AI Topic Tagging
Tag content against a controlled vocabulary instead of letting labels multiply.
What it adds
Automatic assignment of topics from a managed tag catalogue, applied as suggestions with an approval path for new terms.
What your agent is told to do
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What your agent is told to do
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Treat the tag catalogue as the authority. Suggested tags must resolve to existing terms, and anything else goes into a proposal queue where an administrator accepts, renames, or rejects it.
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Cap the number of tags per item and apply a relevance threshold, so only topics genuinely central to the content are applied. Three accurate tags beat twelve that are technically defensible.
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Apply tags as suggestions the user can accept or dismiss on first rollout, and record acceptance rates before considering automatic application anywhere.
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Store the catalogue version with each tagging run and give administrators a way to re-tag affected content when terms are merged, renamed, or retired, without re-tagging the entire corpus.
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Where this sits beside AI Intent Detection, keep the two catalogues separate: intents describe what someone wants done and topics describe what the content is about. Merging them produces a vocabulary that serves neither.
Edge cases it handles
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Edge cases it handles
8- New terms must never be created by the model directly. Route every unmatched suggestion to an approval queue, because an auto-growing vocabulary becomes unusable within weeks and cannot be un-grown.
- Tagging everything a document mentions in passing makes tags useless for filtering. Enforce a per-item ceiling and a relevance threshold, and prefer omission to a weak match.
- Content spanning several topics should receive several tags, and content matching nothing should receive none. An untagged item is a valid outcome and must not be forced into the nearest available term.
- When terms are merged, renamed, or retired, existing assignments must be migrated or invalidated. Stale tags pointing at removed terms should disappear from filters rather than render as blanks.
- Private, restricted, or draft content must not shape tags visible on public surfaces, and tag counts on public pages must not reveal the existence of records the viewer cannot see.
- Re-running the tagger must be idempotent for unchanged content. Repeated runs must not accumulate near-duplicate tags or overwrite tags a person applied by hand.
- Human-applied tags outrank model-applied ones. Record which is which and never let a later automatic run remove a tag a user chose.
- When the model is unavailable, tagging is skipped and the item is queued for a later pass. Publishing must never block on the tagger.
Definition of done
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Definition of done
8- Every applied tag exists in the controlled catalogue at the recorded version.
- Unmatched suggestions enter an approval queue and are never created automatically.
- A per-item tag ceiling and relevance threshold are enforced, and zero tags is a supported result.
- Catalogue changes trigger targeted re-tagging rather than a full corpus pass.
- Manual tags are distinguished from automatic ones and are never removed by a later run.
- Restricted content does not influence tags or counts on surfaces the viewer cannot access.
- The feature matches the existing design system.
- No existing functionality is broken.
Related features
Retrieval-Augmented Generation
Retrieval-Augmented Generation
Ground AI answers in the app's own content by retrieving source passages first.
What it does
A retrieval layer that selects permitted passages from app content and supplies them to the model as cited evidence.
How it works
- 1 Decide which content is answerable from and treat everything else as out of scope. A retrieval feature pointed at the whole database returns confident answers about records nobody meant to expose.
- 2 Apply the requesting user's tenant, record, and field permissions during retrieval, before any passage is assembled into a request. Filtering the answer afterwards is too late — the content has already crossed the boundary.
- 3 Chunk on the content's own structure — sections, headings, rows, message boundaries — and carry enough surrounding context in each chunk that it still means something on its own.
Copy the prompt
No account needed
Add this feature to my app:
https://addthisfeature.com/x/retrieval-augmented-generation
AI Dashboard Insights
AI Dashboard Insights
Explain what actually changed on a dashboard, in the viewer's own filters and period.
What it does
A short generated commentary attached to a dashboard, describing the movements that matter under the filters currently applied.
How it works
- 1 Compute the numbers first, in the app, using the dashboard's existing queries with the viewer's active filters. Pass the model the computed figures and let it write the wording, never the raw rows to add up itself.
- 2 State the period and the comparison period explicitly in every insight. A sentence saying signups are up is meaningless without saying up against what.
- 3 Filter the input to metrics the viewer is permitted to see before it reaches the model. A commentary that mentions revenue to someone whose dashboard hides revenue is a permissions leak.
Copy the prompt
No account needed
Add this feature to my app:
https://addthisfeature.com/x/ai-dashboard-insights
Knowledge Source Sync
Knowledge Source Sync
Keep the AI's knowledge current as documents, records, and external sources change.
What it does
An ingestion pipeline that syncs approved sources on a schedule, tracks versions, and removes content whose source disappears.
How it works
- 1 Give an operator an explicit approval step for every source, and default to nothing being indexed. Automatic inclusion of whatever the connected account can see is how private material ends up answerable.
- 2 Store a content fingerprint or version for each document alongside the time of the last successful sync, and re-ingest only what has actually changed since then.
- 3 Reuse the app's existing background-job system and make each sync resumable at the document level, so an interruption partway through a large source restarts from the last completed document rather than from the beginning.
Copy the prompt
No account needed
Add this feature to my app:
https://addthisfeature.com/x/knowledge-source-sync
How it works
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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.