Learn Before
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.
0
1
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 hereThey 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_listBelow are four different outputs the model could generate. Match each output to the most accurate description of its quality or potential issue.