Large Language Models (LLMs)

ELI5 (Explain Like I’m 5)

Imagine I am going to tell you a story, and I start with ‘Once upon’ and stop. You would automatically guess what comes next—‘a time.’

How are you able to do this? Because you have read many stories in your childhood that started with this sentence. 

A Large Language Model works in a similar way, but at a much larger scale. It is trained on large amounts of text and learned patterns about how words usually follow each other.

When you ask it a question, it looks at your sentence, breaks it into small pieces called tokens, and then keeps guessing the next token until it forms a full answer.

How do LLMs work?

LLMs are built using the transformer architecture. Transformers allow models to look at relationships between words (or tokens) across an entire sentence or document instead of reading left to right one word at a time.

During training, the model repeatedly:

1. Reads text.
2. Hides part of it.
3. Tries to predict what’s missing.
4. If the guess is wrong, a process called backpropagation calculates the error and adjusts internal weights to ensure a more accurate guess next time.

Over time, it learns grammar, facts, styles, and reasoning patterns.

What LLMs are good at (and where they struggle)

Strengths:

• Writing and rephrasing.
• Summarizing large amounts of text.
• Assisting with code, data analysis, and ideation.
• Answering questions from a given context.

Weaknesses:

• Getting real-time information as their training has a cut-off date.
• Hallucinations—giving convincing but factually incorrect responses when it doesn’t know something.
• Inconsistent responses when asked follow-up questions.
• They can be biased depending on the data they have been trained on.
  

Why LLMs Matter

The biggest advantage of LLMs is that they allow machines to process instructions, questions, and knowledge expressed in natural language, not rigid commands. This has led to massive productivity increases in fields such as customer service, translation, data analysis, and content creation. 

A complex SQL query can now be expressed in natural language (in multiple languages) to get the desired result. An illustration of a concept can be generated by giving instructions in spoken language, which earlier required image editing tools like Canva. 

LLMs aid in ideation, rapid prototyping, and iteration in product development. They now sit behind chat assistants, copilots, document analyzers, and agent-based systems across industries.

Their real value comes not from raw intelligence, but from how effectively they are used with tools, data, and clear instructions.