Haiku 5.5 data extraction

Haiku 5.5 data extraction is useful when a source contains recognizable facts but does not arrive as a clean database row. The important question is whether each returned field can be traced to the input. A well-formed object with an invented invoice number is still a failed extraction, even if every downstream parser accepts it.

Start with one record type and an explicit missing-value policy. This page uses a synthetic invoice note so you can inspect the expected facts without uploading customer data. The example is a test fixture, not a model-generated result or a claim about measured extraction accuracy on this website.

Define the Haiku 5.5 data extraction contract

Choose the fields your next step genuinely needs. Each field should have a meaning, a type and a rule for absence. An invoice total, for example, should specify whether it includes tax. A payment date should distinguish an actual payment from a proposed due date. Ambiguous field names force the model to make business decisions you have not written down.

Use a separate state for missing or conflicting evidence. An empty string, zero and an unknown value mean different things. If a purchase-order number is absent, returning zero can look like a real identifier to another system. If two totals conflict, selecting the more plausible one hides a review problem.

Decide whether you want literal extraction or normalization. Keeping the source spelling makes auditing easier; converting a date into a standard format makes later processing easier. You can retain both. That lets a reviewer verify that the normalized value follows the intended locale rather than a guess about how the original author writes dates.

Use a record with a deliberate trap

Here is the synthetic source for an initial Haiku 5.5 data extraction test. It includes an invoice identifier, a total and a misleading number that belongs to a different concept. The absence of a purchase-order number is intentional.

Supplier: Example Paperworks
Invoice reference: INV-1042
Issued: 2026-10-02
Amount due: USD 248.00, tax included
Previous quote: Q-1042 for USD 230.00
Payment terms: due 30 days after issue
Contact note: Please include our invoice reference with payment.

The accepted result must preserve the invoice reference and the amount due. It must not substitute the quote amount, infer a purchase-order number from the similar identifier or claim that payment has already occurred. A due-date calculation, if your application needs one, should be a separately specified transformation with a defined calendar rule.

This fixture is intentionally small. A small test makes the failure understandable. Once the basic distinction works, add realistic formatting changes: reorder the lines, introduce a forwarded message or include an older quote above the invoice. Keep the expected facts fixed so you can see whether presentation changes alter the extraction.

Require evidence in Haiku 5.5 data extraction

Use a prompt that gives the field definitions and requires source evidence. The following is sample input you can copy into a text workspace. It does not execute a database write or import anything automatically.

Extract invoice_reference, supplier, currency, amount_due and purchase_order.
Use only the supplied source. Return null for a missing field.
For each non-null field, include a short exact evidence span from the source.
Do not treat a quote as an invoice or a due date as a payment date.
List conflicting values separately rather than resolving them by guesswork.
Source:
[paste the synthetic invoice note]

Evidence spans help reveal an error that a value-only response can conceal. If the model returns the correct amount but cites the old quote, the result is not trustworthy simply because the number happened to match another line. Check both the value and the connection between the value and its supporting passage.

For longer documents, add stable passage or page identifiers before submitting the text. Do not ask the model to invent a page number for a plain pasted paragraph. If preprocessing removes the original pagination, retain your own mapping so that citations remain meaningful when a reviewer returns to the source file.

Validate Haiku 5.5 data extraction results

In an API application, a structured-output feature can constrain the response format where the selected provider supports it. Anthropic documents the native feature in its structured-output guide. The browser workspace on this site should not be assumed to expose every native API option.

After parsing, validate allowed fields, data types and required evidence. Then apply business checks. A currency should be an allowed value for your workflow. An amount should meet the record's stated precision rules. A source reference should point to text that actually exists. These checks catch different problems and should produce different error messages.

Avoid silently repairing every invalid result. Trimming harmless whitespace may be acceptable under a written normalization policy, while replacing an unknown currency with a default changes the meaning. Preserve the original result when a reviewer needs to understand the failure, subject to your data-retention rules.

Measure field errors before record accuracy

Score each field against an independently prepared answer key. Track wrong values, missing values and unsupported additions separately. A model that extracts every field but invents absent information needs a different correction from one that abstains too often. A single record-level pass rate cannot explain that difference.

For Haiku 5.5 data extraction, include clean records as well as difficult ones. If every fixture contains a trap, the prompt may become overly cautious on ordinary invoices. If no fixture contains missing data, you will not learn whether the application can distinguish absence from an accidental parsing failure.

Review disagreements between human labelers. If two people cannot agree on whether a line represents a subtotal or a final amount, the field definition may need work. Resolve that ambiguity before using the example to declare the model wrong. Otherwise your evaluation measures inconsistent instructions as much as extraction behavior.

Batch Haiku 5.5 data extraction safely

Assign each input a stable ID outside the model. Keep its result associated with that ID rather than relying on response order or a copied customer name. When processing several records, an answer from one document must never be attached to another merely because one request completed earlier.

Decide whether one request contains one record or several. Multiple records can reduce repeated instructions, but they also create opportunities for cross-record contamination. Test two similar invoices with different totals and nearly identical references. The model should not merge their fields into a plausible composite that belongs to neither source.

Handle each result independently. A malformed record should not require rerunning an entire accepted batch unless your protocol makes that unavoidable. Record attempt counts and validation outcomes so a repeated job does not create duplicate rows. This website's comparison workflow is a place to evaluate text tasks; it is not a connection to your production database.

Treat document instructions as data

An invoice may contain a sentence that resembles an instruction to the model. Keep source material clearly separated from application rules, and validate the returned fields even when the source asks for a different format. A document author should not gain authority over your extraction process simply by writing imperative text.

Add a benign adversarial fixture that asks the model to ignore the schema and return an unrelated phrase. The expected result still follows your field contract or flags an unsupported input. This test checks the workflow boundary without requiring real private documents or dangerous content.

Limit what happens after extraction. Human review may be appropriate before a value affects billing, account access or another consequential action. The model's output should be a candidate record with provenance, not an automatic authorization to pay an invoice or change a customer account.

Decide whether the result is worth using

Compare accepted records, review time and total usage, including repair attempts. Haiku 5.5 data extraction can be economical only if the saved work exceeds the cost of checking and correcting it. A low API price does not compensate for hidden manual cleanup or hard-to-trace field errors.

Try the synthetic fixture in Chat, then use Compare to inspect another available model on the same contract. Keep the acceptance checklist fixed. The useful outcome is an extraction procedure you can audit, with a clear boundary between an accepted field, an unknown field and a record that needs a person.