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In the previous lesson, we covered how ChatGPT works at a high level. Today we go deeper into Large Language Models as a technology: what they are exactly, how their power is measured, and what distinguishes the major models in 2025.
A Large Language Model (LLM) is a massive artificial neural network trained on enormous quantities of human text. The "large" refers to the number of parameters — numerical weights inside the network — which can reach hundreds of billions or even trillions.
Parameters are the numerical weights inside the neural network — think of them as "encoded memory" storing what the model learned from training data.
| Model | Approximate Parameter Count | |-------|---------------------------| | GPT-2 (2019) | 1.5 billion | | GPT-3 (2020) | 175 billion | | GPT-4 (2023) | Undisclosed (~1 trillion estimated) | | Claude 3.5 Sonnet | Not publicly disclosed | | Llama 3.1 (open source) | 8B – 405B |
Important note: More parameters ≠ always better. Training efficiency and data quality matter equally.
LLMs learn from:
Data quality matters more than quantity — which is why AI companies invest heavily in "cleaning" data and removing harmful or biased content.
1. Text Generation Writing articles, reports, stories, emails, and marketing content in any style.
2. Translation and Localization Translating between dozens of languages with high quality and contextual understanding.
3. Programming Writing, explaining, and debugging code across dozens of programming languages.
4. Reasoning and Analysis Comparing options, analyzing documents, identifying patterns, logical thinking.
5. Summarization and Editing Condensing long documents and adapting text for different audiences or styles.
❌ Hallucination The model may "invent" information that sounds plausible and confident. The root cause: it calculates probabilities, it doesn't "know" in the way a database does. Always verify critical information from independent sources.
❌ Knowledge Cutoff The model knows nothing about events after its training cutoff date. Claude 4, for example, has a specific cutoff beyond which it has no knowledge unless it can search the web.
❌ Limited Context Window Although modern models support 128K–200K tokens, they may "lose focus" on older information in very long conversations.
| Model | Company | Key Strengths | |-------|---------|--------------| | GPT-4o | OpenAI | Most widely used, real-time voice and image | | Claude 4 | Anthropic | Deep analysis, long documents, safety | | Gemini 2.5 Pro | Google | Integrated with Google data, strong math | | Llama 3.1 | Meta | Open source, runs locally without internet |
No model is "best" at everything — the choice depends on your task, budget, and privacy needs.
The 2024–2025 generation of LLMs moved beyond text:
Claude supports images and code. GPT-4o supports real-time audio.
# Calling different LLM APIs — the pattern is similar
import anthropic
import openai
# Claude (Anthropic)
claude_client = anthropic.Anthropic(api_key="your-anthropic-key")
claude_response = claude_client.messages.create(
model="claude-opus-4-5",
max_tokens=1024,
messages=[{"role": "user", "content": "Explain LLMs in 2 sentences"}]
)
print("Claude:", claude_response.content[0].text)
# GPT-4 (OpenAI) — same concept, different SDK
openai_client = openai.OpenAI(api_key="your-openai-key")
gpt_response = openai_client.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": "Explain LLMs in 2 sentences"}]
)
print("GPT-4o:", gpt_response.choices[0].message.content)
# Key insight: different APIs, same underlying concept
# Both models tokenize your input and predict the most likely next tokens