Beyond the Hype: Building Trustworthy and Reliable LLM Applications with Guardrails
summary
Practical recipe for LLM safety: Java app uses Ollama with Granite 3 Guardian 2B plus classifiers (BERTa zero‑shot, VGG‑based relevance, Code‑Albert, bias‑toxic RoBERTa) and Microsoft Presidio anonymizer to enforce input/output guardrails (token limits, refusal counts, language checks, URL reachability, reading‑time truncation, prompt‑injection detection). Live demo blocks a violent query with an input‑warrail exception, proving the pipeline works; caveat is each guard often calls another model so latency increases as protections multiply.
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chapters (7)
- 0:00Introduction
- 1:25LLM Risks Overview
- 4:58Guardrails in Action
- 16:01Privacy & Abuse Protection
- 22:10Real-World Demo
- 25:58Retrieval Augmented Generation
- 27:40Final Takeaways
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