Why Claude Code Time Estimates Are Poor

LLMs like Claude often provide inaccurate, overly long time estimates for coding tasks because their training data reflects slower, human-only development cycles. To improve these estimates, users can feed the model historical data from their own LLM-assisted projects, creating a custom knowledge base for comparison. An alternative method involves instructing the LLM to break a large task into smaller sub-tasks and estimate the completion time for each step based on an LLM's capabilities, not a human's. These techniques help align AI-generated timelines with reality, improving team communication and project planning.