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Always Validate LLM Output in Production Pipelines
Even with structured outputs and strict prompting, LLMs can return unexpected content. Implement output validation layers: JSON schema validation, regex checks on critical fields, confidence scoring, and human-in-the-loop reviews for high-stakes decisions. Never trust raw LLM output in production without a safety net.
Implement Prompt Injection Defenses
Always sanitize user inputs before passing them to LLMs. Use input validation, output filtering, and privilege separation to prevent prompt injection attacks that could manipulate your AI system into performing unintended actions.
Always Validate AI-Generated Code
Always validate AI-generated code before deploying — even the best models can introduce subtle bugs.
Validate and Sanitize LLM Outputs
If LLM outputs are displayed to users or used in other systems, always validate and sanitize them to prevent injection attacks or the display of inappropriate content.
Request Error Handling
When asking an LLM to write code, specifically request that it include error handling (e.g., try-catch blocks, input validation).
Building a Prompt Template System
Create reusable prompt templates: 1) Identify common prompt patterns in your application, 2) Extract variable parts into placeholders, 3) Create template functions with parameter validation, 4) Build a library of tested templates, 5) Implement version control for template changes.
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