Year 10 - Computing
Chatbot applications and other LLMs
Using data science and AI tools effectively and safely
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Study Guides
This pupil-friendly study guide includes a unit summary, clear notes, common mistakes, and quick questions with answers.
Unit Summary
In this unit, pupils will explore how data science and AI tools are having an impact on our daily lives. They will develop an understanding of how to use these tools effectively as well as an awareness of the issues relating to trust, bias and misinformation.
Lesson Summary
You will learn to describe the purpose of LLMs and explain why the output may not be trustworthy.
Key Notes
- LLMs are trained on large amounts of text data.
- The aim of LLMs and chatbot applications is to provide realistic conversations by predicting the next word or phrase.
- There is no guarantee that data used to train LLMs is accurate, unbiased and trustworthy.
- Bias is when the output of an AI model favours some things and deprioritises or excludes others.
Vocabulary To Learn
- language model: an AI system used to produce or complete written text based on patterns identified in training data
- prediction: an estimate of what might happen next based on patterns found in training data
- bias: when something is unfair towards or against something or someone
- trust: confidence that something will work as expected and produce reliable and fair results
Common Mistakes To Avoid
- Large Language Models (LLMs) understand what they're saying, like a human does.
3 Quick Questions (With Answers)
1. Describe the system or process from this lesson in clear steps.
LLMs are trained on large amounts of text data. The aim of LLMs and chatbot applications is to provide realistic conversations by predicting the next word or phrase.
2. Define this computing term and give one practical example. 'language model'
an AI system used to produce or complete written text based on patterns identified in training data. Add one real device or system example to prove understanding.
3. Correct this common computing misconception.
Mistake: Large Language Models (LLMs) understand what they're saying, like a human does. Correction: LLMs do not understand language or meaning. They generate responses by predicting the most likely next word based on patterns in the data they were trained on, not because they understand the content like a human does.
More Lessons In This Unit
Browse all guides in the Year 10 Computing guide library.