A large language model turns a sequence of input into a sequence of predicted output. Understanding a few parts of that process helps business teams decide where a model can assist an ERP workflow and where a source record, calculation or explicit rule must establish the result.
Tokens, parameters and attention
Text is divided into tokens, which may represent words, parts of words or individual characters. The model processes numerical representations of those tokens. Its learned parameters shape the relationships it uses when producing an answer.
Many large language models use the transformer architecture. Its attention mechanism helps the model weigh relationships between elements of the input. Google’s introduction to large language models explains tokens, transformers and the role of self-attention. These mechanisms support useful language behavior, but do not make every generated statement correct.
Training and inference do different jobs
Training adjusts model parameters using examples. Inference uses the trained model to produce a response from the supplied input. A normal business question does not automatically retrain the model or synchronize its knowledge with your ERP.
Consider a question about a purchase order’s outstanding quantity. The model may help interpret the request and explain a retrieved record. The current quantity must still come from an authorized business source. A fluent explanation cannot establish that stock was received or that an invoice was posted.
"In the past, we managed software; today, we design autonomous processes that AI executes for us. The future belongs to those who automate not just the tasks, but the decision-making itself."
Retrieval adds context
A retrieval step searches external material and supplies relevant results to the application. OpenAI’s retrieval guide describes semantic search over indexed data. This is a different operation from changing the model’s parameters.
For a purchasing assistant, useful context could include an approved procurement policy and the specific order records the user can access. Preserve document versions, effective dates and record identifiers. Plan a clear response when no relevant source is available. Retrieved text also needs checking for relevance and consistency with the question.
Turn model output into a verifiable result
Keep calculations and record updates in controlled application code. Validate identifiers and required fields, reconcile amounts against the system of record, and show source references for factual answers. If the model produces a suggested classification, test it against examples that the business has already reviewed.
Start with a task whose outcome can be checked: extracting a reference number, summarizing a ticket or drafting a response from approved guidance. Measure errors and review effort before widening its responsibilities. Explore GreatWork’s data services and artificial intelligence services when planning the information sources and application controls needed around the model.
I look forward to seeing how these developments will improve service levels and customer satisfaction in the freight industry!