Year 10 - Computing
Getting the most out of an LLM
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 how to improve LLM output and use prompt engineering to improve LLM output.
Key Notes
- The performance of LLMs is highly dependent on how users craft their prompts.
- Prompts should be specific and clear to enable the model to interpret what you want.
- Prompts should provide relevant context or examples so the model can generate more accurate and tailored responses.
- LLMs can reflect biases from their training data, so responses should be critically evaluated and cross-checked.
Vocabulary To Learn
- prompt: the input question, instruction, or message you give to a large language model (LLM)
- vague: when something is unclear or too general to be useful
- context: the extra information you give to help the LLM interpret your prompt better
- engineering: using knowledge and skills to design and create things that solve problems or make life easier
Common Mistakes To Avoid
- You can just type anything into an LLM and it will always give the best answer.
3 Quick Questions (With Answers)
1. Describe the system or process from this lesson in clear steps.
The performance of LLMs is highly dependent on how users craft their prompts. Prompts should be specific and clear to enable the model to interpret what you want.
2. Define this computing term and give one practical example. 'prompt'
the input question, instruction, or message you give to a large language model (LLM). Add one real device or system example to prove understanding.
3. Correct this common computing misconception.
Mistake: You can just type anything into an LLM and it will always give the best answer. Correction: LLMs work best when you give them clear, specific and well-structured prompts. The quality of the input directly affects the quality of the output; this process of designing and refining inputs to get better results is called prompt engineering.
More Lessons In This Unit
Browse all guides in the Year 10 Computing guide library.