Module 15 Lesson 3: Guardrails AI & Logic
Validation at the gate. Learn how to use the 'Guardrails AI' framework to enforce structural and factual constraints on LLM outputs.
Module 15 Lesson 3: Guardrail AI and programmatic controls
Guardrails AI (a specific framework, often confused with the general term) focuses on Structured Validation. It is the "Type System" for LLMs.
1. The RAIL Spec
Guardrails AI uses a .rail file (similar to XML/JSON) to define What the AI must return.
- The Problem: LLMs are "Chatty." If you ask for a JSON object, they often say: "Sure! Here is your JSON:
{ ... }." - The Guardrail solution: It "Wraps" the LLM call. If the LLM returns anything that isn't valid JSON, the guardrail Auto-Corrects it or retries the request until it fits the schema.
2. Programmatic Validators
You can write Python functions (Validators) that run on the LLM's output:
- Regex: "Ensure the output contains a valid email."
- Competitor Check: "Ensure the output DOES NOT contain the name of our top competitor."
- SQL Schema: "Ensure the generated SQL query is valid for our Postgres tables."
- PII Check: "Ensure no SSNs are in the text."
3. The "On-Fail" Actions
What happens when a guardrail fails?
- Re-Ask: The guardrail automatically sends a message back to the AI: "You made a mistake in the JSON. Please fix it."
- Fix: The guardrail tries to fix it (e.g., stripping away extra text).
- Filter: The malicious part is deleted.
- Refrain: An error is returned to the user.
4. Why Logic beats Prompting
If you tell an AI "Only output JSON," it might fail. If you use a Guardrail, the Validation Code (actual Python) ensures that only JSON reaches your database. This separates the "Probabilistic" world of the model from the "Deterministic" world of your software.
Exercise: The Schema Designer
- You are building an AI that generates "Invoices." Which fields in the JSON must be "Strictly Validated"?
- Why is "Auto-Re-asking" better than just "Failing" when an AI makes a minor formatting error?
- How can a guardrail prevent "SQL Injection" by validating the SQL syntax before execution?
- Research: What is "Pydantic" and how does it integrate with the Guardrails AI framework?
Summary
Guardrails AI is about Enforcement. By defining a "Contract" between your code and the AI, you ensure that the AI remains a "Useful Tool" rather than an "Unpredictable Actor."
Next Lesson: Building your own: Custom guardrail development.