Are AI coding assistants getting worse?
Why results can change
AI tools update their models and settings often, and a change that helps one task can hurt another. Context limits matter too, since a large project may be trimmed down to fewer relevant files before the model sees it.
Your own prompts and project setup also affect output. A clear, well-scoped request usually beats a vague one, regardless of the tool. A messy project with unclear names can also produce weaker suggestions.
- Model updates can change behavior
- Context limits affect large projects
- Vague prompts give vague results
- Messy project structure hurts suggestions
What to do when quality drops
Try a smaller task with a clear example. Compare the same prompt in a second tool to see whether the problem is general or specific to one product. A quick comparison often tells you more than a long debate.
Keep notes on prompts that worked, so you can reuse them when quality dips. Good notes turn a bad week into a fast reset. A tool that once felt sharp can feel dull if your own requests have grown more complicated.
- Retry with a smaller, clearer task
- Compare the same prompt in another tool
- Keep notes on prompts that worked
- Check your project structure
Common mistakes
- Assuming a tool is broken after one bad answer.
- Blaming the model without checking your prompt.
- Ignoring that a large context may reduce accuracy.
