What is an LLM?

Client :

Everyone is talking about LLMs right now. ChatGPT, Claude, Gemini… What is an LLM exactly? Is it just an improved chatbot?

Me :

An LLM (Large Language Model) is an AI model trained to predict the next token in a sequence.

That objective sounds simple, but at scale it leads to powerful behavior. By learning from massive text and code corpora, the model captures useful language patterns: structure, style, syntax, and common reasoning paths.

In practical terms, an LLM can:

• answer technical questions at different depth levels,
• summarize long content,
• draft emails, plans, scripts, and code,
• transform content from one format to another.

What an LLM is not

An LLM is not a guaranteed source of truth. It does not store facts like a database does. It generates the most likely continuation based on training data and prompt context.

That is why it may:

• hallucinate facts,
• miss business nuance,
• sound confident while being wrong.

Why companies adopt LLMs

The biggest value is cognitive acceleration:

• faster writing and synthesis,
• more consistent responses,
• lower repetitive workload across support, product, ops, and sales.

The goal is usually augmentation, not replacement.

How to get better outputs

LLMs perform much better when you provide:

1. a clear role,
2. reliable context,
3. an explicit output format.

The sharper the context, the more usable the answer.

An example

Consider a B2B company where sales teams repeatedly answer the same prospect questions about security, onboarding, and integration.

Without a specialized LLM, each response is written manually, which creates:

• slower turnaround,
• inconsistent quality across team members,
• avoidable messaging risk.

With an LLM connected to approved internal materials (offer docs, policies, support knowledge), teams generate a solid first draft in seconds.
People still validate before sending, but the workload drops significantly.

I recommend starting simple

For non-technical leadership teams, a pragmatic rollout is:

1. pick one high-frequency use case with clear ROI,
2. define simple KPIs (time saved, reuse rate, user satisfaction),
3. run a 4-6 week pilot with a small perimeter,
4. scale only if outcomes are measurable.

This keeps the project grounded in business impact instead of technical hype.