If you are trying to get your head around artificial intelligence, the first challenge is not the technology. It is the language. Every week there is a new term. LLMs. Agents. Tokens. Models. Copilots. Workflows. Multimodal AI. Automation. It can feel like everyone else received the instruction manual and you walked into the meeting ten minutes late.
Let me simplify it. You do not need to become a software engineer to use AI well. But you do need to understand the basic language, because language shapes decisions. If you cannot describe what a tool is, you will struggle to know when to use it, when to avoid it, and when someone is dressing up hype as strategy.
What Is an LLM?
LLM stands for large language model. That sounds technical, but the practical version is simple: an LLM is an AI system trained to understand and generate language. When you type a question into ChatGPT, Claude, Gemini, or another AI assistant, you are usually interacting with an LLM. It reads your prompt, uses the context available to it, and generates a response in natural language.
Think of it less like a calculator and more like a very capable thinking partner. It can help you draft, summarise, analyse, brainstorm, compare, explain, rewrite, plan, and challenge your thinking. But it does not know things in the same way a person knows things. It can be brilliant one minute and confidently wrong the next. So the first rule is this: use an LLM to accelerate thinking, not replace judgement.
For most people starting out, this is the best first step. Use an LLM to rewrite an email. Summarise a report. Explain a concept. Turn messy notes into a plan. Ask it to critique your thinking. Ask it what you might be missing. This is where AI literacy begins.
What Is Agentic AI?
Agentic AI is the next step up. An LLM gives you an answer. An AI agent can take action. That is the key difference.
An agentic system might read an email, decide what needs to happen, search a database, update a file, draft a response, book a meeting, or trigger a workflow. It does not just respond to you. It pursues a task.
That makes it powerful. It also makes it riskier. If an AI assistant writes a poor paragraph, you can delete it. If an AI agent sends the wrong email, changes a customer record, or acts on bad instructions, the consequences are real.
Before giving an AI agent access to systems, data, customers, money, or operational workflows, you need to ask basic questions. What can it access? What can it change? Who reviews its work? Where are the approval points? What happens if it gets something wrong?
Agentic AI is not something to fear. But it is something to respect.
Which Type Do I Use For What?
Use an LLM when the work is mainly thinking, language, analysis, or communication. That includes writing, summarising, researching, comparing options, preparing agendas, drafting proposals, pressure-testing ideas, and turning vague thoughts into structure.
Use agentic AI when the work involves a sequence of steps and some level of action. That might include monitoring information, preparing reports, updating internal documents, managing repetitive workflows, or coordinating tasks across tools.
If you need help thinking, use an LLM. If you need help doing, consider an agent. But do not jump straight to agents just because they sound more advanced. In many businesses, the biggest gains will come from ordinary people using ordinary AI assistants well. Better prompts. Better questions. Better review. Better judgement.
The real advantage is not the tool. It is the capability you build around the tool.
Which Platform Is Best?
This is the question everyone asks. Should I use ChatGPT? Claude? Gemini? Copilot? DeepSeek? Which one is smartest?
Here is my position: for most people starting out, it does not matter nearly as much as you think. ChatGPT from OpenAI is the most well known and has the largest user base. Claude from Anthropic is exceptionally strong at writing, reasoning, and working through complex documents. Google's Gemini is tightly integrated with the Google ecosystem. Microsoft Copilot is embedded across the Office suite. DeepSeek has emerged as a serious open-source contender out of China. There are others gaining ground every quarter.
The major platforms are evolving so quickly that any fixed ranking becomes stale almost immediately. One might be better at writing this month. Another might be better at coding. Another might be stronger with long documents, images, or research. Then a few weeks later, the order changes again. They leapfrog each other constantly.
So do not let platform selection become a sophisticated excuse for inaction. Pick one. Learn it properly. Use it every day for real work. Build the habit. Learn how to ask better questions. Learn how to check the output. Learn where it helps and where it falls short.
If you later decide another platform is better, move. Or use more than one. The switching cost is far lower than the cost of standing still.
The businesses that win with AI will not be the ones who perfectly picked the best platform in 2026. They will be the ones who built AI literacy early, experimented often, and learned how to turn these tools into better decisions, faster execution, and stronger commercial outcomes.
My Position
Do not start with the most complicated version of AI. Start with the language. Understand what an LLM is. Understand what an agent is. Use the right tool for the right level of risk. Do not obsess over which platform is best.
The best AI platform is the one you actually use, understand, and can govern. Start there. The rest will move quickly enough.
If you are really stuck and want help, reach out.
Chi va piano, va sano e va lontano.
He who goes slowly, goes safely and goes far.