AI series · Part 1: GenAI and Knowledge Modeling
SHACL: Validation Rules That Keep LLM-Extracted Data Honest
SHACL is a set of validation rules for knowledge graph data, like form validation for facts. It catches an LLM's extraction mistakes before they are stored.
SHACL (pronounced "shackle") is a language for writing validation rules for knowledge graph data. Think of an online form that says "Email is required" or "Phone must be 10 digits". Data that breaks the rules is rejected before it is stored.
Why it matters more in the GenAI era
Banks already validate KYC data: every customer must have exactly one PAN and a valid date of birth. The same idea becomes critical when an LLM extracts facts from documents into a knowledge graph. LLMs make mistakes, and SHACL catches them before they are saved.
The rules in our HR assistant
- Every employee needs exactly one valid email
- Every employee must belong to a department
- Sick leave balance must be between 0 and 12
We then fed it a bad record for Vikram:
| Field | Value | Result |
|---|---|---|
| vikram-at-acme | ❌ Not a valid email | |
| department | (missing) | ❌ Required |
| sick balance | 40 | ❌ Must be 0–12 |
The record is rejected with 3 errors, and none of it reaches the graph.
The ontology defines the model, SHACL validates that the data follows it, and the KG stores only validated facts. That keeps LLM-extracted data trustworthy.
- The short version
Next in the GenAI series: RAG Explained: Give Your LLM an Open-Book Exam
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We deploy these patterns across production engagements in fintech, healthcare, and eCommerce. Talk to a senior engineer about how they apply to your platform.