content-moderation-patterns

# Content Moderation Patterns Two-stage pattern that balances cost, latency, and quality: cheap deterministic filters first, then LLM classification only on survivors. ## Architecture ``` [ input ] │ ▼ [ pre-filter ] ── (regex, allow/block lists, length check) ──► reject early │ ▼ [ LLM classifier ] ── (Haiku, structured output) ──► categories + confidence │ ▼ [ decision router ] ├── high confidence + policy violation → reject ├── high confidence + clean → pass └── low confidence or edge categories → human review queue ``` ## Pre-filter Stage (cheap) Catch the obvious cases before paying an LLM call: ```python BANNED_PATTERNS = [ re.compile(r"\b(banned_term_1|banned_term_2)\b", re.I), re.compile(r"\bhttps?://(?!allowed-domain\.com)", re.I), # external links ] def pre_filter(text: str) -> tuple[bool, str]: if len(text) > 10_000: return False, "too_long" for pat in BANNED_PATTERNS: if pat.search(text): return False, f"banned_pattern:{pat.pattern}" return True, "pass" ``` Roughly 40-70% of spammy input should die here. Log counts by rule so you can tune. ## LLM Classifier Stage (Haiku) Use the smallest capable model. Haiku is usually right for moderation. ```python CATEGORIES = ["harassment", "self_harm", "spam", "off_topic", "pii", "clean"] def classify(text: str) -> dict: response = client.messages.create( model="claude-haiku-4-5", max_tokens=256, tools=[{ ...