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

We explain how large language models (LLMs) work, what they’re good at, and where they can fail. You’ll learn what prompts are, why hallucinations happen, and how context helps. This sets the foundation for building AI assistants that understand queries and carry on meaningful conversations.

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.

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