AI Document Prompts for Analysis and Source Checks

A good AI document prompt does not just ask for a summary. It specifies what to extract, how to handle uncertainty, and what to flag when an answer cannot be supported by the document. Without those instructions, the AI fills in gaps with plausible-sounding text that did not come from the source.

Tools like fenbs let you store tested document prompts and the confirmed source links they produce, so the next analysis session builds on what was already checked. An AI brain approach to document work means keeping the prompts, the findings, and the sources together rather than scattered across sessions.

What document prompts need to do

Document analysis fails in predictable ways when the prompt is too open. The AI summarizes by defaulting to the most prominent headings, not the most important claims for your purpose. It fills gaps rather than flagging them. It presents uncertain inferences as findings.

A document prompt that works needs three explicit instructions: what to look for (not just "summarize"), how to handle anything missing or unclear, and how to present the answer so you can verify each claim against the source.

Prompts for extracting claims

An extraction prompt asks for specific facts rather than a general summary. Name the facts you need and tell the AI to quote the source text for each one.

From the following document, extract:
1. [Fact type 1, e.g. dates and deadlines]
2. [Fact type 2, e.g. named parties and their roles]
3. [Fact type 3, e.g. financial terms]

For each item, quote the relevant sentence from the document.
If the document does not contain an item, write "Not found" rather than inferring.

[Paste document here]

The "quote the sentence" instruction is important. It makes the extraction verifiable: you can check each finding against the original without reading the whole document again. "Not found" is more useful than a guessed answer because it tells you where your analysis has a gap.

Prompts for comparing documents

A comparison prompt needs to tell the AI which dimensions to compare and how to present the result so you can act on it. A list of differences is not the same as a structured comparison.

Compare Document A and Document B on the following dimensions:
1. [Dimension 1, e.g. payment terms]
2. [Dimension 2, e.g. termination clauses]
3. [Dimension 3, e.g. liability limits]

Present the result as a table with one row per dimension and a column for each document.
For any dimension where the documents agree, note that they agree.
For any dimension not addressed in a document, write "Not addressed".

[Paste Document A]
---
[Paste Document B]

A structured comparison makes the key differences scannable. It also surfaces gaps: a clause that appears in one document but not the other shows up as "Not addressed" rather than being quietly omitted from the summary.

Prompts for flagging unsupported answers

The highest-risk document prompt is one that asks "what does this document say about X?" without an uncertainty instruction. If the document says nothing about X, the AI may answer anyway using background knowledge rather than the source text.

Answer the following question using only the text of the document provided.
If the document does not address the question, say "The document does not address this" and stop.
Do not use background knowledge.

Question: [your question]

[Paste document here]

Add this instruction to any prompt where the distinction between "the document says" and "I know" matters, including legal reviews, compliance checks, and research summaries where the source is the evidence.

Source check prompts

After any document analysis, a source check prompt asks the AI to list the claims it made and where each one came from. This is a separate step from the analysis itself.

Review your previous response.
List each factual claim you made.
For each claim, identify the sentence or section of the document it came from.
For any claim that is your inference rather than a direct statement in the document, mark it [INFERRED].

Running this after a complex analysis often surfaces inferences you did not notice in the original response. An inference is not necessarily wrong, but you want to know it is an inference before acting on it.

Confirmed source links from document analysis belong in fenbs under Links and listings for the project. A reviewed contract clause, a confirmed policy section, or a verified data point is more useful when it is saved with its source than when it exists only in a chat history.

How to do this with fenbs

1. Create a free account and add a project for the document work.
2. Add the extraction, comparison, source-constrained, and source-check prompt templates to Skills and prompts in fenbs.
3. After each analysis session, save confirmed source links under Links and listings in the project.
4. Connect your AI app over MCP from Integrations so it reads the current prompts and confirmed links before starting each analysis.
5. Record any decisions about which source to trust when sources conflict in Decisions and rules, so the next session does not re-open a settled question.

For document work that involves multiple tools, see system prompt examples for patterns that set the right constraints across different AI interfaces.

When to involve a person rather than the AI

AI document analysis works well for extraction, comparison, and initial review. It is not a substitute for professional judgment on legal, financial, or medical documents. The AI can surface claims and gaps; a qualified person interprets their significance. Flag anything the AI marked as uncertain for human follow-up before acting on the finding.

Keep a record of which prompts worked reliably on documents similar to yours. An extraction prompt tuned for one contract structure may need adjustment for a different layout or jurisdiction. The time to discover that mismatch is before acting on the output. Save the tested version in fenbs with a brief note describing the document type it was developed on, so the next person using it knows which cases it covers.

For project management and planning documents, prompts for project management ai covers prompts tuned for that document type. For a broader overview of AI assistant capabilities at work, what is cursor ai shows how an AI-embedded development tool approaches context and source management.