LLM101: A Simple Guide to Large Language Models

Artificial Intelligence can feel complex from the outside.

ChatGPT. Claude. Microsoft Copilot. Gemini.

Different names. Different interfaces. Different strengths.

But underneath, they share a common foundation. They are all built around Large Language Models, or LLMs.

At psyborg®, we see LLMs as a new creative and operational layer for business. Not magic. Not a replacement for thinking. More like a reasoning, writing and production partner that can help people move from idea to output faster.

Part mind. Part machine.

What is an LLM?

An LLM is a Large Language Model.

It’s a type of AI system trained to understand and generate language. It works by reading your input, interpreting the context and predicting what should come next.

That sounds simple, but the scale is what makes it powerful.

LLMs are trained across enormous collections of documented human knowledge, such as books, websites, articles, code, transcripts and other text-based material. This training helps them recognise patterns in language, ideas, logic, structure and meaning.

They don’t “know” things like a person does. They predict likely responses based on patterns they’ve learned.

To learn more about how large language models work, see Microsoft’s introduction to prompt engineering for GPT models: https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/prompt-engineering

Simple example

If you type:

The sky is…

An LLM might predict:

blue.

If you type:

Write a professional email to a client explaining why SEO is a long-term investment…

The LLM uses the same underlying skill, but with more context. It predicts a structured, relevant and useful response based on your instruction.

That’s why the quality of your input matters.

Better context usually means better output.

What does an LLM do?

A useful way to understand an LLM is this:

It’s an advanced prediction engine built on documented knowledge.

It can help you:

For example, Microsoft Copilot can work inside tools like Word, Excel, Outlook and Teams to help summarise meetings, draft emails and work with business documents.

Claude is often strong for long-form reasoning, document analysis and structured thinking.

ChatGPT is flexible across writing, analysis, images, code and everyday problem solving.

Gemini connects strongly into the Google ecosystem and is known for large context capabilities in certain models.

The tool matters, but the method matters more.

The common theme: context

The real skill in using LLMs is not just “prompting”.

It’s context.

An LLM can only respond to what it has access to in the moment.

That includes:

This is why one-line prompts often produce generic results.

The model isn’t being lazy. It’s under-briefed.

What is a context window?

The context window is the amount of information an AI model can consider at once.

This includes:

Think of the context window like the AI’s working memory.

If the conversation or source material gets too large, the model may start losing access to earlier details or compressing them. This is why long-running chats can slowly drift, forget earlier instructions or become less precise.

To learn more about long context, see Google’s Gemini guide to long context: https://ai.google.dev/gemini-api/docs/long-context

Approximate context window examples

These limits change often and depend on the model, app, plan and whether you’re using the public chat interface or API.

The key lesson is simple.

Bigger context helps, but it doesn’t remove the need for clear instructions.

What is a token?

A token is a small piece of text that an AI model reads.

A token might be:

A rough rule is:

1 token is about three-quarters of an English word.

Or said another way:

100 tokens is about 75 words.

This matters because tokens affect:

For most business users, you don’t need to count tokens manually.

You just need to understand that AI has a working memory limit.

To learn more about tokens and token limits, see OpenAI’s guide:
https://help.openai.com/en/articles/4936856-what-are-tokens-and-how-to-count-them

You can also experiment with tokenisation using OpenAI’s tokenizer:
https://platform.openai.com/tokenizer

What is a good chat structure?

A good chat is not a random question.

It’s a brief.

At psyborg®, we often think of a strong prompt like this:

Microsoft describes strong Copilot prompts as having a goal, context, expectations and source.

To learn more about this structure, see Microsoft’s guide to writing prompts in Microsoft 365 Copilot:
https://support.microsoft.com/en-us/microsoft-365-copilot/get-started-writing-prompts-in-microsoft-365-copilot

Example prompt

Act as a brand strategist helping a small engineering consultancy adopt AI safely.

The audience is a team of directors who are curious but cautious.

Explain three practical AI use cases for Microsoft Copilot in their business.

Focus on document summarisation, tender review and internal knowledge capture.

Use a table with benefits, risks and first steps.

Keep the language clear, practical and non-technical.

That is much stronger than:

How can we use AI?

Why?

Because it gives the model a job, a business context, an audience, a structure and a useful output format.

Prompt engineering essentials

Prompt engineering is the practice of designing better instructions for AI.

But don’t overcomplicate it.

For most business users, good prompting comes down to clarity.

1. Be specific

Weak prompt:

Write something about AI.

Better prompt:

Write a 600-word blog introduction for Australian small business owners explaining how AI can help with admin, marketing and internal processes.

2. Give context

Weak prompt:

Write a proposal.

Better prompt:

Write a proposal for a Newcastle-based HR provider that needs help clarifying its brand & improving SEO.

3. Define the audience

A prompt for a CEO should be different to a prompt for a junior team member.

Tell the model who it’s speaking to.

4. Ask for a format

Use tables, headings, checklists, summaries or step-by-step frameworks.

AI performs better when the output shape is clear.

5. Provide examples

If you like a certain tone, structure or style, paste in an example.

For brand work, this is critical.

The model needs to learn what “sounds like us” means.

6. Iterate

The first answer is rarely the final answer.

Follow up with:

Make this more concise.
Make it more strategic.
Make it sound more human.
Turn this into a checklist.
Challenge the assumptions.
Give me three stronger versions.

AI works best as a conversation, not a vending machine.

Multimodal prompts

Modern LLMs are no longer limited to text.

Many AI platforms can now work with:

This is called multimodal AI.

To learn more about multimodal capabilities in Gemini, see Google’s Gemini API documentation:
https://ai.google.dev/gemini-api/docs

To learn more about multimodal work in ChatGPT, see OpenAI’s ChatGPT release notes and product updates:
https://help.openai.com/en/articles/6825453-chatgpt-release-notes

Example

You can upload a screenshot of a website and ask:

Review this homepage from a brand, SEO and conversion perspective.
Tell me what is working, what is unclear and what you would improve first.

Or you can upload a meeting transcript and ask:

Summarise the key decisions, risks, follow-up tasks and unanswered questions.

This is where AI becomes less like a chatbot and more like a thinking layer across your business material.

The platforms are different, but the principles are the same

ChatGPT, Claude, Copilot and Gemini each have different strengths.

But the core principles remain the same.

Give the model better context.

Ask better questions.

Use clearer structure.

Review the output.

Apply human judgement.

This is just the beginning

LLMs are the foundation layer.

Around them, new AI capabilities are rapidly taking shape:

To learn more about OpenAI’s approach to agents and tools, see OpenAI’s agents documentation:
https://platform.openai.com/docs/guides/agents

To learn more about Claude and computer-use style workflows, see Anthropic’s Claude documentation:
https://docs.anthropic.com/en/docs/agents-and-tools

This is why AI is not just a software trend.

It’s a shift in how work is briefed, structured, produced and improved.

The psyborg® perspective

At psyborg®, we’ve been working with AI for over three years across branding, content, websites, strategy, workshops and business systems.

What we’ve learned is this:

AI doesn’t remove the need for human thinking.

It increases the value of it.

The businesses that benefit most from AI are not the ones chasing every new tool. They are the ones that understand their workflows, their customers, their brand voice and their decision-making process.

AI needs direction.

It needs intent.

It needs context.

That’s where strategy comes in.

Where to start

If you’re new to AI, start small.

Pick one task.

Improve one workflow.

Build one prompt.

Create one useful template.

Then repeat.

Good AI adoption is not about replacing people. It’s about helping people think, write, decide and produce with more clarity.

Creativity, fuelled by logic.

Explore psyborg® AI tools

If you want to build your own practical AI skills, explore the psyborg® AI Workbooks:

https://www.psyborg.com.au/product-category/ai-tools/

These workbooks are designed to help individuals, families and business users better understand how to use AI with structure, creativity and confidence.

Go deeper with AI

For business users who want to explore AI Strategy Sessions, workshops and guided adoption, visit:

https://www.psyborg.com.au/go-deeper-with-ai

AI is moving quickly.

The best time to understand it is now.

Part mind. Part machine.

Read more: Who is your client avatar? >>

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