Knowledge Base Chat
Answer questions from the app's approved help articles and policies, with sources shown.
What it adds
A retrieval-backed answering surface over the app's published knowledge sources, restricted to what the asker is allowed to read.
What your agent is told to do
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What your agent is told to do
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Define which collections count as knowledge sources and mark each with an audience — public, customer, or internal. Filter the retrievable set by the asker's audience before retrieval, not after the answer is written.
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Restrict retrieval to currently published versions. Drafts, archived articles, and superseded revisions must be excluded from the corpus, not merely ranked lower.
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Cite every source used, and when two retrieved sources contradict each other, present both with their dates rather than picking one and hiding the conflict.
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Keep the answer inside the retrieved evidence. If retrieval returns nothing relevant, say the knowledge base does not cover it and offer the search results or a route to support.
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Re-index a source when it is published, edited, unpublished, or has its audience changed, using the app's existing background-job system, and drop any cached answer that cited it.
Edge cases it handles
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Edge cases it handles
8- An internal policy retrieved for a customer question is a disclosure incident. Audience filtering must run inside the retrieval query so no restricted passage ever reaches the model.
- A draft rewrite of an article must never outrank the published version. If a source has no published revision, it is not in the corpus at all.
- Contradictory sources are common when an old article was never retired. Show both with publication dates and say which is more recent rather than silently averaging them.
- Answering beyond the retrieved evidence produces plausible policy that does not exist. Anything the retrieved passages do not support must be omitted, not softened with a hedge.
- Edits must invalidate cached answers immediately. Serving yesterday's answer with today's citation is worse than having no cache, because the citation looks like verification.
- Show the answer as assistance, not authority, and keep the underlying articles one click away so a user can check it.
- When the model is unavailable, fall back to the existing keyword search over the same articles rather than showing an empty page.
- Document Question Answering covers a single file uploaded by one user; this feature covers the app's shared published sources. Keep the two corpora and their permission models separate.
Definition of done
8
Definition of done
8- Retrieval is filtered by the asker's audience before any passage reaches the model.
- Only currently published revisions are retrievable; drafts and archived material are excluded.
- Every answer lists its sources, and conflicting sources are shown together with dates.
- Questions unsupported by retrieved evidence return an explicit no-answer response.
- Publishing, editing, or unpublishing a source re-indexes it and invalidates answers that cited it.
- Keyword search over the same articles remains available when the model is unavailable.
- The feature matches the existing design system.
- No existing functionality is broken.
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What it does
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How it works
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- 3 Set a materiality threshold before generation, in both relative and absolute terms, so a swing on a metric with three events does not outrank a real change on a metric with thousands.
Copy the prompt
No account needed
Add this feature to my app:
https://addthisfeature.com/x/ai-insight-cards
AI Trend Analysis
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What it does
A written summary attached to a time series or metric set, describing direction, magnitude, and comparison period.
How it works
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Copy the prompt
No account needed
Add this feature to my app:
https://addthisfeature.com/x/ai-trend-analysis
AI Key Point Extraction
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Reduce long content to the decisions, facts, and requests that actually matter.
What it does
A short list of the salient points in a long document or thread, each tied back to the passage it came from.
How it works
- 1 Apply this where length is the problem: long threads, meeting notes, call transcripts, lengthy tickets, multi-page documents. Content that already fits on a screen does not need extracting.
- 2 Classify each point by kind — decision, fact, risk, request, open question — so a reader can scan for the category they came for instead of reading a flat list.
- 3 Carry a reference to the source passage with every point and let the reader jump to it. A point nobody can verify is a claim, not a summary.
Copy the prompt
No account needed
Add this feature to my app:
https://addthisfeature.com/x/ai-key-point-extraction
How it works
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1
Copy the link
Grab the Markdown instruction URL for this feature.
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Give it to your AI
Paste it into Claude Code, Cursor, v0, Lovable — whatever you build with.
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It inspects, then implements
Your agent reads your existing app first, then adds the feature to fit it.
Works with your stack
These instructions are written to adapt. They tell the agent to detect your framework, match your existing design system, and reuse what you already have — rather than assuming a particular stack.
Need it tighter than that? Customize the feature and tell it exactly what you're running.