Artificial Intelligence 101
New to AI? This guide gives small businesses and nonprofits a clear starting point, explaining what AI is, how it works, and what to expect as you begin using it.
What
Exactly
Is
AI?
AI (Artificial Intelligence) is a broad term for computer systems that can perform tasks we usually associate with human intelligence, like understanding language, recognizing patterns, making predictions, or generating content.
Most AI you hear about today falls into a few related categories. Machine learning is a subset of AI where systems learn patterns from data rather than relying only on fixed rules. Deep learning is a subset of machine learning that uses layered neural networks to learn more complex patterns, which is why it is widely used for modern language tools, image recognition, and speech recognition.
This matters for one simple reason: when someone says “AI,” they might be referring to very different tools. Some AI is built to classify and detect patterns (fraud detection, spam filtering). Some AI is built to recommend what you might like next (music, videos, products). And some AI is built to work with language (summarizing, drafting, rewriting, organizing information). If you do not define which kind of AI you are talking about, it becomes easy to expect the wrong outcomes or choose the wrong solution.
What
Is ChatGPT?
ChatGPT is an AI assistant built on a Large Language Model (LLM). LLMs are trained on large amounts of text and learn the patterns of language. When you ask a question, the system generates an answer by predicting what text is most likely to come next based on your input and the conversation so far.
AI You Have Probably Heard About
Many of the AI names you hear are separate tools designed for different purposes. Some are all-purpose assistants for writing and summarizing, while others are built into workplace software or specialize in images, video, or coding.
ChatGPT
This model is considered the industry standard for general-purpose AI. It is known for high-level reasoning, multi-modal capabilities (text, image, audio), and speed.
Copilot
This model is extensively used in enterprise environments and integrated with Windows and Office.
Gemini
This model is popular for its versatility in handling text, image, and video, and its massive context window.
Claude
This model is highly regarded for coding, complex reasoning, and nuance. Claude 3.5 Sonnet is noted for being exceptionally fast and intelligent.
Grok
This model is integrated directly into the X (formerly Twitter) platform, offering real-time data access and strong reasoning/math capabilities.
DeepSeek
This model is popular for its strong performance in coding and mathematics, particularly the DeepSeek-R1 model.
Think of an LLM as very advanced autocomplete.
It does not “think” like a person.
It generates language that fits the patterns it learned
How
ChatGPT
Learns
Stage 1: Pretraining
The model is exposed to a massive amount of text and learns to predict the next token (a chunk of text) from context. Over time, it learns grammar, writing style patterns, and broad language behavior.
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Stage 2: Fine-Tuning (With Human Feedback)
After pretraining, models are often refined using more targeted examples and human feedback so they follow instructions better, behave more like an assistant, and reduce undesirable outputs.
What Happens When You Ask It Something
When you submit a message, the model breaks your input into tokens, which are small chunks of text. It then generates a response token by token, using probabilities learned during training, guided by the conversation context. That is why the same request can produce slightly different phrasing across attempts, and why longer conversations can drift unless you restate what you want and what constraints matter.
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The main operational takeaway is this: the quality of the output is strongly influenced by the quality of the input. Clear instructions, context, and constraints tend to produce more reliable results.
This is why AI can feel impressive in conversation.
But it’s also why it can be wrong.
AI Hallucinations
A hallucination is when AI produces information that sounds convincing but is not true. It happens because the model is optimizing for a coherent response, not verified accuracy.
What AI Is Good At
AI performs best when you treat it like a drafting and organizing engine:
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Getting to a strong first draft faster (emails, SOPs, policies, outlines)
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Summarizing long content into key points and action items
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Turning messy notes into checklists, templates, and structured docs
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Creating options (subject lines, talking points, variations)
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Standardizing internal language so work is more consistent
Where AI Can Mislead You
AI can cause problems when people treat it like a fact database:
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Confident mistakes: plausible answers that are wrong
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Invented details: names, timelines, “quotes,” or fake references
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Outdated assumptions: models may not reflect the latest changes
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Ambiguity: if your question is vague, it will guess what you meant
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Tone issues: output may not match your brand or values
FAQs
Is AI the Same as Automation?
Not exactly. Automation follows rules to execute steps. AI helps with tasks involving language and pattern recognition. The best results usually combine both.
Does ChatGPT Search the Internet for Answers?
​Not by default. It generates language from learned patterns. Some tools add browsing or internal document retrieval, but do not assume it is citing current sources unless you specifically configure it.
Why Does AI Sound Confident But Can Still Be Wrong?
AI is optimized to produce coherent language. Verification and review are how you keep AI useful without turning it into a risk.
Will AI Replace Jobs?
Most SMBs benefit when AI removes low-value busywork and improves consistency. The most successful teams treat AI like an assistant, not a decision-maker.
What Is the Best First AI Policy?
Start with three rules:
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Do not paste sensitive data
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Require human review for external-facing work
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Define approved tools and approved use cases