LLM and Transformer Foundations for Practical AI Users
Lesson 2: The Pipeline Mental Model
Lesson Objectives
By the end of this lesson, students should be able to:
- Describe preprocessing, model prediction, and postprocessing.
- Explain why output formatting is not the same as truth.
- Use pipeline thinking to debug weak AI results.
Lesson Content
A pipeline is a practical way to understand many AI tools. First, the system prepares the input. Text may be split into tokens. Images may be resized. Audio may be converted into features. Second, the model produces a prediction or generated output. Third, the system formats the result into something a person can read or use.
Pipeline thinking helps students troubleshoot. If the input is messy, preprocessing may lose important details. If the model is mismatched to the task, the output may be unreliable. If postprocessing is weak, the result may look confusing even when the model found something useful.
For example, a question-answering workflow should answer from supplied context. If it gives an answer not present in the context, the workflow failed its job even if the sentence sounds polished.
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