AI guardrails are the safety mechanisms that prevent your AI system from producing harmful, biased or off-topic outputs. For startups deploying LLM-based products, guardrails are essential — a single viral screenshot of your chatbot saying something inappropriate can cause serious reputational damage.
How to implement this:
- Input filtering: Validate and sanitise user prompts before they reach your model. Block known prompt injection patterns and set maximum input lengths.
- Output filtering: Screen AI responses for harmful content, PII leakage and off-topic answers before showing them to users. Use a classifier or a second LLM call as a safety layer.
- Rate limiting: Cap requests per user to prevent abuse and control costs. Start with conservative limits and adjust based on real usage.
- System prompts: Define clear behavioural boundaries in your system prompt — what the AI should and should not do, and how it should handle edge cases.
- Monitoring: Log all interactions (respecting privacy) and set up alerts for anomalous patterns such as repeated jailbreak attempts or unusual output lengths.
The OWASP LLM Top 10 is a practical checklist for the most common vulnerabilities. Tidal Control helps you document your guardrails as controls and track their effectiveness over time.