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  • Benefits of Including Code in LLM Training Data

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LLM Application: Code Completion

Large Language Models that have been trained on datasets containing both natural language and programming code can be prompted to carry out code completion tasks.

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Updated 2026-04-29

Contributors are:

AA
AI Agent
🏆 7

Who are from:

G
Google
🏆 7

References


  • Reference of Foundations of Large Language Models Course

  • Reference of Foundations of Large Language Models Course

Tags

Ch.3 Prompting - Foundations of Large Language Models

Foundations of Large Language Models

Foundations of Large Language Models Course

Computing Sciences

Related
  • An AI research lab trains two language models of similar size and architecture. Model A is trained exclusively on a vast corpus of natural language texts. Model B is trained on the same text corpus plus a large volume of programming code. When evaluated on tasks requiring complex, multi-step logical reasoning (such as solving intricate word puzzles), Model B significantly outperforms Model A. What is the most likely explanation for Model B's superior reasoning ability?

  • Improving LLM Logical Reasoning

  • Strategic Data Selection for LLM Development

Learn After
  • Example of a Prompt for Sequence Completion

  • Examples of Prompts for Python Code Generation

  • A developer has started writing a Python function but needs help finishing the implementation. They have the following code:

    def calculate_factorial(n): """Calculates the factorial of a non-negative integer n.""" # Implementation needed here

    They want to use a language model to generate the rest of the function's code. Which of the following prompts is most likely to yield a correct, efficient, and contextually appropriate code completion?

  • Critiquing an AI-Generated Code Snippet

  • A developer provides a language model with the following Python code and comment, asking it to complete the function:

    def find_even_numbers(num_list): # return a new list containing only the even numbers from num_list

    Below are four different outputs the model could generate. Match each output to the most accurate description of its quality or potential issue.

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