer some fundamental questions. Your answers will shape every decision that follows:

What problem are you solving?

  • General-purpose assistant: Broad knowledge, conversational, helpful (like ChatGPT/Claude)
  • Domain-specific expert: Deep knowledge in one area (legal, medical, finance)
  • Code generation: Optimized for programming tasks (Codex, StarCoder)
  • Research/reasoning: Complex multi-step problem solving
✅
Key Takeaways
  • LLMs emerged from decades of research, with Transformers (2017) being the key breakthrough
  • Modern LLMs can perform a wide range of language tasks with surprising capability
  • Before building, clearly define your purpose, scale, and constraints
  • The gap between open and closed models is rapidly shrinking

Ready to plan your architecture? Let's dive into the technical decisions.