Unlocking the Power of Pair Programming: A Summary of a short course by Deeplearning.ai

Ali Issa
2 min readOct 17, 2023

๐๐š๐ข๐ซ ๐๐ซ๐จ๐ ๐ซ๐š๐ฆ๐ฆ๐ข๐ง๐  ๐ฐ๐ข๐ญ๐ก ๐š ๐‹๐š๐ซ๐ ๐ž ๐‹๐š๐ง๐ ๐ฎ๐š๐ ๐ž ๐Œ๐จ๐๐ž๐ฅ

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Pair programming with LLM presents a range of opportunities to boost coding proficiency and elevate code quality. My insights on this topic are derived from a short course offered by DeepLearning.AI and instructed by Laurence Moroney. Here are the key takeaways from my notes:

๐๐ซ๐จ๐ฆ๐ฉ๐ญ ๐“๐ž๐ฆ๐ฉ๐ฅ๐š๐ญ๐ž๐ฌ : To create effective prompts for the LLM, a structured prompt template is recommended. This template divides the prompt into three components:
1. ๐’๐ฒ๐ฌ๐ญ๐ž๐ฆ ๐๐ซ๐จ๐ฆ๐ฉ๐ญ (๐๐ซ๐ข๐ฆ๐ข๐ง๐ ):
- This part prepares the LLM for the specific task it should perform. It defines the expected behavior of the model.
2. ๐๐ฎ๐ž๐ฌ๐ญ๐ข๐จ๐ง:
- The question component is where you specify the userโ€™s query or the particular coding task you want the LLM to address.
3. ๐ƒ๐ž๐œ๐จ๐ซ๐š๐ญ๐จr:
- The decorator outlines how the LLM should generate or format its output, determining the structure of the response.

Pair programming scenarios with an LLM include:

1. ๐„๐ง๐ก๐š๐ง๐œ๐ข๐ง๐  ๐‚๐จ๐๐ข๐ง๐  ๐’๐ค๐ข๐ฅ๐ฅ๐ฌ
- Utilising a generator model, such as one from Google, to improve coding abilities by allowing the model to enhance code and provide explanations for the changes made. However, itโ€™s essential not to rely solely on the generated code; rigorous testing is necessary, especially for code deployed to a broad audience.

2. ๐Ž๐ฉ๐ญ๐ข๐ฆ๐ข๐ณ๐ข๐ง๐  ๐‚๐จde
- This involves making code more efficient in terms of memory and speed. While an LLM can generate more efficient code, itโ€™s crucial to review the generated code thoroughly. Sometimes, the model might incorporate libraries with different capabilities, which could lead to incorrect results.

3. ๐ƒ๐ž๐›๐ฎ๐ ๐ ๐ข๐ง๐  ๐‚๐จ๐๐ž
- LLMs are often used for debugging by providing error descriptions and code snippets. While LLMs can detect errors, they may also produce incorrect results. Fine-tuned models trained on coding languages are better suited for identifying complex errors.

4. ๐‡๐š๐ง๐๐ฅ๐ข๐ง๐  ๐“๐ž๐œ๐ก๐ง๐ข๐œ๐š๐ฅ ๐ƒ๐ž๐›๐ญ
- LLMs can assist in simplifying complex, undocumented code, especially when transitioning it from one developer to another.

Click here for more info about the course.

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