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                LLM and Transformer Foundations for Practical AI Users
                
                    Lesson 3: Encoder, Decoder, and Sequence-to-Sequence Tasks                

                Lesson Objectives
By the end of this lesson, students should be able to:
Explain model-family differences without heavy math.
Match understanding tasks, generation tasks, and conversion tasks to practical model behavior.
Avoid assuming every model is designed for every job.
Lesson Content
Modern language models are not all built for the same kind of work. A practical way to group them is by the direction of information flow.
Some models are strongest at understanding a whole piece of text, such as classification, entity extraction, or answering from a known passage. Some are strongest at generating the next part of text, such as drafting, continuing, coding, or conversation. Some combine understanding and generation for tasks like translation, summarization, or converting one kind of text into another.
Students do not need to memorize architecture names to use this idea. The key question is: "Am I asking the AI to understand, generate, or transform?" That question helps you choose the right tool and write better review checks.
        
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