Shipping an LLM prototype takes an afternoon; making that AI system achieve 99%+ factual grounding, zero prompt-injection leaks, and deterministic JSON schema compliance in production requires rigorous evaluation suites (evals) and runtime guardrails.
If you are looking to hire a freelance AI evaluation, LLM guardrails, and hallucination prevention programmer, I am Aldian Fazrihady.
Production AI Reliability Patterns I Implement
- Automated Golden-Dataset Evaluation Suites: Running CI/CD regression checks on retrieval recall, citation faithfulness, tool-selection accuracy, and latency percentiles whenever prompts or models change.
- Deterministic Output Validators: Enforcing strict Pydantic / Zod schemas, mathematical reconciliation checks, and regex fallback extractors (From 16% to 100% Extraction: Rule-Based Fallbacks).
- Sandboxed Tool Execution & RBAC Scopes: Ensuring autonomous agents cannot execute unauthorized mutations or access cross-tenant records (Building Autonomous AI Agents with MCP & Guardrails).