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Table Of Contents
Supercharged Coding with GenAI
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In the age of big data, artificial intelligence, and intensive data processing, an essential characteristic of well written Python programs is their ability to efficiently manage both memory usage and runtime performance. As part of the Software Development Life Cycle (SDLC), it is common practice to profile our programs, anticipate performance limitations and strive for efficiency in both aspects.
In addition to utilizing traditional tools for profiling program performance and addressing inefficiencies, we can leverage the model mastery of Large Language Models (LLMs) to code profile snippets, analyze maximal capacities, and optimize code to handle larger workloads.
In this chapter, we will examine two examples of inefficient implementations: a Fibonacci calculator with significant runtime overhead and a statistical analysis function that demands excessive memory for large matrices. By utilizing prompt precision best practices and chaining...