What is an LLM and How to Add AI to Your Product Without Code

What is an LLM?

LLM stands for Large Language Model — a powerful AI system trained on a massive amount of text data. The most well-known models are ChatGPT, Claude, GigaChat, YandexGPT, DeepSeek, and Qwen. Some of them are open-source, and you can even run them on your own server.

For a user, an LLM is a black box:

You give it text — and get a meaningful output: a translation, a summary, or even code.

The key concept here is the prompt — that’s what you feed into the model.

For example:

“Translate this text into Spanish" or “Write a Telegram bot in Python.”

It’s important to understand:

• An LLM doesn’t know facts.
• It doesn’t search Wikipedia.
• It just continues text based on probability.
  

That’s why sometimes it hallucinates — confidently making things up.

Where Are LLMs Useful?

Here are some common and powerful use cases:

• Customer support
• Internal assistants
• Feedback processing
• Report and documentation generation
• Content translation and adaptation
  

The most important part:

→ Formulate your prompt clearly and thoughtfully.

Hallucinations: Why Do They Happen?

Let’s take an example. You ask:

“What books did Shakespeare write in the 20th century?”

The model might reply:

“In 1923, Shakespeare published The Queen’s Tragedy…”

Sounds convincing — but it’s a complete fabrication. Shakespeare died in the 17th century.

These confident but false answers are called hallucinations.

To solve this problem, we use the RAG approach — Retrieval-Augmented Generation — where we feed the model real, factual data.

More on that in Lesson 2.

Your First Practical Project

Let’s build a simple app on Directual: An assistant that recommends movies.

The user enters a description or preference, and the model suggests 3 suitable movies We’ll use the ChatGPT API, with an API key from your OpenAI account.

We’ll set up:

• A database table for requests
• A UI form and a list of responses
• Two backend scenarios:
  • One to save the user request
  • Another to send it to the LLM and save the model’s reply
    

The response is saved to the database and instantly shown in the interface.

→ In the end, you’ll have a working AI-powered service that responds to real users.

You can download the app snapshot from this lesson.

⚠️ After importing, you’ll need to manually install all the required plugins!

What’s Next?

Congratulations — you’ve taken your first step!

You now know:

• What an LLM is
• How to work with it
• And how to integrate it into your own web app — without writing code
  

But things are about to get even more interesting.

In Lesson 2, we’ll learn how to build a smarter AI — one that replies based on your actual data, not just guesses.

We’ll explore:

• Vector databases
• Embeddings
• Semantic search
  

… and build all of that with your own hands.