# @the.datascience.gal on Instagram

- **Type:** Image
- **Original URL:** https://www.instagram.com/p/DcyKbN1k00D
- **Gondola URL:** https://gondola.cc/posts/69887534-thedatasciencegal-instagram
- **Thumbnail:** https://img.gondola.cc/tr:w-,h-,fo-auto/postThumbnails/141b96b6a4.jpg
- **Posted:** 2026-09-02T11:46:51.000+00:00
- **Account Owner:** Aishwarya Srinivasan | Data & AI | LinkedIn Top Voice (@the.datascience.gal) — https://gondola.cc/the.datascience.gal

## Caption

How LLMs Are Actually Built

You use ChatGPT, Claude and Gemini every day.

But do you actually know what happens before an AI model can answer your questions?

LLMs are generally built through three major stages:

1. Pre-training
The model processes massive amounts of text and learns patterns by predicting what comes next. This is where it develops its broad knowledge and language capabilities.

2. Post-training
The base model is taught to follow instructions and produce more useful responses. Techniques like supervised fine-tuning and LoRA can be used here.

3. Alignment
The model learns preferences, boundaries and behaviors that make its responses safer and more helpful. Methods include RLHF, DPO and other preference or reward-based approaches.

The easiest way to remember it:

Pre-training = Learn
Post-training = Improve
Alignment = Behave

And the next time an AI model gives you a weird answer, ask yourself:

Is it missing knowledge?
Is the behavior wrong?
Or is it failing to follow the intended preferences?

The stage often tells you where the problem actually comes from.

Comment AI and I’ll send you the full explanation with examples.

#AI #LLM #MachineLearning #ArtificialIntelligence #ChatGPT

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## Tags

llm, machinelearning, ai, artificialintelligence, chatgpt

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