Understanding How AI Works & Working Effectively with the Machine
Understanding How AI Works
By Daniel Forsyth, Dataforge Canada
AI is everywhere—your phone, your car, your coffee maker (probably). But what's actually happening when ChatGPT writes you a poem or your phone recognizes your face? How do I use it effectively?
Let me break it down.
Pattern Matching Plus Reasoning
AI is fundamentally based on finding patterns in massive amounts of data. But in 2026, modern AI systems have evolved beyond simple pattern recognition into genuine reasoning capabilities.
Think of it like this. You've seen thousands of dogs in your life. Show you a picture of a weird-looking poodle you've never seen before, and you'll still recognize it's a dog. You learned the pattern—four legs, tail, fur, certain face shape—even though nobody sat you down with a "dog manual."
AI works on the same principle, except it needs millions of examples instead of thousands, and it's looking for mathematical patterns instead of visual ones. But the latest AI systems—particularly reasoning models like OpenAI's o3 and DeepSeek-R1—can now think through problems step-by-step, verify their own work, and demonstrate genuine logical reasoning rather than just matching patterns.
The Training Process
Systems like ChatGPT learn by reading massive amounts of text from the internet—books, articles, websites, conversations. The training process is simpler than you'd think: the AI learns to predict what comes next.
Technically, it works with "tokens" (pieces of words or punctuation), but think of it like this: show the AI "The cat sat on the..." and it learns that "mat" or "chair" are likely to come next, while "refrigerator" is less likely. Do this billions of times with billions of text examples, and the system gets remarkably good at predicting plausible sequences.
Here's the key thing: nobody is labeling this data with "correct answers." The AI is just learning patterns from raw text. When you ask ChatGPT a question, it's not looking up answers in a database. It's generating a response by predicting what words should come next based on all those patterns it learned.
That's why it can sound confident even when it's wrong—it's just predicting plausible-sounding text based on what it learned during training.
The Context Window: AI's Working Memory
AI only knows what it was trained on—period. It doesn't have access to your files, your company data, or current information unless you provide it.
This is where the "context window" comes in. Think of it as AI's working memory for a single conversation. Everything you tell the AI in a conversation stays in this window, and the AI can refer back to it. Modern systems have context windows that can hold hundreds of pages of text.
Here's the practical implication: if you want AI to work with your data, you need to either put that data into the context window (by pasting it, uploading files, or describing it), or ask the AI to search for current information first. The AI can only work with what's in front of it at that moment.
This is why AI can't just "know" about your business or access your systems without explicit connection. You're either feeding it information directly, or you're using tools that connect AI to your data sources.
Working Effectively with AI
After working with AI tools in real projects, here are some practical tips:
Think of AI as the worker, you as the guide. AI can handle both large and small functions—sometimes brilliantly, sometimes poorly. It can get a huge amount of work done, but it needs to be worked with like a tool wielded by an operator. You provide the direction, judgment, and verification.
Provide context first. Since AI only knows what it was trained on, you need to give it your specific information. Upload relevant documents, paste data into the conversation, or ask it to search for current information before you ask it to analyze or generate anything. The AI can only work with what's in its context window.
Plan before execution. Before asking AI to execute something complex, develop a clear plan first. A useful tactic is to have the AI help you build a document outlining your plan—then work with it to refine and adjust that plan until it's solid. This is where you catch misunderstandings and align on approach. That said, for well-defined technical problems—like debugging code or restructuring a program—AI can often develop effective strategies on its own because these domains have clear rules and verifiable outcomes.
Let AI do what it's good at. Once you have a solid plan, AI is often excellent at execution. Writing code from a clear spec, processing data according to defined rules, generating content from a detailed outline, refactoring entire programs—these are where AI shines. In structured domains like programming, AI can genuinely "understand" complex systems and work with them intelligently.
Use AI for summarization. AI is effective at summarizing code, documents, datasets, and other information. This capability can save time.
Iterate for better results. Don't expect perfect output on the first try. Running AI over the same material multiple times—refining, asking it to improve specific aspects, or approaching from different angles—often yields better results than a single pass.
The Bottom Line
AI in 2026 combines pattern matching with reasoning capabilities. It's useful for specific tasks—particularly in coding, data analysis, and content generation. It's not magic, it's not conscious, and it won't replace human judgment anytime soon.
The key is understanding what it actually does: AI works from patterns learned during training, plus whatever information you provide in the current conversation. It can reason through problems, but it doesn't truly "understand" in the human sense. It only knows what it was trained on unless you give it current information.
When someone tries to sell you an AI solution, ask them: "What patterns is it finding, how will we provide the specific context it needs, and how will we verify its outputs?" If they can't answer that clearly, be cautious.
The companies getting value from AI in 2026 are treating it as a tool with specific strengths and limitations, rather than believing unrealistic claims.
How Dataforge Canada Can Help
We work with businesses to implement AI solutions when they make sense. This means understanding what AI can do, and knowing how to integrate AI with your existing systems and data.
If you're evaluating AI tools for your operation, we can help you assess what will work for your specific situation.
Contact Dataforge Canada:
- Web: https://dataforgecanada.com
- Serving Burlington, Hamilton, and the Greater Toronto Area since 1994
Read this article online:
https://dataforgecanada.com/blog/understanding-how-ai-works-working-effectively-with-the-machine
About the Author:
Daniel Forsyth is the founder of Dataforge Canada, providing IT solutions since 1994. With experience across multiple industries including transportation, manufacturing, and enterprise systems, he focuses on practical implementation of technology.
Disclaimer:
This article represents the author's professional opinion based on industry experience. Technology capabilities evolve rapidly—always evaluate current solutions against your specific requirements.
References
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