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Asking Questions

Queries are the heart of Kinn. They're how all the discovery happens — you ask a question in plain language and Kinn answers from the conversations it has ingested across your connected integrations.

The most common mistake isn't asking a "wrong" question — it's asking a thin one. Faced with an open-ended box, it's easy to fall back on something like "summarize Discord this week." That'll give you something interesting, but it leaves most of Kinn's power on the table. The habits below are how you unlock the rest.

tip

You don't have to start with a perfect query. Give Kinn a little context and refine from there — a good answer is usually a short conversation, not a single flawless prompt.

Start by asking the agent​

If you're not sure what to ask, ask the agent itself. It's been built and tuned around the needs of real partners, so it usually has good ideas about how it can help. Something as simple as "I'm planning a marketing push and could use ideas for creative — what do you have on our audience?" is enough to get going. When in doubt, this is always a safe first move.

Give context before you ask​

Kinn is good at spotting opportunities you might miss — but only if it knows what you're trying to do. It's easy to ask for a metric and forget the goal behind it, and the goal is exactly what helps Kinn weight the answer for you.

Compare these two:

How are our creators doing on Instagram and TikTok?

We spent $50K on creator promotions this month and have a $10K budget for a follow-up campaign. Looking at engagement and comment sentiment across the creators we're tracking on Instagram and TikTok, which ones should we prioritize for the next round?

Same underlying data — but the second tells Kinn what "good" means for you, so the answer comes back weighted toward the decision you actually have to make.

Be specific​

When you leave things vague, Kinn fills the gaps with assumptions. It won't invent data — but you'll get sharper results when you pin down the specifics: dates, platforms, KPIs, and the current version, patch, or release (or DLC, for a game).

Specificity is also where Kinn has a real edge. Because it answers from your integrations rather than the open web, it doesn't get pulled toward stale, viral posts the way web-grounded tools do. If a problem went viral a year ago and was patched since, a general model will often resurface that old post; Kinn won't, especially when you tell it which version or time window you care about. Naming the current patch lets you cleanly ignore feedback that's already been addressed.

Ask for visuals​

AI tends to produce a lot of text, which is great when you're the one digging — but sometimes you need to convey a result quickly. Kinn has a large chart library and a good sense of which chart fits which question, so just ask. "Chart engagement by creator over the last month" or "show sentiment as a trend" turns a wall of text into something you can drop into a deck.

Compare across platforms​

Analyzing several integrations at once is one of Kinn's most novel capabilities, and precisely because it's novel, it gets overlooked. The key is knowing that each platform skews differently:

  • Discord is usually your core community — passionate, invested, close to the product.
  • Reddit tends to be more neutral and detached.
  • Steam skews toward strong reactions — complaints and intense feedback show up here first.
  • Social integrations (YouTube, TikTok, Instagram) lean casual and creator-driven.

If you're hunting bugs, gauging feedback, or looking for insight, asking across platforms — and reading each one's bias into the answer — is a great place to start. "Are people reporting this crash across Discord and Reddit, or just in one place?" tells you far more than either integration alone.

Example queries​

Copy these and adapt them to your integrations and timeframes. Start with the transformations to see what separates a thin query from a strong one, then use the library below as a jumping-off point.

From bland to powerful​

The same question, leveled up by adding a goal and some specifics:

Bland: Summarize Discord this week.

Better: We shipped the 2.1 patch on Monday. Summarize what people in Discord are saying about it this week — especially any bugs or regressions — and flag anything that's also coming up on Reddit.

Bland: How are our creators doing?

Better: We spent $50K on creator promotions this month. Rank the creators we're tracking on TikTok and Instagram by engagement and comment sentiment, and chart the top five — we're deciding where to put a $10K follow-up budget.

The better versions tell Kinn the goal, the timeframe, the version, and what to do with the result — the same habits described above.

Triage: finding and grouping problems​

  • What are the most common bugs reported this week?
  • Group the open complaints by theme.
  • Is anyone reporting problems with login since the latest release?
  • Which issues are showing up across more than one integration?

Trend-spotting: what's changing​

  • What are people talking about more this week than last week?
  • How has sentiment changed since the latest release?
  • What new feature requests have come up this month?

Release tracking: did the release land​

  • Are users still hitting the crash we fixed in 2.1?
  • What's the reaction to the 2.1 patch so far, across Discord and Reddit?
  • Summarize feedback on the new onboarding flow and chart sentiment over time.

Creator and campaign performance​

  • Rank our tracked creators by engagement over the last 30 days.
  • Which sponsored posts are driving the most discussion in the comments?
  • Compare comment sentiment across the creators promoting us on TikTok vs. Instagram.

Cross-platform insight​

  • Are people reporting this bug everywhere, or just in one community?
  • Where is feedback most positive about the latest release — Discord, Reddit, or the comments on our videos?
  • Steam reviews are turning negative — is the same complaint showing up in Discord, or is it isolated to Steam?

Common pitfalls​

If an answer comes back thin, vague, or empty, it's usually one of these. Each is the flip side of a habit above.

You started with a bland catch-all​

"Summarize Discord this week" gives you a summary, but not an answer to any particular question — because you didn't ask one. Add a goal: what decision is this for, what are you hoping to find?

You left out the context​

Kinn can only weight an answer toward your goal if it knows the goal. If you ask for metrics without saying you're optimizing a campaign budget, you'll get metrics — not a recommendation. Say what the question is for.

You were vague about version or date​

Leave the timeframe open and you'll get feedback mixed across patches, including problems that have already been fixed. Naming the current version, patch, or date range is what lets Kinn cleanly set aside outdated feedback — one of its biggest advantages over web-grounded tools.

You only looked at one platform​

A single integration tells you what one audience thinks, and every platform has its own bias — Discord skews passionate, Reddit more neutral, Steam toward strong reactions, social more casual. Asking across platforms is where the most interesting signal lives.

You're drowning in text​

If the answer is more than you can skim, ask Kinn to visualize it. A chart or a tight summary often conveys the result faster than paragraphs.

When in doubt, ask the agent​

If you're stuck on how to phrase something, hand the problem to the agent and let it suggest a direction. It's more capable than a blank query box makes it look.

The practical ones​

  • The integration hasn't finished syncing — check its status before reading too much into an empty answer.
  • The data simply isn't there — if no one's discussing a topic in your connected integrations, consider connecting another integration.