vera-engine / CONTEXT.md
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feat: implement 4-tier LLM fail-fast cascade & benchmark-based local 3B selection
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Domain Context

Glossary

  • Vera: AI bot β†’ WhatsApp engagement β†’ grow merchant biz.
  • 4-Context:
    1. CategoryContext: Vertical info (dentists/salons).
    2. MerchantContext: Biz state (perf/identity).
    3. TriggerContext: Event β†’ convo start.
    4. CustomerContext: Opt data (acting for merchant).
  • Grounding: 100% real metrics from req. 0 metric hallucination.
  • Curiosity Hook: Hook merchant when no perf data.
  • Hinglish: Hindi+En mix (60/40) β†’ relate to IN merchants.

Rules

  • Len: No hard cap. Keep terse/readable.
  • CTA: 1 clear action. YES/STOP or open.
  • Tone: Match category voice (peer/clinical). No promo.
  • Lang: Match merchant pref.
  • Speed: Res < 30s.

Tech Strategy (50/50 Score)

  • Distill: Context Distiller β†’ shrink 500KB JSON β†’ 4 bullets.
  • Router: API req β†’ compose_with_fallback. 4-tier chain (NVIDIA β†’ Groq β†’ Gemini β†’ Local llama-cpp-python qwen2.5-3b-instruct).
  • Format: Schema force β†’ pure JSON.
  • Idempotent: /v1/healthz β†’ clear contexts.
  • Hooks: Loss aversion, peer proof, scarcity, binary commit.