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Book Overview & Buying
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Table Of Contents
Supercharged Coding with GenAI
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In this chapter, we explored how LLMs can assist in ensuring efficient applications by profiling runtime and memory usage, identifying maximal capacities, and suggesting optimized code to increase those capacities.
Using the recursive Fibonacci example, we saw how adopting a more efficient algorithm significantly reduces runtime. With the get_top_video function, we tackled large matrices under RAM constraints, assisting GenAI to optimize memory usage through chunking. GitHub Copilot assisted in profiling runtime and memory consumption and profiling runs across different inputs. ChatGPT estimated the maximal capacity within runtime and RAM constraints. Leveraging chained prompts, ChatGPT demonstrated the ability to vastly improve the implementation to achieve larger capacities and can do much more.
In the next chapter, we will further explore how to integrate GenAI into the SDLC, focusing on logging, monitoring applications, and error handling.