Beyond file trees: why AI coding assistants need smarter context(nuanced.dev)
nuanced.dev
Beyond file trees: why AI coding assistants need smarter context
https://www.nuanced.dev/blog/beyond-file-trees
6 comments
Function calls are definitely one part of adding context, but lots of others that codegen tools are probably missing. Have you guys considered post-mortems, pagerduty outputs, slack threads focused on specific issues, etc.?
Yeah! We actually did an experiment where we provided AI tools with memory profiler outputs, Sentry exception reports, and telemetry from Datadog.
Will share results soon.
I would love to see the results; where will you share?
When debugging code, experienced developers don't read every file—they follow execution paths and understand system architecture. But today's AI coding tools try to read all files and get bogged down in unnecessary details.
With context windows limited to 200K tokens, cramming in random files isn't just inefficient, it's impossible for large codebases. If you’re debugging a failing test, you only need to understand the relevant files in the call chain. It's not about more context, it's about relevant context. That's what Nuanced provides through static analysis and machine learning.
With context windows limited to 200K tokens, cramming in random files isn't just inefficient, it's impossible for large codebases. If you’re debugging a failing test, you only need to understand the relevant files in the call chain. It's not about more context, it's about relevant context. That's what Nuanced provides through static analysis and machine learning.
Maybe show us an example of this graph in the blog post?