3a00ff4d33
- Complete Fastify gateway with 8-stage pipeline - Circuit breaker (opossum) per model tier - Rate limiting per caller - Ban list validation (EN/DE/auto-detected) - TIP validator (SFF-8024, part numbers, wavelengths) - Prometheus metrics - pg-boss async queue - PostgreSQL audit log + review queue - 9 prompt templates (TIP, LinkedIn, ShieldX) - Learning engine scaffolding - Auto-learning: ban-list, few-shot, routing, prompt optimizer
51 lines
1.9 KiB
YAML
51 lines
1.9 KiB
YAML
id: internal-ban-detect
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version: "1.0.0"
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task_type: internal-ban-detect
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model_preference: "qwen2.5:14b"
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temperature: 0.2
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max_tokens: 1000
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output_format: "json"
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system_prompt: |
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You analyze LLM-generated text samples to identify phrases that sound like AI-generated filler,
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marketing speak, or buzzwords that should be banned from future outputs.
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Look for:
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- Transition phrases that add no information ("Having said that", "It's worth noting", "That being said")
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- Marketing buzzwords ("leverage", "synergy", "cutting-edge", "state-of-the-art", "holistic", "robust")
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- Clichéd openers ("In today's fast-paced world", "In today's digital age", "As we navigate")
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- Clichéd closers ("In conclusion", "To summarize", "All in all", "At the end of the day")
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- Empty intensifiers ("truly", "really", "absolutely", "certainly") used as filler
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- Passive constructions hiding agency ("It is widely known", "It has been shown")
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- German equivalents of all the above ("Letztendlich", "Zusammenfassend", "ganzheitlich",
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"nachhaltig" when used as buzzword, "abschließend", "selbstverständlich")
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Do NOT flag:
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- Technical terms that happen to appear in the ban categories (e.g. "robust" in a systems context)
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- Words that carry genuine meaning in context
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- Short common words (< 4 characters)
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Return ONLY valid JSON in this exact format:
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{
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"candidates": [
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{
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"term": "string (lowercase, the exact phrase)",
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"language": "en" | "de" | "auto",
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"category": "buzzword" | "filler" | "opener" | "closer" | "transition",
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"example_context": "string (the surrounding sentence where you found it)"
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}
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]
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}
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If you find no candidates, return: { "candidates": [] }
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user_template: |
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Analyze these LLM output samples for AI-filler phrases and marketing buzzwords:
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{{input}}
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Return JSON with all identified candidates.
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variables:
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- input
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