A Small Introduction from the Author
Hey there! This is my first blog article.
Over the past six months, I've been delving deeply into the world of AI technologies, experimenting with various models and tools. I've accumulated a lot of practical experience that I'd like to share with the community.
I'd be happy to hear your comments and questions - it's such a huge motivation for me to continue writing!
Now let's get to business...
How I First "Talked" to AI
I remember the first time I tried ChatGPT in early 2025. Before that, I was skeptical about AI in general; I thought it was just another hyped trend that would soon fade away. Yeah, I know – classic slowpoke move!
At that time, I was mostly interested in comparing technologies, constantly searching for the right tools for various tasks.
Previously, this was quite a challenge: choosing a solution meant visiting technology websites, reading documentation, searching for comparisons online, and analyzing the pros and cons. One research session could take hours.
Then I decided to give ChatGPT a try: "I need to choose a database for a high-load web application. What do you recommend?"
And you know what? This "bot" provided me with a structured list in under a minute: PostgreSQL for reliability, Redis for caching, and MongoDB for flexibility, along with pros, cons, and advice on when to use each one. What usually took me half a day of research was done in a flash!
That's when I realized this was something fundamentally new. AI didn't just replace my search engine; it became a personal analyst that could instantly perform comparative analyses of technologies.
If you're also amazed by how these systems can converse almost like humans and solve practical problems, let's explore it together — without any boring jargon or complex terms.
What is an AI Model Anyway?
AI models are essentially very advanced autocomplete systems. And that always amazes me!
You've used autocomplete on your phone or in search engines, right? You start typing "how to cook," and the system suggests "pasta," "soup," and "eggs." AI models work similarly, just on a much grander scale.
They have essentially read millions of books, articles, and websites from the internet and learned to predict what word should come next. For example, if you write "Outside it's...," your brain might automatically fill in "raining" or "snowing." AI models do the same thing but can continue beyond simple phrases.
They can write entire articles, solve math problems, generate Python code, or even compose poetry. All of this stems from an incredibly sophisticated autocomplete that "understands" context and generates coherent, meaningful text.
How Do These Models Learn?
Here's where it gets really interesting. The process of creating an AI model involves three distinct stages, each with its unique characteristics.
Stage 1: Data Collection
At this stage, the model doesn't even exist yet! Engineers gather a massive amount of textual information from the internet. We're talking terabytes of data—essentially collecting all of Wikipedia, countless books, articles, forums, and news, multiplied by thousands. This is purely a technical task of gathering and preparing "learning material."
Stage 2: Model Training
And this is where something amazing happens! The model learns in a way similar to how a child learns language. For months, on computers costing as much as a nice house, it "listens" to all this text, memorizing patterns. It learns that "good" is often followed by "morning," that people typically respond "fine" to "how are you?" and that in Python code, "def" is usually followed by the function name. Gradually, the model begins to "feel" the language and understand its structure and logic.
Stage 3: Fine-tuning
Engineers then step in and say, "Listen, you know a lot, but you need to learn how to communicate effectively with people." This is when fine-tuning occurs; the model is taught to be helpful, honest, and safe.
Conversation Memory - Context
When you open a new chat with AI, something akin to "conversation memory" is created — context. The AI remembers everything discussed within that chat.
This is very convenient:
- You can say, "As we discussed above..."
- Request clarification on a previous answer
- Ask clarifying questions
However, there's a catch — each model has a limited memory. Older models can remember about 4,000 words, while newer models can remember up to 200,000. When this limit is surpassed, older messages are "forgotten."
Practical advice: if you're engaged in a long conversation, periodically ask the AI to summarize key points. This will help preserve important information even when context is trimmed.
System Prompt - How to Explain to AI Who It Is
One of the coolest features of working with AI is the ability to "configure someone's personality" with a single phrase.
The System Prompt serves as an instruction for the model, like a "job description" for a virtual employee.
For example:`
You are a Python programmer. Answer briefly, always show code.`
And the model will actually start behaving like an experienced Python developer!
Here's a fun example:`
You are Master Yoda from Star Wars. Respond in his speech style.`
And the model will start saying: "Understand system prompts, important this is. Strong with them, the model becomes, yes!"
I constantly experiment with different prompts. For writing code I use some settings, for brainstorming - others, for checking texts - a third set. It's like having a team of specialists with different profiles.
Temperature - Controlling AI's Creativity
Another fascinating parameter you can adjust is temperature. Think of it as a creativity dial for your AI assistant.
When I first discovered this setting, it felt like unlocking a hidden superpower! Here's how it works:
Low temperature (0.1-0.3) - the model becomes very focused and predictable:
- Perfect for factual answers, code generation, or mathematical calculations
- AI will choose the most "obvious" and safe response
- Great when you need consistency and accuracy
High temperature (0.7-1.0) - the model becomes more creative and unpredictable:
- Excellent for creative writing, brainstorming, or generating diverse ideas
- AI will take more risks and produce unexpected combinations
- Sometimes gives brilliant insights, sometimes complete nonsense
I personally use low temperature when asking for code reviews or technical explanations, and high temperature when I need creative marketing ideas or want to explore different perspectives on a problem.
Most chat interfaces don't expose this setting directly, but if you're using APIs or local models, experimenting with temperature can dramatically change your results!
Where these models live and how to befriend them
Cloud services - ChatGPT, Claude, Gemini:
Pros: fast, powerful, always available
Cons: need internet, paid subscription, your data goes to the provider
Local models - can be downloaded at home:
Pros: complete privacy, works without internet, free
Cons: need powerful hardware, harder to set up
Personally, I use a combo: for experiments and learning I install local models, for serious work I use cloud ones.
If you want to try local models, I recommend checking out [Hugging Face](https://huggingface.co/models) - thousands of free models for any taste.
Zoo of modern AI models
Now there's a real zoo of AI models on the market. Each with its own character:
GPT from OpenAI - the pioneer who taught everyone to talk to AI. Very talkative, sometimes too confident.
Claude from Anthropic - my personal favorite for serious tasks. More cautious, honestly admits when it doesn't know something. Works great with code.
Gemini from Google - you might have seen it recently in Google search results, where it gives brief summaries for your queries.
DeepSeek - a relatively new player that burst onto the scene in early 2025. Attracted attention by offering high-quality AI capabilities with generous free usage limits.
By size, models are roughly divided like this:
- Small (up to 7 billion parameters) - can run on a gaming PC
- Medium (13-70 billion) - need a serious graphics card
- Giant (100+ billion) - only in data centers of large companies
My personal life hacks for working with AI
After six months of active AI use, I've developed my own principles:
1. Always check facts - especially dates, numbers, quotes
2. Experiment with prompts - different wording gives different results
3. Don't be afraid to ask again - "can you explain simpler?" works great
4. Use for brainstorming - AI generates ideas excellently
5. Remember about context - long conversation can confuse the model
Now for the sad part - limitations
When I first started working with AI, I thought it was almost magic. Then I faced reality, and it turned out to be not so rosy.
Problem #1: They live in the past
I remember asking an AI model at the beginning of this year about cryptocurrency rates. And it gives me 2024 data and with a smart look discusses "how the situation is developing". Models only know what was on the internet at the time of their training. After that - information vacuum.
Problem #2: They're not aware of the real world
AI cannot:
- Check what the weather is like outside right now
- Find out the current date (unless specifically provided)
- Send email or make a call
- Check if a website is working
They're like a genius hermit who knows a lot but can't leave the room.
Problem #3: Sometimes they lie with a smart look
This is called "hallucinations". AI can confidently tell you about a non-existent book, give a wrong formula, or make up a historical fact. And does it so convincingly that you want to believe it.
Personally, this initially annoyed me. But then I understood - it's just a feature of the technology. You need to check critically important information.
But there's good news! Today, many of these limitations can already be bypassed. There are ways to "teach" AI to get fresh information from the internet, work with real data, and even perform actions in the digital world. We'll talk about this in detail in the next article about AI agents - technology that turns a static model into an active digital assistant.
Conclusion
AI models are not magic, but an understandable technology. A very smart text prediction system that learned to imitate human speech.
Main things to remember:
- AI predicts text based on patterns from training data
- System Prompt allows "configuring the model's personality"
- Context is conversation memory with limitations
- Always verify critically important information
And in the next article we'll learn how to turn a static AI model into an active assistant...
What This Article Series Will Cover
Today we laid the foundation - figured out what AI models are and how they work. But this is just the beginning of our journey into the world of practical AI!
What's coming in the next articles:
🤖 AI Agents - turning a static model into an active assistant that can get data from the internet and perform actions
⚡ MCP Protocol - modern technology for connecting AI to any services and APIs
💻 Creating a Personal Assistant - step by step we'll build a Telegram bot that knows your preferences and solves your specific tasks
🚀 Automating Programmer Work - practical use cases of AI for eliminating routine in development
Subscribe to not miss the next articles! We'll move from theory to practice, and by the end of the series you'll have a working AI assistant adapted to your tasks.
Got questions or suggestions for topics? Write in the comments - I'd be happy to discuss!