Semantic Search
Find records by meaning, so the right result appears without the exact wording.
What it adds
An embedding-backed retrieval path over the app's searchable records, blended with the existing keyword search and filtered by the same permissions.
What your agent is told to do
5
What your agent is told to do
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Choose per record type exactly which fields are embedded, and exclude anything that is hidden, internal, or restricted. A field that is not searchable today must not become searchable by way of an embedding.
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Apply tenant scoping and per-record permission checks to the retrieved set before results are shown, and over-fetch candidates so filtering does not leave a short page.
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Blend semantic results with the existing keyword search rather than replacing it. Exact identifiers, quoted phrases, and precise names must still return their exact match at the top.
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Re-embed a record when its embedded fields change, and store the model or index version alongside each vector so a version change can trigger a controlled rebuild rather than silently mixing incompatible vectors.
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Ranking of an already-retrieved set is owned by AI Search Result Reranking. This feature owns candidate retrieval and the index; do not add a second ordering pass here.
Edge cases it handles
7
Edge cases it handles
7- Embedding a field the user cannot see leaks it through result ordering even when the field itself is never displayed. Decide the embedded field list explicitly per record type.
- Permission and tenant filters must run after retrieval and before display. A vector index treated as pre-filtered will eventually return another tenant's record.
- A user searching for an order number or a quoted phrase wants that exact record, not something conceptually adjacent. Exact matches must not be diluted by semantic neighbours.
- Content changes and index-version changes both invalidate vectors. Handle re-embedding in the background with a visible backlog, and keep serving keyword results while a rebuild is in progress.
- One-word, ambiguous, or non-supported-language queries produce poor semantic matches. Detect them and fall back to keyword behaviour rather than returning confidently irrelevant results.
- Embedding every record on first deploy is a large, slow, and expensive job. Batch it, make it resumable, and cap its rate so it does not starve the rest of the app.
- If the embedding provider is down, search must still work on keywords alone, with no error shown beyond a quiet note that results may be less relevant.
Definition of done
8
Definition of done
8- Only the explicitly listed fields per record type are embedded, and restricted fields are excluded.
- Tenant and permission filters are applied to retrieved candidates before any result is displayed.
- Exact identifier and quoted-phrase matches still rank first.
- Content edits and index-version changes queue a re-embed, and vectors record the version that produced them.
- Short, ambiguous, or unsupported-language queries fall back to keyword search.
- Search returns keyword results with no visible failure when the embedding provider is unavailable.
- The feature matches the existing design system.
- No existing functionality is broken.
Related features
AI Insight Cards
AI Insight Cards
Turn a report into a few grounded cards, each tied to a number the app calculated.
What it does
A small set of cards summarising notable movements in a report, each linked to the query and figure that produced it.
How it works
- 1 Detect candidate movements in the app first, using the report's own aggregations. The model ranks and phrases the candidates it is given; it does not go looking for them and it does not produce the arithmetic.
- 2 Bind each card to the query, metric, and period that support it, and make the card link through to the filtered view so a reader can check it in one click.
- 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
AI Trend Analysis
Explain what a chart is actually showing, in sentences backed by computed numbers.
What it does
A written summary attached to a time series or metric set, describing direction, magnitude, and comparison period.
How it works
- 1 Compute every number in ordinary code before the model is involved: the change, the rate, the comparison period, the baseline, the seasonal adjustment. The model writes prose about figures it is given and must never produce a figure of its own.
- 2 State the comparison window and the time zone in the summary text, resolved from the workspace's configured zone rather than the server's. A change described as week over week is meaningless without saying which weeks.
- 3 Instruct the model to describe what the data shows and to stop there. Do not let it assert causes, attribute movement to campaigns or releases, or predict what happens next.
Copy the prompt
No account needed
Add this feature to my app:
https://addthisfeature.com/x/ai-trend-analysis
AI Key Point Extraction
AI Key Point Extraction
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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2
Give it to your AI
Paste it into Claude Code, Cursor, v0, Lovable — whatever you build with.
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3
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.