Raw LLM Responses

Inspect the exact model output for any coded comment.

Comment
Excellent post pro From a security perspective: LLM: Protect against prompt injection, jailbreaks & unsafe outputs RAG: Enforce data access control, sanitize retrieved content Agent: Apply least privilege + human-in-the-loop for sensitive/irreversible actions MCP: Zero-trust between services, strong authentication & encrypted communication
LinkedIn Workplace & Jobs Cloud Security Architect | Zero Trust & Identit… 2026-05-25T17:0…
Coding Result
DimensionValue
Primary valuesafety
Secondary valueaccountability
Alignment targetorganisations
Stancedemanding
Emotionapproval
Value justificationThe commenter emphasizes the need for security measures such as protecting against prompt injection and enforcing data access control, which aligns with the value of safety.
Target justificationThe commenter is addressing security concerns from an organisational perspective, indicating that the target of the alignment is organisations.
Coded at2026-06-11T08:09:08Z
Raw LLM Response
``` { "value_primary": "safety", "value_secondary": "accountability", "target": "organisations", "stance": "demanding", "emotion": "approval", "value_justification": "The commenter emphasizes the need for security measures such as protecting against prompt injection and enforcing data access control, which aligns with the value of safety.", "target_justification": "The commenter is addressing security concerns from an organisational perspective, indicating that the target of the alignment is organisations." } ```