Document Question Answering
Let users ask questions about a document and get answers drawn only from that document.
What it adds
An extraction and retrieval pipeline over uploaded files, with answers that cite the passages they came from.
What your agent is told to do
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What your agent is told to do
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Extract text from each upload with the app's existing background-job system, and record per-page or per-section extraction status so unreadable parts are known rather than silently missing.
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Retrieve candidate passages first, then answer only from those passages. Instruct the model to say the document does not cover a question rather than filling the gap from general knowledge.
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Attach a citation to every claim, pointing at the specific passage and its location in the file, and make the citation open that location in the document viewer.
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Isolate extracted text and any derived index by tenant and by the document's own permissions, and re-check permission at query time rather than relying on the index being correctly partitioned.
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When a document is replaced or edited, mark its index stale immediately and block answers until re-extraction finishes. Do not serve answers from the previous version's passages under the new file's name.
Edge cases it handles
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Edge cases it handles
8- Scanned pages, images without text, password-protected files, and unsupported formats will appear. Report exactly which pages or sections could not be read and keep answering from the ones that could.
- An answer with no citation is not trustworthy. If no retrieved passage supports the claim, return the no-answer state rather than an uncited paragraph.
- The document not containing the answer is a correct outcome, not a failure. Say so plainly and offer the closest passages found, without inventing a synthesis.
- Extracted text and any index derived from it inherit the document's access rules. A user who loses access to the file must immediately stop getting answers built from it.
- Replacing a document must invalidate cached answers and prior citations. A citation pointing at a page number that no longer exists must be shown as expired, not followed blindly.
- Very large documents will exceed both the extraction budget and the context window. Cap the file size accepted and tell the user the limit before they wait for an upload to fail.
- If the model is unavailable, keep the document viewer and its full-text search working so the file is still usable.
- Knowledge Base Chat answers across the app's published sources. This feature answers within a single user-supplied document; do not mix the two corpora in one answer.
Definition of done
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Definition of done
8- Extraction runs in the background and records which pages or sections were unreadable.
- Every answer carries citations that resolve to a location in the source document.
- Questions the document does not cover return an explicit no-answer response rather than a generated one.
- Extracted text and derived indexes are unreachable across tenants, and permission is verified at query time.
- Replacing a document invalidates its index and its cached answers before any new question is answered.
- Document viewing and keyword search continue to work when the model is unavailable.
- The feature matches the existing design system.
- No existing functionality is broken.
Related features
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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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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.