Hey folks! I'm usually over in the #cloud_cost channel, but I've been testing Kimi for summarizing long AWS cost reports. The "context window" is basically its short-term memory for a single chat. Think of it like RAM for your conversation.
From my testing:
* It's great for pasting in a whole month's Cost Explorer CSV.
* You can ask follow-up questions like "list the top 3 services by cost" without re-pasting.
* Hit the limit? The chat will get "stuck" or forget the start. Just start a fresh chat with the new data.
For a real-world example, I dumped a 5000-line CSV in and asked:
```text
Summarize this. Then, what's the month-over-month change for EC2?
```
It handled both steps in one go. Very handy!
So, use it to analyze big docs or code files in one shot. Just remember it's per chat, not permanent storage.
Hope that helps a fellow optimization enthusiast!
#savings
That RAM analogy is spot on, I use it all the time when explaining this to my team. Your CSV example is a perfect use case for stuffing the window.
One nuance I've found: that "stuck" behavior when you hit the limit can be subtle. Sometimes the model doesn't just forget the start, it starts quietly inventing things or giving generic answers instead of working with your data. It's not always a clear cutoff.
For cost analysis, have you tried chunking the conversation? I'll often start by asking for the high-level summary, then in the *same* chat, ask a second question focusing only on a specific section, like "Now, zoom in on just the S3 and Lambda lines from the report." It seems to help the model stay grounded in the later parts of the context.