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AI is useful for navigating the federal budget, not for certifying what the government spent or deciding whether a policy is wise. Used with authoritative budget tables and careful definitions, it can sort thousands of pages, summarize program descriptions, extract comparable figures, and flag unusual patterns for a human to investigate. Its answer is only as reliable as the documents, data definitions, and analysis design supplied to it.
This guide shows a reproducible workflow: define a checkable question, select the official record that answers it, preserve metadata, use AI for organization and questioning, recompute figures independently, and report every material limitation.
What the federal budget actually measures
The U.S. federal fiscal year runs from October 1 through September 30. The annual budget is commonly explained in three spending areas:
| Area | What it covers | USAGov’s rounded description |
|---|---|---|
| Mandatory spending | Programs whose funding is generally determined by eligibility rules and permanent law. | Typically more than half of annual spending. |
| Discretionary spending | Funding lawmakers set through annual appropriations acts. | Usually around one-third. |
| Interest on the debt | Interest payments on federal borrowing. | Usually less than 10 percent. |
Those percentages are rounded explanatory characterizations on the current USAGov page, not a substitute for a dated budget table. A serious analysis should cite the fiscal year, accounting measure, and table used.
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Proposal, appropriation, obligation and outlay are different
The budget process starts roughly a year before a fiscal year. Agencies send requests to the Office of Management and Budget (OMB); OMB develops the President’s budget proposal; the President submits it to Congress early in the next calendar year; congressional committees and subcommittees consider funding; and Congress sends appropriations bills to the President. A proposal is a plan, not enacted law.
An appropriation is legal authority Congress provides for an agency or purpose. An obligation is a commitment to pay, such as a signed contract or grant award. An outlay is money actually disbursed. A single program can therefore show an appropriation, obligations and outlays that differ in amount and timing. Never ask an AI system to compare a President’s proposed amount with an agency’s outlays as if they were the same measure.
Where AI helps—and where it does not
Good uses
- Build an index of budget volumes, appendices, tables and program names.
- Summarize a supplied justification while retaining page or table references.
- Extract the same field from many agency documents into a reviewable spreadsheet.
- Translate technical budget language into plain English without changing the cited definition.
- Suggest like-for-like comparisons and identify missing years, units or categories.
- Flag abrupt changes or unusual relationships as leads for human investigation.
GAO has described potential federal benefits from generative AI such as better written communications, more efficient information access and program-status tracking. In April 2025 testimony, GAO wrote: “AI-enabled tools could also help by sifting through large volumes of data to find anomalous patterns or relationships.” The same testimony cautioned that these uses require high-quality data and a skilled workforce.
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- Treating a fluent answer as evidence that a number appears in an official table.
- Asking a model to decide whether spending is wasteful, fraudulent or justified from a single anomaly.
- Combining figures with different fiscal-year bases, definitions or reporting systems without adjustment.
- Using generated citations without opening the underlying document.
- Uploading nonpublic procurement, personnel or personally identifiable information to an unapproved service.
GAO’s warning is direct: “Introducing insufficient, unrelated, or bad data will make an AI model less reliable.”
Choose the source that answers your question
Start with the record appropriate to the claim you want to make. The source choice is more important than the model.
| Question | Primary material to use | What the result represents |
|---|---|---|
| What does the administration propose? | The President’s budget and an agency’s budget justification. | A proposed plan, subject to congressional action. |
| What did Congress authorize or appropriate? | Enacted appropriations acts and congressional explanatory materials. | Legal funding authority and related directions. |
| What awards or transactions were recorded? | Treasury’s USAspending.gov and the underlying agency reporting. | Reported obligations, outlays and award or transaction information, with the site’s stated coverage and limitations. |
Keep the source file or download, the agency, fiscal year, extraction date, table or dataset name, units, category definitions and any disclosure about missing or unlinked records. Those details let another reader reproduce your result.
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A reliable AI-assisted workflow
1. Turn the idea into a checkable question
Replace “Where does the money go?” with a bounded question such as: “How did the Department of X’s reported obligations in program Y change between fiscal years 2022 and 2024?” Specify the agency, program or account, fiscal years, measure, currency units and whether you want proposed, enacted or recorded spending.
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2. Gather the authoritative documents
Download the relevant budget volumes, enacted law or USAspending.gov data. Record the publication or update date. If a document contains several tables, note the exact table title and page. Do not let an AI system silently fill a missing year or infer that similarly named accounts are identical.
3. Inspect definitions before extraction
Read the glossary, footnotes and methodology. Confirm whether amounts are nominal dollars, constant dollars, obligations, outlays, budget authority or another measure. Check whether a category includes transfers, classified activity, revolving funds or shared services. Give those definitions to the model along with the data.
4. Ask AI to structure supplied material
Use a prompt that limits the model to your files:
Using only the supplied documents, create a table with agency, fiscal year, account, measure, amount, unit, source table and page. Quote the definition or footnote for each measure. If a field is absent or ambiguous, write “not stated” and explain what must be checked by a human. Do not calculate totals.Require a source location for every extracted claim. A model may invent a plausible page number, so open the page yourself and mark each row as verified or unverified.
5. Recompute arithmetic outside the model
Use a spreadsheet or a small deterministic script for additions, percentage changes and unit conversions. For a year-over-year change, calculate (new amount − old amount) ÷ old amount × 100 only after confirming that the two amounts use the same measure, scope, units and fiscal-year basis. Preserve the original values alongside the computed result.
6. Investigate anomalies, do not label them misconduct
A sudden increase can reflect a new contract, an emergency supplemental appropriation, a change in reporting standards, a reclassified program or late data. Ask the model for possible explanations and documents to inspect, then verify each explanation in agency statements, congressional material or the underlying award record. An anomaly is a research lead—not proof of fraud, waste, intent or an incorrect payment.
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7. Write an evidence-bounded finding
State what was measured, the period and source, what the data omit, and what AI did. For example: “The downloaded table shows reported obligations rising between fiscal years 2022 and 2024; the comparison uses nominal dollars and the same account code. The model extracted the rows, while the arithmetic was independently recomputed. The result does not establish why the increase occurred.”
Working with USAspending.gov data
USAspending.gov presents information from agency financial systems and external government-wide reporting systems. It is a valuable public starting point for awards and transactions, but it is not a guarantee that every record is complete, linked or directly comparable.
GAO reported that, for the period ending March 31, 2021, 83 of 101 agencies submitted data files on time. Of the agencies that submitted data, 19 of 101 omitted a required file linking budget and award information. GAO also found hundreds of billions of dollars in obligations reported with program activity listed as “Unknown/Other.” These are historical findings for that reporting period, not a measurement of current completeness.
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Checks to perform before comparing records
- Confirm the fiscal-year range and the date on which the data were downloaded.
- Check whether the result is an obligation, outlay, award amount, budget authority or another measure.
- Use the same agency, account, program and recipient scope in every period.
- Review “unlinked,” “unknown,” excluded or classified categories.
- Look for changes in definitions, reporting standards or agency system submissions.
- Record whether amounts are nominal or inflation-adjusted; do not mix the two.
Comparing agencies, programs or years
A defensible comparison carries the following attributes with every figure:
| Comparison axis | Question to answer |
|---|---|
| Fiscal year and extraction date | Are both observations from the same fiscal-year convention, and when was each downloaded? |
| Stage of the process | Are you comparing a proposal, enacted funding, obligations or outlays? |
| Measure and unit | Are the figures defined the same way and expressed in the same dollar units? |
| Scope | Do agency, program, account and recipient boundaries match? |
| Completeness and linkage | Could missing files, unlinked awards or “Unknown/Other” activity affect one side more than the other? |
| Definition changes | Did a reporting-standard, account-code or classification change create an apparent increase or decrease? |
If any answer is no or unknown, narrow the claim. “The reported totals are not directly comparable” is more accurate than manufacturing a trend from incompatible records.
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Prompt patterns that keep a model honest
Source-grounded extraction
Tell the model to use only attached documents, return a row-level citation (file, table and page), preserve units and output “not stated” when the source is silent. Ask it to separate verbatim facts from interpretation.
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Definition audit
Ask: “List every definition, exclusion and footnote that could change this comparison. For each, quote the source location and explain which rows it affects.” This turns the model into a checklist generator rather than an authority.
Contradiction check
Provide two official documents and ask the model to identify conflicting totals, dates or account names without choosing a winner. Resolve the conflict by reading the documents’ scope and revision notes.
Question generation
After a model summarizes a table, ask for five follow-up questions a budget analyst should verify. Examples include whether a supplemental law changed the baseline, whether an award was later de-obligated, and whether a category is “Unknown/Other.”
Limits, security and governance
GAO reported that selected agencies’ inventoried AI use cases nearly doubled from 571 in 2023 to 1,110 in 2024; reported generative-AI use cases rose from 32 to 282, about ninefold. These counts cover inventories from 11 selected agencies, not every federal agency, and they do not measure successful deployment.
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- User-Friendly Layout - The budget planner features a user-friendly layout designed for easy navigation and organization. Each month, you'll find dedicated budget pages where you can set financial goals, track your income, and plan your expenses. Additional sections include debt trackers, savings goals, bill payment trackers, and more, making it simple to stay on top of your finances.
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Fraud signals require a separate investigation
GAO cited an estimated $233 billion to $521 billion in annual federal losses to fraud, based on data from 2018–2022. That estimate is not a measured amount uniquely attributable to AI and is not a claim about budget waste. AI can help prioritize records for review, but proving fraud requires evidence about transactions, controls, intent and applicable law. A model’s pattern score cannot supply those elements.
A publication checklist
- Question, agency, program and fiscal years are stated.
- Proposal, appropriation, obligation and outlay are not conflated.
- Every number has a unit, definition, source location and date.
- Calculations were independently recomputed.
- USAspending.gov disclosures, linkage status and “Unknown/Other” records were reviewed where relevant.
- Historical data-quality findings are labeled historical.
- AI’s role is disclosed as extraction, summarization or anomaly triage.
- Unverified explanations and accusations are removed.
- Privacy and organizational-use requirements were followed.
Frequently asked questions
Can AI explain where federal spending goes?
It can organize official categories and explain them in plain language, but the answer depends on whether you mean proposed budget authority, enacted appropriations, obligations or outlays. Specify the measure and fiscal year before asking.
Is USAspending.gov a complete ledger?
It is a public reporting source, not an infallible ledger. Historical GAO work found late submissions, missing linkage files and large “Unknown/Other” obligations; current coverage must be checked in the site’s disclosures and methodology.
Should I trust citations generated by a chatbot?
No. Treat them as pointers. Open the cited budget volume or dataset, confirm the table and footnote, and record the verified location yourself.
What is the safest way to investigate an apparent spending spike?
Recheck scope, units, fiscal-year basis and reporting changes; inspect supplemental laws, awards and agency notes; then describe the spike as a lead until documentary evidence explains it.
Can AI determine whether a program is worthwhile?
It can summarize stated goals, costs and reported outcomes. Judging policy tradeoffs requires value choices, context and evidence beyond a spending table, so the final judgment belongs to accountable human decision-makers.
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