The useful starting point
Define success with representative documents and real questions from the team. Extraction and search should be evaluated together with the work they support.
Collect examples before choosing an approach
Start with a small, representative set of documents and the information people need from them. Include awkward examples, not only the cleanest file. A consistent report, a scanned page and a document with complex tables can require different handling.
Describe the task in ordinary language: finding a passage, locating a document or extracting particular fields. Those are different needs. The first version should make clear which of them it supports.
Start with an example people can judge
Illustrative example · a constructed document and expected fields; no client document is reproduced
Consider a small sample document with a reference, date, currency, total and two line items. Agree which fields matter before trying an extraction tool. An example can be simple enough to inspect and still reveal several different failure modes.
SAMPLE DOCUMENT
Reference: DEMO-104
Currency: GBP
Item A: 120.00
Item B: 80.00
Total: 200.00| Task | Expected handling | Problem this can expose |
|---|---|---|
| Extract the reference | Preserve DEMO-104 as text | Changing an identifier while normalising output |
| Extract the total | Return 200.00 with GBP as a separate, explicit value | Losing the currency or confusing a line item with the total |
| Find the document | A search for DEMO-104 leads to its content and source | A useful value is extracted but cannot be retrieved |
| Inspect a doubtful result | The user can compare it with the original file | An answer appears confident without a way to check it |
This is an acceptance exercise, not an accuracy benchmark. A real evaluation needs representative files from the intended document set, including the variations that make the work difficult.
Choose the retrieval approach from the question
| Need | A starting option to evaluate | Evidence to collect |
|---|---|---|
| Find an exact reference | An indexed field or exact-text lookup | Can the complete identifier be found reliably? |
| Find a passage containing known words | Text search with a source preview | Can users reach the relevant passage? |
| Narrow a collection by known attributes | Structured fields and filters | Are the necessary fields available and consistent? |
| Find related meaning without exact wording | Evaluate semantic retrieval against other options | Do representative questions return genuinely useful source content? |
These approaches can be combined, but each introduces its own work to build and evaluate. Starting with the questions avoids paying for an elaborate search method when an exact lookup would solve the important task.
Design a result someone can inspect
A useful result might show the source name, the relevant passage or field and a route back to the document. The appropriate details depend on the task and access requirements.
- 1Choose representative documents
- 2Extract the required information
- 3Index what needs to be found
- 4Show a result with its source
Keep extraction, retrieval and review distinct in the evaluation. A system can succeed at one stage and fail at another. That distinction makes feedback actionable: the team can identify whether the problem is missing text, a weak query result or an unhelpful presentation.
Treat imperfect output as part of the workflow
Ask what should happen when information is missing, ambiguous or read incorrectly. Depending on the task, the user may need to inspect the original document, correct a value or mark the file for another look.
These are design choices to make during scoping. A technically successful extraction request does not, by itself, tell a person whether the resulting information is complete or reliable enough to use.
Evaluate search with questions people actually ask
Give the proposed system the kind of query someone would use at work. Can they find the right content? Can they tell why the result is relevant? Is it easy to return to the source document?
Do not select an approach only because it includes a fashionable label. Keyword search, structured filters and other retrieval methods have different strengths. Choose against the documents and tasks, then test the choice.
Define the team requirements early
List who supplies documents, who searches them and who maintains the collection. If different people should see different material, make that a requirement of the workflow from the beginning.
- Which document types are in scope?
- Which information must be extracted?
- How will a user inspect or correct a doubtful result?
- What does a useful search result contain?
- Who should be able to find and open each document?
These questions help identify dependencies before the team commits to an interface or extraction service.
What our document project brings together
Our document-workflow project includes extraction, search and authentication in the backend, alongside a React administrative interface. Those are distinct parts of making documents useful to a team: getting information out, finding it again and providing an interface through which the work can be managed.
This is why the acceptance examples in this guide treat extraction, retrieval and inspection separately. They produce more useful feedback than asking whether a demonstration looks like “document AI”. The project account explains the recorded scope without attaching an unsupported model choice or accuracy result to it.
Common questions
Do we need AI for PDF extraction?
The document types and task should determine the approach. Some work can be handled through structured parsing, text extraction or exact search. Other material may justify additional methods, which should be evaluated against representative examples.
How should we measure extraction quality?
Define the fields or answers that matter, label representative examples and inspect errors by type. Report the document set and method alongside any result; a percentage without that context is difficult to interpret.