Extracting Structured Data from Messy Text
Pull reliable fields out of emails, notes and forms into clean records you can sort, count and trust
A taste of a lesson
Customers write things like 'deliver next Thursday' and the model gives random dates. How do I fix it?
The model has no reliable idea what 'today' is unless you tell it. Pass the message's received date into the instructions, for example 'This message was received on 2026-05-06 (a Wednesday). Resolve relative dates against that.' Also ask for the original phrase in an evidence field, so a reviewer can see 'next Thursday' beside the resolved date. Add a rule check that the date falls in the future and within a sensible range. Quick test: what date should 'next Thursday' resolve to for a message received on a Wednesday, in your team's usage?
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Design an output schema with types, allowed values, units and nulls
- Write extraction instructions that handle ambiguity and forbid guessing
- Normalise and validate extracted fields with rules and lookups
- Choose between simple patterns, models or both for each field
- Measure per field accuracy on a labelled sample and improve it
Lesson plan
- 1 Designing the schema Define exactly which fields you need and in what form. Start
- 2 Instructions for ambiguity Tell the model how to handle unclear, relative or multiple values. Start
- 3 Rules, patterns and models Use the simplest reliable method for each field. Start
- 4 Normalising and validating Clean and check every extracted record before it is stored. Start
- 5 Measuring accuracy Prove extraction quality with a labelled sample. Start
- 6 Running it in production Keep extraction reliable and auditable over time. Start
Try asking
About this tutor
For operations, sales and admin staff, and builders of automations, who need fields from free text: names, dates, amounts, product codes, addresses or requested actions hidden in emails, chat messages, call notes and form comments. You will learn to design an output schema, write extraction instructions that handle missing and ambiguous information, normalise formats, validate results, and measure accuracy on a labelled sample. We also cover when simple rules or regular patterns beat a model, and how to keep the original text alongside the extracted record.
Reviews
4.5
2 ratingsSample
- Hyun J.Sample
Adding the evidence field made reviews twice as quick and the invented values almost disappeared.
- Beatriz C.Sample
Labelling a hundred examples felt tedious, but per field accuracy showed exactly where to improve. Good method.
About the teacher
I teach operations teams to build AI automations for documents, requests and data that fail safely
9 tutors 316 lessons taught Sample
I work with operations teams who handle volume: invoices, support tickets, forms, contracts and meeting notes. My background is in finance operations and process improvement, so I think about accuracy, audit trails and what happens at month end when something quietly broke two weeks ago. I teach how to put AI steps inside workflows so that extraction, classification and summaries...
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