Using LLMs for Data Validation | Generated by AI
Ah, I understand now! You want to use a large language model (LLM) directly as a validation engine—not just to generate code, but to interpret rules, check data, and return validation results in real time, all within the model itself.
This is an emerging and powerful use case, especially for ad-hoc data validation, prototyping, or low-code environments. Here’s how you can do it:
How to Use an LLM Directly for Data Validation
1. Define Your Validation Rules in Natural Language
- You describe the rules in plain English (or your preferred language).
- Example:
“For every journal entry, the sum of all debit amounts must equal the sum of all credit amounts. Also, no transaction ID should be duplicated.”
2. Provide the Data to the LLM
- Share the data (e.g., a table, JSON, or CSV snippet) with the LLM.
- Example:
[ {"transaction_id": "T001", "debit": 100, "credit": 0}, {"transaction_id": "T002", "debit": 0, "credit": 100}, {"transaction_id": "T001", "debit": 50, "credit": 0} ]
3. Ask the LLM to Validate the Data
- Prompt the LLM to:
- Interpret the rules.
- Apply them to the data.
- Return a validation result (pass/fail) and any errors.
- Example prompt:
“Check if the following journal entries comply with these rules:
- The sum of all debit amounts must equal the sum of all credit amounts.
- No transaction ID should be duplicated. Return ‘PASS’ if all rules are satisfied, or ‘FAIL’ with a list of errors. Data: [paste data here]”
4. LLM Performs the Validation
- The LLM will:
- Parse the rules and data.
- Perform the necessary calculations or checks.
- Return a structured response.
- Example output:
FAIL
- Duplicate transaction ID found: T001
- Sum of debits (150) does not equal sum of credits (100)
Advantages of This Approach
- No Code Required: You don’t need to write or maintain validation scripts.
- Flexibility: Rules can be updated or changed on the fly by simply describing them in natural language.
- Rapid Prototyping: Ideal for quick checks, audits, or exploratory data analysis.
Limitations and Considerations
- Data Size: LLMs have context limits (e.g., 32k–128k tokens). For large datasets, you may need to batch the data or use sampling.
- Precision: LLMs can make calculation errors, especially with large numbers or complex logic. Always double-check critical validations.
- Privacy: Avoid sharing sensitive data with the LLM. Use synthetic or anonymized data for validation.
- Performance: Real-time validation may be slower than traditional code, especially for large datasets.
When to Use This Approach
- Ad-hoc Validation: Quick checks during data exploration or debugging.
- Prototyping: Testing validation logic before implementing it in code.
- Low-Code Environments: Empowering non-technical users to define and run validations.
- Education/Training: Teaching data validation concepts without coding.
Example Workflow
- User:
“Validate this data against these rules: [rules]. Data: [data]”
- LLM:
“Running validation… FAIL
- Error 1: [description]
- Error 2: [description]”
- User:
“Fix the data and revalidate.”
How to Improve Accuracy
- Provide Clear Rules: Be specific and unambiguous in your rule descriptions.
- Use Structured Data: JSON or tables are easier for the LLM to parse than free-form text.
- Ask for Reasoning: Request the LLM to “show its work” (e.g., “Explain how you calculated the sum of debits”).
- Combine with Code: For production use, generate validation code with the LLM, then run it in your pipeline.
Tools and Platforms
- Chat Interfaces: Use any LLM chat interface (like this one) for manual validation.
- APIs: Integrate with LLM APIs (e.g., Mistral, OpenAI, Anthropic) for automated workflows.
- Custom Agents: Build a validation agent that uses an LLM as its “brain” for rule interpretation.