Getting better outputs from AI tools through better understanding

What a large language model is actually doing

At base level, a large language model (LLM) is an engine that completes word patterns. It has absorbed vast amounts of text and learned the probable relationships between words. When given a prompt, it encodes that input, predicts the most likely next token, and repeats the process until it reaches a stopping condition built into its code.

This matters because an LLM does not think the way a person does. It generates a probable continuation of what has been asked for, rather than reasoning from genuine understanding. That explains why it can produce fluent text so quickly, and it also explains its shortcomings: an odd or wrong response is usually the model following a statistical pattern toward a completion that turned out to be inappropriate for the actual question.

Tokens and the context window

Tokens are typically short chunks of text, around three or four characters, sometimes shorter than a whole word. A model can only process a fixed number of tokens at once, known as the context window. Anything that falls outside this window is effectively forgotten by the model as the conversation continues.

This becomes obvious in long conversations or long documents, where the model appears to lose track of earlier content. A bigger context window sounds like the obvious fix, but it is not always better: larger windows can increase cost, and they can even degrade performance as the model struggles to identify which of thousands of tokens actually matter to the current question.

• Tokens, not words, are the unit a model processes and pays for
• Content outside the context window is not remembered, even within the same session
• A larger context window trades cost and, sometimes, accuracy for more retained content

Training data, knowledge gaps and retrieval

An LLM learns from the data it is trained on, such as web pages, books, academic papers and code repositories. It adopts the patterns in that material but does not remember the source text as a person would remember a document. Its knowledge stops at its last training date, and its coverage across topics is uneven. This is the part most often forgotten, and it is a frequent source of frustration.

Retrieval-augmented generation (RAG) works around this by injecting up-to-date or domain-specific information directly into the prompt. Pointing a model toward relevant content in this way markedly improves the output, because the model is superb at recognising and following patterns but cannot refer to source material outside its training unless that material is supplied to it.

Hallucination and how to reduce it

When people do not know an answer, they sometimes bluff. LLMs do the same. Given a weak prompt, a model will often fill the gap with content that is acceptable in form but false in substance, a behaviour known as hallucination.

Being more specific in a prompt is the main defence: breaking a request down and being clear about what is actually needed reduces the room for guesswork. It also helps to explicitly ask the model to say when it does not know or is not sure, which reduces (though does not eliminate) the chance of confident falsehoods. If hallucination is suspected in any output, it must be fact-checked closely and edited before use; an LLM saves no time at all if what it produces is fundamentally wrong.

• Break a broad request into smaller, specific instructions
• State the format, tone and success criteria explicitly
• Ask the model to flag uncertainty rather than guess
• Fact-check any statistic, name, date or claim before relying on it

Bias in training data

Humans are inherently biased, and that bias is reflected across the internet and in all the sources on which LLMs are trained. Training data reflects the most common demographics and viewpoints online, which explains why prevalent perspectives often dominate an output, for example through the over-representation of Western contexts.

This can be managed, though not solved, by varying prompts and comparing the outputs produced to see where better wording achieves a more balanced result. Treating a single output as neutral or authoritative on a contested topic is a mistake worth avoiding.

Privacy and data handling

The general rule is to treat an LLM like any other external API and avoid sending personal data unless the agreement in place explicitly allows it. For anything confidential, any privacy features available should be used. It is easy to treat an AI tool as a co-worker, since it talks back conversationally, but data processing should be kept within an organisation's own environment wherever possible, backed by clear staff policies for its use. In an IT support context, the same discipline applies to any tool with access to a device or session, whether that is an AI assistant or a secure remote support platform such as 247connect used to view a screen: access and data flow should be governed, not assumed.