This chapter explores the challenges and strategies for improving AI agents' performance in complex coding tasks, emphasizing precise code specifications, code-specific model ownership, and fine-tuning tasks on top of base models. It also discusses the need for context awareness, optimizing for end-to-end coding, and addressing biases in machine learning models for code bases.
Greylock partner Corinne Riley reads her essay "Code Smarter, Not Harder: Solving the Unknowns to Developing AI Engineers."
Building AI tools for code generation and engineering workflows is one of the most exciting and worthy undertakings by startups today. But there are still many open questions about the technical unlocks that must be solved to make coding tools that work as well as (or better than) human engineers in a production setting. Riley explores these core questions alongside an analysis of the current ecosystem of startups developing AI coding tools. You can read the essay here: https://greylock.com/greymatter/code-smarter-not-harder/
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