Agentic data science with the Data Agent Kit
cloud.google.com·7h ago
Data Science
Towards Data Science·Aug 28·5 min read
Why Claude Code Time Estimates Are Poor
TL;DR
Claude's code time estimates often fall short due to its reliance on historical data and lack of context awareness, leading to inaccuracies. This highlights the need for improved communication and understanding when using large language models (LLMs) for programming tasks.
✦ Why It Matters
Developers should critically assess LLM-generated time estimates and adjust project timelines accordingly.
Key Takeaways
- Track your implementation times for better estimates.
- Break tasks into smaller subtasks for accurate time predictions.
- Instruct Claude to estimate based on AI performance.
- Use historical data to inform future estimates.
How It Works
By tracking the time taken for previous tasks, engineers can create a skill for Claude Code that references this data, allowing it to make more informed estimates based on actual performance rather than human averages.