AI Forecast Narrative
Turn the numbers a forecast already produced into a plain explanation of the outlook.
What it adds
A written interpretation of an existing forecast, covering the horizon, the confidence range, and the assumptions behind it.
What your agent is told to do
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What your agent is told to do
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Take the forecast values, the confidence interval, and the horizon from the app's existing forecasting calculation and pass them to the model as fixed inputs. The model interprets numbers; it never produces or adjusts them.
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Mark historical values and projected values distinctly in what you send, and require the narrative to keep them distinct in the prose. A sentence that blends last quarter's actuals with next quarter's projection is misinformation.
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State the horizon, the confidence range, and the assumptions the forecast rests on in the narrative itself, in the metric's own units and the workspace's time zone.
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Invalidate the narrative whenever the forecast inputs change — new actuals, a changed model, a corrected historical series — and regenerate rather than leaving prose that no longer matches the chart above it.
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Describing past movement is owned by AI Trend Analysis and explaining a specific outlier is owned by AI Anomaly Explanation. This feature covers the projected portion only; do not let it re-narrate the history the sibling features already describe.
Edge cases it handles
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Edge cases it handles
8- Any number in the narrative that did not come from the forecasting calculation is a defect. Verify the figures in the generated text against the source values and discard the narrative on a mismatch.
- The horizon, the confidence interval, and the assumptions must be named. A projection stated without its uncertainty reads as a promise and will be quoted as one.
- Projected values must never be described in the past tense or alongside actuals without a label. Enforce the distinction in the output format, not only in the instruction.
- A narrative generated from a superseded forecast must be marked stale or removed. Tie it to the forecast run that produced it and drop it when a new run lands.
- A wide confidence interval or a short history means the forecast is weak. Suppress the narrative or lead with the caveat rather than writing a confident paragraph about a guess.
- If the model refuses, times out, or returns a truncated paragraph, show the forecast chart and its interval alone. The numeric forecast is the product; the prose is a convenience.
- Forecasts often cover revenue or headcount. Decide what may leave the app, and do not send customer names, account identifiers, or other detail the narrative does not require.
- Narratives should be generated once per forecast run and stored, with a ceiling on regeneration, so a dashboard refresh does not trigger a new call each time.
Definition of done
9
Definition of done
9- Every figure in the narrative traces back to the forecasting calculation and is verified against it.
- Historical and projected values are labelled distinctly in the output.
- The horizon, confidence range, and assumptions are stated in every narrative.
- A narrative is invalidated and regenerated when its forecast inputs change.
- Low-confidence forecasts are either suppressed or carry an explicit caveat rather than reading as commitments.
- The forecast chart and its interval remain available when the model is unavailable.
- Narratives are stored per forecast run and are not regenerated on each page view.
- 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 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 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
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.