I'm building a product that needs structured extraction from messy text

Great use case. If you’re extracting structured data from messy text, the main challenges are usually:

1. Inconsistent formatting

   • Missing labels
   • Extra whitespace / punctuation
   • Mixed ordering of fields
   • Multiple records in one blob

2. Ambiguity

   • A value may match more than one field
   • Some fields may be implicit rather than explicit

3. Reliability

   • Need deterministic output shape
   • Need confidence/error handling
   • Need validation against a schema

4. Evaluation

   • You’ll want precision/recall for each field
   • You’ll need a gold dataset with messy real-world examples

A practical approach is usually:

1) Define a strict schema

Example:

{
  "name": "",
  "email": "",
  "phone": "",
  "company": "",
  "address": ""
}

2) Normalize the input first

• Remove weird line breaks
• Standardize unicode
• Strip boilerplate
• Preserve original text for traceability

3) Use extraction rules or an LLM, depending on complexity

• Rules/regex: best for highly regular patterns
• LLM: better when text is unstructured or varied
• Hybrid: often best in production

4) Validate output

• Type checks
• Required field checks
• Format checks (email, phone, dates)
• Cross-field logic if needed

5) Return confidence / provenance

Useful output:

{
  "name": {
    "value": "Acme Corp",
    "source_text": "Acme Corp, Inc.",
    "confidence": 0.92
  }
}

6) Build a fallback strategy

• If extraction fails, retry with a different prompt/rule
• Flag for human review
• Keep logs for improvement

If you want, I can help you design:

• a schema
• a prompt for LLM-based extraction
• a regex/rules pipeline
• or an evaluation framework for accuracy on messy text

If you share an example of the messy text and the fields you want, I can propose a concrete extraction strategy.