AI Source Citations
Attach every factual claim in an AI answer to the record or passage that supports it.
What it adds
Inline citations on AI-generated answers, each resolving to a specific record, document, or passage inside the app.
What your agent is told to do
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What your agent is told to do
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Find the places where the app already generates answers from its own data, and capture which records were retrieved for each generation. Citations are built from the retrieval step, not reconstructed afterwards by asking the model where it got something.
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Cite the smallest useful unit. A paragraph or a single field beats a whole document, and a whole document beats a collection, because a citation the user has to search through is barely a citation.
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Re-check permissions at the moment a citation is opened, using the app's existing authorization rules. The set of records the generation ran over may include things the reader is not entitled to see, and the citation must fail closed rather than reveal a title or excerpt.
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Mark statements that combine several sources as inferences, listing every source involved, and distinguish them visually from statements lifted from one place. Confidence banding and review routing belong to AI Confidence and Uncertainty; this feature owns only provenance.
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Do not attach a citation to a statement merely because the record was in the retrieval set. An unsupported claim with a plausible-looking citation is worse than an uncited one, because the citation stops the reader checking.
Edge cases it handles
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Edge cases it handles
8- A citation must point at the smallest span that actually supports the claim. Citing an entire long document forces the reader to re-do the work and turns provenance into decoration.
- Permissions must be checked when the citation is opened, not only when the answer was produced. A user whose access was revoked, or a colleague the answer was shared with, must be refused without leaking the source's title or a preview.
- Retrieved but unused records must not be cited. The retrieval step returns candidates, and treating all of them as support attaches authority to material the answer never relied on.
- A statement synthesised across several sources must say so and name all of them, rather than picking one source and presenting the inference as a direct quotation.
- When a cited record is deleted, moved, or has its access changed, the citation must resolve to a clear unavailable state that says the source no longer exists or is no longer visible. A dead link or a silent redirect to the wrong record destroys trust in every other citation on the page.
- An answer where the model produced no supportable claims must say it could not find support, rather than shipping an uncited wall of text that looks identical to a cited one.
- Citations must survive the answer being exported, printed, or copied, at minimum as a readable reference, because an answer separated from its provenance will be quoted as fact.
- If the generation is truncated or the model returns malformed structured output, discard the partial citation mapping rather than attaching offsets that now point at the wrong text.
Definition of done
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Definition of done
8- Every factual statement in a generated answer either carries a citation or is visibly marked as unsupported.
- Citations resolve to a specific record or passage, not to a search result or a collection.
- Opening a citation re-checks the reader's permissions and refuses without leaking source content.
- Statements inferred across multiple sources are labelled as such and list every source involved.
- Deleted or newly restricted sources render as an explicit unavailable citation rather than a broken link.
- Records retrieved but not used to support the answer are never cited.
- The feature matches the existing design system.
- No existing functionality is broken.
Related features
Prompt Injection Defense
Prompt Injection Defense
Stop instructions hidden in documents, pages, and tool output from steering the AI.
What it does
A boundary between the app's own instructions and untrusted content, backed by enforced tool permissions and confirmation for consequential actions.
How it works
- 1 Enumerate every route by which content the app did not author reaches a model: uploaded files, retrieved passages, fetched pages, records from connected accounts, and the results of tool calls.
- 2 Keep the app's own rules in the trusted portion of the request and enclose untrusted content in a clearly delimited region labelled as material to be analysed rather than instructions to be obeyed.
- 3 Enforce what the model may reach outside the model. Tool permissions, tenant scoping, and rate limits must hold even when the model has been completely persuaded — an instruction telling the model not to delete things is not a control.
Copy the prompt
No account needed
Add this feature to my app:
https://addthisfeature.com/x/prompt-injection-defense
Model Fallback
Model Fallback
Keep AI features working when the primary model provider fails.
What it does
A bounded retry path that reruns a failed AI task on a compatible alternative model and discloses the substitution.
How it works
- 1 Classify failures before reacting. Provider outages, capacity rejections, rate limiting, and timeouts are worth retrying elsewhere; a refusal, an invalid request, or an authentication error will fail identically on any destination and must surface immediately.
- 2 Honour rate limiting with backoff before moving on. Retrying instantly against a provider that asked you to wait deepens the outage and can extend the penalty period.
- 3 Allow fallback only to models that can honour the same output shape and the same tool behaviour as the original. A substitute that returns a different structure turns a provider outage into a data problem inside the app.
Copy the prompt
No account needed
Add this feature to my app:
https://addthisfeature.com/x/model-fallback
AI Import Column Mapping
AI Import Column Mapping
Guess how an uploaded file's columns line up with the app's fields, then ask.
What it does
A proposed mapping from uploaded columns to destination fields, shown with samples and confirmed before the import runs.
How it works
- 1 Take the uploaded headers and a small sample of rows and propose a mapping to the destination fields. Match on exact and near-exact header names first and only consult the model for the columns that remain unresolved.
- 2 Constrain the candidate set to fields the current user and the importer are actually permitted to write. A mapping that targets a read-only, computed, or privileged field must never be offered, whatever the header says.
- 3 Show the proposal as a table: source column, sample values, destination field, confidence, and the transformation that will be applied. A user cannot approve a mapping they cannot see the effect of.
Copy the prompt
No account needed
Add this feature to my app:
https://addthisfeature.com/x/ai-import-column-mapping
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.