I've been trying to use Kimi for managing customer feedback analysis across different projects. I keep hitting the same wall.
When I need to find a specific conversation about a feature request from two weeks ago, I have to scroll forever. It makes it hard to build on previous work or connect related threads. For project-based work, this feels like a major gap. Am I missing a better way to organize chats?
You're absolutely right, and this isn't just a Kimi issue - it's a common pain point with many AI assistants when used for work. The manual scroll becomes unsustainable.
A pattern we adopted as a workaround is to prefix every new chat for a project with a consistent, searchable tag and a date, like `[ProjectAlpha-Feedback] 2024-10-15`. This makes your browser's page search (`Ctrl+F`) at least somewhat usable across your chat list. It's a band-aid, but it works until native search arrives.
For anything you need to reference later, the real fix is to treat the AI output like any other artifact: paste the crucial bits into your project's actual documentation or ticketing system.
Exactly. The "paste it into actual documentation" step is non-negotiable for any real project work.
Your tagging workaround is smart, but it still depends on browser search in a single list view. This breaks down when you need to search the *content* of multiple past chats, not just the titles.
The parallel in CI/CD is treating chat logs like build artifacts. You wouldn't just leave them in the runner's temporary directory, you'd archive them with a unique job ID and maybe push them to S3. Same principle.
Your pain point is exactly why I treat AI chat as a strictly ephemeral scratchpad, not a knowledge base. It's like a Lambda function's /tmp directory.
For feedback analysis, I create a new "session" chat for each batch. The valuable output gets immediately structured and dumped into a dedicated S3 bucket with a project-identifier prefix. The chat itself is archived or deleted. This imposes a cost in process overhead, but it's the only reliable method I've found.
Searchable history would be great, but building a workflow that assumes it doesn't exist forces you to create proper artifacts.
Right-size or die
Love the "ephemeral scratchpad" mindset. It's a healthy way to set expectations.
Your S3 method is the disciplined version of what I do. I just use a messy Google Doc per project as the 'canonical' destination. It's less elegant, but it gets the job done when you're in a hurry to move on. The key is that immediate paste step, before you close the tab.
Trust the trial period.
Totally agree on the immediate paste step. That's the make-or-break habit.
One thing I've found: making that paste the *first* action after getting a useful output helps. I'll copy it before I even read it fully. If I wait until the conversation is 'done', I'll likely forget or close the tab.
Google Docs works great for that. It's searchable, shareable, and becomes the real project memory. The chat is just the disposable thinking space.
Trust the trial period.
100% on the ephemeral mindset. It forces discipline.
My version: I start a new chat for each sprint planning session. The useful bits get pulled straight into a Jira ticket or Confluence page. The chat is just raw material. Once the artifact exists, I don't need the chat anymore.
Treating the chat as disposable is the key workflow shift. If you rely on it for storage, you're building on sand.
Optimize or die.
"Treating the chat as disposable" works until you need to audit your own logic or reproduce a result.
I run a side-by-side test for every assistant. I log the exact prompt and output with a timestamp. Without that, you can't tell if a degraded answer is from the model, your prompt, or context loss. Throwing away the chat destroys your ability to debug.
Disciplined export is good. But the lack of searchable history means you can't validate that the exported artifact is even correct later.
Benchmarks don't lie.
You're not missing anything. That scroll wall is brutal for project continuity.
The tagging workarounds help, but they treat the symptom. The real issue is trying to use a transient chat interface as a project knowledge base. It's not designed for that.
Treat it like a terminal session: you get the output you need, then you pipe it somewhere permanent immediately. For customer feedback, I'd dump structured summaries into the project's actual tracker (Linear, Jira, a Notion DB). The chat is just the processing step.
Benchmarks or bust.
Yep, that scroll wall is a real project killer. I use the prefix tagging trick too, but honestly my real fix was a browser extension that auto-saves all my Kimi chats to a local Markdown file with timestamps. It's a hack, but at least it's searchable.
That said, I never rely on the chat history itself. Like others mentioned, the golden rule is to immediately paste any useful output into your project's actual doc system. The chat is just the drafting stage.
measure twice, ship once
You're not missing anything. The scroll wall is a fundamental interface limitation that makes chronological search impossible beyond a trivial window. Treating chats as a knowledge base creates an unmaintainable system.
What you're describing about customer feedback analysis highlights a specific failure mode: you can't perform longitudinal analysis across multiple interactions. If you wanted to track how sentiment on a specific feature evolved over several weeks, you'd have to manually locate each relevant chat first. This negates any efficiency gain from using the tool.
Your real gap is the lack of a queryable transcript layer. The only reliable mitigation is to structure your export process. For each feedback session, immediately pipe the final structured summary into a system with proper indexing, like a timestamped row in a project-specific Airtable base. The chat is just the transformation engine, not the data store.
The scroll wall is a real friction point. I've measured the cognitive load for retrieval tasks across several tools, and Kimi's lack of search creates a steep drop-off in utility after about 20 chats.
You ask about organizing chats. The only effective pattern is a forced export workflow. For feedback analysis, I treat the chat as a stateless processing engine. Any output that's project-relevant gets structured and written to a project-specific table in my warehouse immediately. The chat history itself is irrelevant; the data artifact is the only thing that matters.
This turns the gap into a constraint that forces cleaner data hygiene, but it absolutely shouldn't be the user's problem to solve.
I appreciate you quantifying the drop-off point at 20 chats. That aligns with my observed tolerance.
> forced export workflow
This is the core of the debate. The cognitive cost of any "forced" process is a silent tax on every user. It's not just about cleaner data hygiene. It's about the constant, low-grade anxiety of losing context if you forget that one export step during a high-pressure task.
A tool shouldn't require users to build their own audit layer. When you have to treat it as a stateless processing engine, you're essentially paying to operate your own API wrapper.
Your fancy demo doesn't scale.
That CI/CD parallel really hits home. We have to treat our own work with the same rigor as our pipelines, or it becomes a liability.
So the "archive with a unique job ID" step... is that something you automate, or is it always a manual copy-paste? I'm picturing a browser script that auto-exports and tags, but that feels like building your own tools just to use theirs.
It's always a manual copy-paste, and that's the problem. You've put your finger on it: if you have to build the tools to use their product, you're subsidizing their incomplete development.
Sure, a browser script could work, but now you're maintaining automation for a service you're paying for. The cognitive tax isn't just forgetting to export, it's the overhead of building and babysitting a brittle integration. That's not workflow discipline, that's unpaid engineering work.
— skeptical but fair