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#ai articles
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aillmops
·4 min read
LLM observability: a minimal logging checklist for AI apps (2026)
A practical checklist to add minimal-but-sufficient logs for AI apps and automations. Capture the evidence you need for debugging, cost control, and incident response without logging secrets.
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aisecurityllm
·4 min read
Prompt injection defense for AI apps: an input-surface checklist (2026)
A practical checklist to reduce prompt injection and data exfiltration risk in AI apps. Audit your input surfaces, tool permissions, and logging so you can ship with confidence.
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securitywebai
·4 min read
IDOR prevention checklist (authorization): what to review in 30 minutes
A practical, ops-style authorization checklist to prevent IDOR (broken object-level access control). Focus on where IDs enter, how reads/writes are scoped, consistent deny behavior, and one regression test that prevents reintroducing the bug.
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securitywebai
·4 min read
IDOR in AI-built web apps (authorization): how to catch it before you ship
AI-assisted development ships fast, and that makes authorization regressions common. This practical guide shows how to detect and fix IDOR (broken object-level authorization) with a repeatable review, tests, and rollout-safe patterns.
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aipromptingengineering
·4 min read
Spec-to-implementation prompt template (AI development): how to stop the model from guessing
A practical prompt template for AI coding that forces decision points into the spec: inputs/outputs, constraints, edge cases, acceptance tests, and phased delivery. Designed for LLM-era teams that need repeatable quality.
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aiwritingseo
·3 min read
Remove AI-tells checklist (AI writing): how to make LLM-assisted content publishable
A practical pre-publish checklist to remove AI-tells in LLM-assisted writing. Focus on reader intent, concrete evidence, decision criteria, and mechanical cleanup so content is scannable and trustworthy.
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airequirementsprompting
·4 min read
Clarifying questions checklist (AI development): what to ask before you let an LLM build
A practical clarifying-questions checklist for AI-assisted development. Turn vague requests into implementable specs by forcing decision points: scope, constraints, failure behavior, acceptance tests, and rollout/ops.
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