AI Search Result Reranking
Reorder search results by what the user actually meant, without changing what they may see.
What it adds
A post-retrieval ordering pass over a bounded candidate set, with a deterministic fallback and a record of why the order changed.
What your agent is told to do
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What your agent is told to do
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Run reranking strictly after the secure search layer has produced its result set. The reranker orders what it is given and must never widen the set or fetch a record on its own.
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Cap the candidate set to a fixed number of top results and a fixed latency budget. If the pass does not return within that budget, ship the original order rather than making the user wait.
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Preserve exact-match precedence: identifiers, quoted terms, and exact title matches keep their positions and are excluded from reordering.
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Keep a per-query record of the original order, the reranked order, and the score or reason for each move, visible to operators so a surprising result can be explained.
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Candidate retrieval, embedding, and index freshness belong to Semantic Search. This feature owns only the ordering of results already retrieved.
Edge cases it handles
7
Edge cases it handles
7- The reranker must only ever see records the secure search layer already cleared. Passing it raw candidates before filtering turns a ranking feature into a permission leak.
- Reranking an unbounded result set is slow and expensive. Fix a candidate ceiling and a cost ceiling per query, and degrade to the original order past either.
- Moving an exact identifier match off the first position makes the search look broken. Pin exact and quoted matches ahead of the reranked remainder.
- Ranking changes that nobody can explain destroy trust in search. Log inputs and per-result reasons so an operator can reconstruct any specific ordering afterwards.
- A model failure, timeout, or malformed score list must fall back to the deterministic ranking silently. The user sees slightly worse ordering, never an error page.
- Scores that come back for fewer results than were sent, or reference records that were not in the candidate set, must be discarded entirely rather than partially applied.
- Pagination must be computed after reranking, or the second page will repeat or skip records the user already saw.
Definition of done
8
Definition of done
8- The reranker receives only records already authorized by the secure search layer.
- Every query has a candidate ceiling and a latency budget, past which the original order is returned.
- Exact identifier, quoted, and exact-title matches retain their leading positions.
- Original order, final order, and per-result reasons are recorded and inspectable by operators.
- Model failures and malformed scores fall back to deterministic ranking with no user-visible error.
- Paging through reranked results never repeats or drops a record.
- 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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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
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.
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- 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.