Training changes learned settings. Answering uses a saved model with a current request. Changing the information supplied to a request can change an answer while the saved model remains the same.
A museum assistant has a useful skill: turning long notices into short visitor answers. Where did that skill come from, and what changes when a new notice arrives? Follow 2 moments. Earlier, a model is prepared using examples. Later, the saved model receives a visitor's question and the
information available for that request. During training, a computer processes examples and adjusts the model's internal numerical settings. These settings help determine how input leads to output. We call them learned parameters. You can understand their role without calculating them: the training process changes
the system so that its later responses reflect patterns found across the examples. Language training can ask a model to predict missing or following text and adjust it using the examples. Further training can use demonstrations of desired answers. In a study published in March 2022,
OpenAI researchers used demonstrations and human comparisons to further train an existing language model. This was a deliberate preparation process involving collected examples and changes to the model. After preparation, a version of the model is saved. Now a visitor asks whether the museum archive is open today.
The application supplies a current notice saying the archive is closed today. The model uses the question and notice to compose an answer. Using the saved model to produce an output is called inference. Here it means answering with the prepared system.
The information supplied for this particular request is its context. That can include instructions, the question and a relevant notice. The application can replace yesterday's notice with today's notice while continuing to use the same saved model. The available evidence changes, so the
answer should reflect the new closure. We have changed what the model can consult in this request. Keep those actions separate when diagnosing a problem. If the assistant has an old notice, staff can replace that notice. If a team wants to change
the model's learned behavior through training, it needs a training process and a saved result to inspect. To check whether the learned settings changed, inspect the training record and the saved model version. The current notice still needs a date and a match to the visitor's question.
A closure for the archive cannot settle whether the cafe is open. Staff check the facility and applicable day, then compare the proposed answer with that evidence. Good language use and the right current information both matter to the visitor's journey. Try this.
A community centre replaces an old swimming timetable in its assistant's input with the current timetable. It keeps the same saved model. Which part of the process has changed, and what evidence would you need before saying the model's learned settings changed? Pause and think.
The supplied context has changed. To establish a change in learned settings, you would need evidence of a training update or a different saved model version. A different answer can follow from different information supplied to the same model. Use the worksheet to separate 2 actions: staff supply today's closure notice,
and a development team trains and saves a revised model using examples. For each, say what changes and what record you would check. Pause and write your answers. The notice action changes the current request's evidence; inspect the supplied notice and its date.
The training action changes the learned system; inspect the training record and saved version. You now have a practical explanation of learning and answering. Next we will follow the current question through the model, using plain language for each step.
Main Point
Training changes learned settings. Answering uses a saved model with a current request. Changing the information supplied to a request can change an answer while the saved model remains the same.
Key Points
Parameters are internal numerical settings adjusted during training.
Inference means using the prepared model to produce an output.
Context is the information supplied for a particular request.
A changed answer alone does not establish that training occurred.
1. Find the earlier learning process
Training processes examples and adjusts internal numerical settings, called learned parameters. Language training may use a prediction task. Further training can use demonstrations and comparisons of answers. The saved result is a model version that can be used for later requests.
2. Inspect a dated training example
Ouyang and colleagues described InstructGPT on 4 March 2022. They collected demonstrations and rankings and used them in further training of GPT-3. This is an inspectable historical example of a deliberate learned-system change. The collected examples and subsequent trained version make the preparation process inspectable. The study concerns its authors’ system and evaluation setting.
3. Use the prepared system
A visitor asks whether the archive is open today. The application supplies a notice stating that the archive is closed today. The prepared model processes the request and produces an answer; this use is inference. The notice, instructions and question are context: information available for this request. Staff can change that supplied context while keeping the saved model unchanged.
4. Check what actually changed
If an answer changes after an old timetable is replaced, inspect the context before inferring a training update. A record of the supplied timetable supports an input change. Training records and the saved version would support a learned-system change. Also check that the notice concerns the requested facility and applicable day: an archive closure says nothing about cafe opening.
5. Explain the 2 moments
Describe learning as preparation that changes the system, then answering as use of that system with current information. This distinction helps you choose a practical remedy: obtain appropriate current evidence, inspect a model version or investigate an actual training process. “Follow One Question Into An Answer” follows the answer-producing steps without requiring calculations.
6. Learning checks
M14-START02-Q01
The community centre replaces an old timetable with a current timetable in the request and keeps the same saved model. What changed? What would establish a learned-settings change?
Compare your reasoning
The supplied context changed. Evidence of a training update or different saved model version would be needed to establish a learned-settings change. A changed answer alone is compatible with a different input to the same saved model.
M14-START02-P01
For each action, identify what changes and the record to inspect: staff supply today’s closure notice; developers train and save a revised model using examples.
Write your own explanation before opening the comparison. The separate worksheet provides blank response spaces.
Compare your explanation
Supplying today’s notice changes current request evidence: inspect the notice actually supplied, its applicable day and facility. Training and saving a revised model changes the learned system: inspect the training record and exact saved model version.
Preparation and optional refresh
For more practice with examples, learned patterns and the distinction between learning and using a prepared system, How Machine Learning Learns is available. Review those explanations when the training/current-request distinction still feels unfamiliar; the calculations can wait.
Availability checked 30 October 2026.
Sources
Ouyang, Long, and colleagues, OpenAI. Training language models to follow instructions with human feedback. Published 4 March 2022 on arXiv. Sections 3.1 and 4.1 describe demonstrations, comparisons and subsequent training and evaluation. This lesson uses the preparation method, without adopting a performance result for a museum or current product. Accessed 30 October 2026.
Devlin, Jacob, and colleagues, Google AI Language. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL-HLT, June 2019; preprint version 2 dated 24 May 2019. Section 3.1 describes predicting selected text pieces in training. This historical method supports the prediction example. Accessed 30 October 2026.
Use the supplied fictional cases and templates for practice.
Practice
Write your answer, compare it with the model answer and decide what to revisit. These written checkpoints are self-assessed.
The community centre replaces an old timetable with a current timetable in the request and keeps the same saved model. What changed? What would establish a learned-settings change?
Show The Model Answer
The supplied context changed. Evidence of a training update or different saved model version would be needed to establish a learned-settings change. A changed answer alone is compatible with a different input to the same saved model.
Compare your reasoning with the model answer. These written checks are self-assessed.
For each action, identify what changes and the record to inspect: staff supply today’s closure notice; developers train and save a revised model using examples.
Write your own explanation before opening the comparison. The separate worksheet provides blank response spaces.
Show The Model Answer
Supplying today’s notice changes current request evidence: inspect the notice actually supplied, its applicable day and facility. Training and saving a revised model changes the learned system: inspect the training record and exact saved model version.
Compare your reasoning with the model answer. These written checks are self-assessed.