How aidnn works
aidnn is built to take a finance or business question, plan an approach to answering it, pull from the data sources you’ve connected, and run the analysis. That has a few practical implications for how you prompt:- aidnn does its own planning. You don’t need to tell it the steps — you tell it the outcome you want, and it figures out how to get there.
- aidnn looks at your data and your prompt together. If you reference a file, sheet, or table by name, aidnn uses that. If you don’t, it makes its best guess based on what’s available.
- aidnn verifies its own work. Most analytical outputs go through a verification step before you see them. This is a deliberate design choice — it’s what makes aidnn outputs reliable enough to bring into executive conversations.
- aidnn works best within a focused session. A single notebook session is where aidnn carries context. Starting a new notebook is a fresh start.
1. Lead with the goal, not the steps
Tell aidnn the outcome you want — not the steps to get there. This is the most important rule of thumb to keep in mind. When you over-specify the steps, you can accidentally constrain aidnn in ways that produce a worse answer than if you had simply stated what you wanted to learn.Example
2. Give context about your goal
Provide aidnn with context around your question so it can orient its analysis and give more accurate, tailored results. The more it knows about what you’re trying to accomplish — and who the output is for — the better the response.Example
Without context, aidnn doesn’t know whether to focus on trends, outliers, costs, compliance, or something else entirely. A single spreadsheet can be analyzed dozens of different ways — telling aidnn the purpose helps it pick the right one.
3. Be specific about data sources
aidnn does its best to figure out which data to use, but if you don’t name the source clearly, it falls back on assumptions based on file or table names. Sometimes those assumptions are right; sometimes they’re not. The fix is to be explicit.Example
How aidnn interprets naming conventions
aidnn uses naming patterns to infer what a table is for:- A _mapping table (e.g.,
dept_to_exec_mapping) — aidnn assumes it maps values from one field to another and joins it automatically. - An _enriched table — aidnn assumes the mapping has already been applied and treats it as cleaned, ready-to-analyze data.
- A _raw or _source table — aidnn treats this as untransformed input and may try to enrich it before analyzing.
4. Specify quantities and boundaries
Tell aidnn exactly how many items, rows, or time windows you need. This avoids over- or under-reporting and reduces follow-up corrections.Example
When you know the right number of buckets, date ranges, or groupings, state them upfront. It dramatically reduces the number of “redo” messages.
5. The rules are easy — the exceptions are where detail matters
The core logic of most analyses is straightforward — sum this, divide by that, compare year over year. aidnn handles the standard rules well. Where users get tripped up is the exceptions: the company-specific quirks of how your business actually books, categorizes, or reports things.Examples of exceptions aidnn won’t know unless you tell it
- How your company defines “active” customers (paid in last 30 days? Has a contract? Has logged in?)
- Which GL accounts to include or exclude in OPEX (e.g., do you exclude one-time restructuring charges?)
- What counts as “large” — top 10 by spend? Anything over $1M? Anything strategic regardless of dollar size?
- Whether intercompany eliminations have already been applied to the data you’re uploading
- How fiscal periods map to calendar periods (especially for 4-4-5 calendars)
- Whether bookings are gross or net of discounts in your reporting
Example
When you’re running a standard analysis, aidnn’s defaults are usually fine. When the analysis depends on a company-specific definition or carve-out, state that exception in the prompt. Otherwise aidnn will apply the textbook rule.
6. Structure complex requests step by step
When your request involves multiple steps — pulling from different tabs, cross-referencing files, applying business rules — break it down into a clear, numbered sequence rather than writing one long paragraph.Example
When you pack multiple instructions into a single block of text, details get lost. Numbered steps help aidnn execute each part in order without missing anything.
7. Building dashboards and spreadsheets
Dashboards and structured spreadsheets are a common aidnn use case. A few specific tips:Be explicit about the structure you want
If you want a single consolidated output, say so. If you want multiple deliverables, call that out specifically. aidnn handles “give me one consolidated workbook with these tabs” better than open-ended requests.“Build a single Excel workbook with two tabs: Tab 1 is a master tracker showing all contracts with vendor, value, start date, end date, and owner. Tab 2 is an expiration dashboard showing only contracts expiring in the next 90 days, sorted by expiration date.”
Think in views, not just tables
Dashboards work best when each tab or view has a clear purpose. Instead of “build a dashboard,” try:“Build a dashboard with three views: a summary view showing totals by category, a detail view showing the full record list sorted by value, and an exception view flagging anything expiring within 30 days or exceeding a spend threshold.”
Specify sort order
If you want results sorted a particular way — highest spend first, soonest expiration first, alphabetical — say so explicitly. “Sort by [column] descending” is the clearest phrasing.Specify formatting in one consolidated request
Bundle formatting instructions together rather than spreading them across follow-ups. aidnn handles formatting most reliably when stated clearly up front.“Format dollar columns as currency with no decimals. Bold the totals row. Highlight any contract expiring in the next 30 days.”
Keep formatting simple to start
If you’re new to aidnn, start with the structural and analytical content first, then layer in formatting. Get the right data into the right shape, then refine the visual presentation.8. One ask per turn after the first
Your first prompt can (and should) describe the full outcome. After that, iterate with one focused request per turn. Combining unrelated requests in a single follow-up leads to messier, less verifiable output.Example
Each turn builds on the last. aidnn keeps full context within a session, so you don’t lose anything by going step by step.
9. Iterate — don’t start over
When you’re close but need adjustments, make targeted follow-up requests instead of asking aidnn to “redo” everything. Small, specific tweaks are faster and preserve the work already done.Example
If aidnn goes in the wrong direction, you don’t have to start over. Use Delete to remove a result and everything after it, then try a different prompt from that point — you keep all the good work before it. Or use Duplicate to copy the session and take it in a new direction without losing the original.
10. When aidnn gets it wrong
- One-line correction first. “The West region should map to Lisa, not James — please re-run.” Cheapest, fastest. aidnn fixes and continues.
- Say “Verify it.” aidnn runs verification on most outputs automatically, but you can also explicitly invoke it as a follow-up. Especially useful after adjustments or before sharing.
- Ask it to show its work. If a number looks off, ask: “The report shows 320 contracts due in 0–30 days — that seems high. Walk me through how you calculated that.” Other useful follow-ups: “What data did you use?”
- Flag platform issues. If something feels like a bug — files not loading, formatting broken, scheduled runs not firing — flag it to your account contact. Those are platform issues, not prompting issues.