What is an LLM (Large Language Model)?
- The foundational technology powering most Generative AI applications today, enabling them to create text, code, and other content based on patterns learned from vast datasets.
- While other types of models (like image generators) also fall under "Generative AI," LLMs are the specific engine behind text-based generative tasks, which is often what people mean when they discuss the field broadly.
What it does?
- An LLM is an AI that generates text by predicting the next words in a sequence.
- It takes an input (a prompt) and produces an output (text).
- That output can serve different purposes, such as:
- Answering a question
- Suggesting an action (e.g., “What should I do next?”)
- Continuing a conversation
⚠️ Warning ⚠️
The LLM doesn’t understand or execute these outputs—it only generates text that looks like a helpful response based on patterns it learned during training.
LLMs are powerful statistical programs—pattern matchers on a massive scale.
- They've been trained on enormous amounts of data from the internet and other sources.
- Training involves analyzing patterns in text.
- When given an input sequence, an LLM predicts the most likely text to come next.
Honestly I often anthropomorphize LLMs, treating them as if they have human-like intentions or memory 😅. While they can simulate conversation impressively, it's important to stay grounded:
- LLMs are not sentient. They don't have motivations or understanding.
- Their apparent intelligence is emergent. The astonishing capability is a byproduct of s**cale (trillions of parameters).
- Memory is an illusion. LLMs are stateless by default—each interaction is fresh, with no knowledge of previous exchanges.**
You can listen to a nice podcast here about this exact concept.
GPT vs. ChatGPT
Many people confuse the model with the product. Here's the difference:
| GPT (The Model) | ChatGPT (The Product) |
|---|---|
| A Large Language Model | A software product built by OpenAI |
| The statistical engine | Uses the GPT model as a component |
| Stateless: no memory of past inputs | Has memory, search capabilities, and other features |
| Takes an input sequence, outputs text | Wraps the model in a user-friendly interface |
What is an API? And Why OpenRouter?
- It stands for Application Programming Interface.
- A way to connect different applications and services together using common standards.
- And here we are gonna use OpenRouter API which unifies all different models for you!
What is an AI Agent?
The term "AI Agent" is kinda to me means when I ask some model to do something for me on itself if I grant it the permissions it might need to have.
But if you like IBM's definition it is "a system that autonomously performs tasks by designing workflows with available tools". But this was what they said in 2024 which in this domain can be called stone age already 🥲.
And this is what Google defines it:
AI agents are software systems that use AI to pursue goals and complete tasks on behalf of users. They show reasoning, planning, and memory and have a level of autonomy to make decisions, learn, and adapt.
— Ref
So I guess you could think of it like this: an AI Agent is an LLM that runs tools in a loop to achieve a goal.
This means:
- The agent has a goal (e.g., "Fix the XYZ bug and open an MR for it in Gitlab").
- It can use tools (e.g., Gitlab API, databases, internet).
- It runs in a loop, deciding which tool to use next based on previous results until the goal is achieved.
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