Turn messy text into structured data you can trust enough to feed another system — bound the source, extract the fields, force clean JSON, and validate before it flows downstream.
Extraction looks easy until you wire it into something. Ask a model to pull the fields and you get JSON most of the time, prose around it some of the time, a hallucinated value when the data is missing, and a format that drifts the moment the input changes. None of that survives contact with a downstream system that expects the same shape every call. Reliable extraction is a short pipeline: give the model a clean, bounded source, tell it exactly what to pull and what to do when a field is absent, force a strict output shape, and check the result before you trust it.
Each step uses an existing NewPrompt tool, pre-filled by a matching resource. Open the resource to read it, or jump straight into the tool with the inputs ready.
Bound the source
For anything longer than a snippet, delimit the source so the model can't mistake the content for instructions — the classic extraction failure. A bounded source is the difference between extracting and improvising.
Define exactly what to pull
Specify the fields, their types, and — crucially — what to do when a value isn't there. 'Leave it null, don't guess' is the instruction that prevents invented data.
Force a strict JSON shape
Constrain the output to clean JSON with no prose wrapper, so a parser downstream gets the same structure on every call.
Validate before it flows on
Check the output against the expected schema and catch the drift — a missing field, a wrong type, an invalid value — before it reaches the system that depends on it.
Unstructured text becomes structured, schema-valid data that holds its shape call after call — safe to hand to a parser, a database, or the next step in an automation, instead of something you eyeball every time.
An extraction prompt handles the middle step alone; this workflow wraps it with what makes extraction survive production. It bounds the source so content can't read as instructions, forces a strict JSON shape, then validates the result against your schema before it feeds a parser, database, or automation downstream.
Forcing JSON shapes the output; validating confirms it. Models still drift — a missing field, a string where a number belongs. The validation step catches that before a parser chokes on it, and hands you a repair prompt when it happens.
Step 2's explicit missing-data rule. Telling the model to return null instead of guessing is the single biggest defense against hallucinated extraction, and the validator in step 4 flags it if it slips through.
Structured, schema-valid JSON that holds the same shape call after call — safe to hand straight to a parser, database, or the next automation step instead of eyeballing it each time. It's not a finished pipeline; you run the prompts, and step 4's validation helps catch drift before it flows on.
The raw messy text you want fields pulled from, plus a field spec: each field name, its type, and what to do when a value is absent. Step 2 pins that down — leave it null, don't guess — and step 1 delimits the source so the model treats it as data, not commands.
Rerun step 4's validation to see exactly what's off — a missing field, a wrong type, an invalid value — then use the repair prompt it hands back. If prose wraps the JSON, tighten step 3's strict-shape constraint; if a value is invented, re-check step 2's null rule.
Complete build journeys that include this workflow as a stage.
The full path to an AI document processing system — define the use case, design the intake pipeline, extract fields from unstructured documents, classify and route them, pin the output contract, evaluate accuracy, then ship it monitored.
The full path to an AI content moderation system — define the policy and label taxonomy, extract signals from user content, classify it against policy, emit structured decisions, evaluate false positives and negatives, wire enforcement and review queues, review abuse risks, then ship.
The full path to an AI research assistant — define its scope, organize the source corpus, ground responses in references, extract key facts, synthesize findings, check groundedness, then validate it for use.
The full path to an AI meeting assistant — define the use case, turn transcripts into structured notes, extract decisions and action items, classify follow-ups, write a shareable summary, evaluate accuracy, then ready it for the team.
The full path to a retrieval system that returns grounded answers — understand the corpus, chunk and ground it, extract and classify the metadata, then evaluate that retrieval actually works.
The full path to pages that rank at scale, not penalty bait — map the intents, build the data set, structure it, template the page, then QA before publishing hundreds.
The full path to an applicant tracking system — model jobs, candidates, and hiring stages, generate job descriptions and screening prompts, parse résumés into structured data, design the hiring API, set roles, review security, then ship.
The full path to knowledge that's findable by people and AI — plan the taxonomy, structure it for search, write the articles, tag the metadata, make it retrievable, then ship it maintainable.
The full path to a pipeline that moves data without corrupting it — design the ingestion and transforms, extract and structure the sources, gate the quality, store it, then deliver and ship it monitored.
When a source is missing a field, AI tends to fill the gap with a plausible guess instead of saying it isn't there. Here's how to make the model mark missing data explicitly — and check the result before you trust it.
AI pulls the fields you asked for, but hands back a flat list with no way to tell which values it read from the document and which it guessed. Here's how to make each extracted value carry its source quote, location, and a review flag — so you can check the result instead of trusting it.
Ask AI to extract fields with "use null if missing" and one null means missing, false, a stated "none", and a real zero — valid JSON, wrong meaning. Handle unknown vs empty vs not-applicable in AI output: give each field a value state so absence gets classified, not collapsed.
Paste a messy export and "classify these rows" and the AI splits one bug into three categories, mixes currencies, and counts three duplicate rows as three problems — silently. Normalize messy inputs before AI processes them: standardize the formats and flag the ambiguous cases.
Make any AI task return JSON your code can rely on — define the schema, force the model to it, validate every response, and diff the drift when a model update breaks the shape.
Build a text classification step you can automate on — pull out the unit to classify, assign a label from a fixed set, and validate the label is one you actually allow.
Get AI to actually read a document that's too big for one prompt — fit it to the model, split it cleanly, package the parts, and analyze them without losing the thread.
Design a pipeline that moves data without corrupting it — map the sources and ingestion, design the transformation stages, set validation and quality gates, then document the pipeline and monitoring.