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AI

Artificial intelligence as it actually turns up in day-to-day work: using LLMs to build faster, wiring models into real products, automating the genuinely boring parts, and being honest about where the technology falls over. For developers adding AI to what they build, and for business owners trying to work out which parts of it are real. No hype, no doom.

Frequently asked questions

What is the difference between an LLM, a chatbot and an AI agent?

A large language model is the underlying model that predicts text. A chatbot is a conversational interface wrapped around one. An AI agent goes further: it is given tools it can call, a goal, and the ability to loop, deciding what to do next based on the result of what it just did. The distinction matters because agents take actions with real consequences, so they need real guardrails.

What is an AI hallucination and how do you reduce them?

A hallucination is a confident, fluent answer that is simply wrong, because the model produces plausible text rather than retrieved fact. You reduce them by giving the model the facts instead of relying on its memory: retrieval from your own documents, tool calls to real data sources, and asking for citations you can check. You never eliminate them, so anything consequential still needs a human or an automated check.

What is RAG?

RAG, or retrieval-augmented generation, means fetching relevant documents at question time and placing them into the model’s prompt, so the answer is grounded in your content rather than the model’s training data. It is the standard way to make an LLM answer questions about material it was never trained on: your product docs, your policies, your codebase. Retrieval quality, not model choice, is usually what makes RAG good or bad.

Is it safe to put company or client data into AI tools?

It depends entirely on the tool and the plan you are on, so check rather than assume. Consumer tiers frequently train on your inputs by default; business and API tiers usually do not, but that is a terms-of-service question with a specific answer. Before anything sensitive goes near a model, know where the data is processed, how long it is retained, whether it trains the model, and whether your own contracts permit it.

Does AI-generated content hurt my SEO?

Not automatically. Google’s stated position is that it rewards helpful, people-first content however it was produced, and demotes content made mainly to game rankings. In practice, unedited model output tends to be generic, unsourced and interchangeable, which is exactly the profile that performs badly. AI as a drafting and research aid is fine; publishing at volume without expertise or editing is where the risk actually lives.

How do I stop AI API costs running away?

Treat tokens like any other metered resource: measure first, then optimise. The reliable wins are routing the easy majority of requests to a smaller model, caching repeated prompts, trimming context you are sending out of habit, and setting hard spend limits on the API key rather than trusting yourself to notice. Runaway cost is nearly always context size per request, not the number of requests.