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American Global Nexus LLC · Proprietary Astrology LLM
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Bedrock base Llama answers from your uploaded books via retrieval (RAG) — shows
citations like [R1] [K3]. AI AstroGuru is the model you fine-tuned
on the same books — answers from baked-in knowledge, no citations. Same question,
compare quality.
Drop PDFs, TXT, MD, EPUB or images. Tag Rule for material the model must follow 100%, or Knowledge for background context. Images become teaching examples — give a one-line caption (e.g. "long horizontal palm line crossing the hand = strong life energy").
Read first: Open the Training Manual — it walks you through palmistry, Feng Shui, complex conditional rules, and verification.
Each rule maps a chart pattern (planets placed in a single house) to a yes/no question shown on the validation screen. Notes travel with the rule into the prediction model so it knows the caveats. No sign / nakshatra here — only planets-in-house. Other rule types come later.
Header row: rule_id,planets,house,sign,degrees,nakshatras,gender,question,note — rule_id is optional (omit = create; include = update existing).
Quote multi-planet cells: "Saturn,Jupiter". Multiple questions per cell, pipe-separated: "You have a belly.|Your gut shows.". Multiple nakshatras: "Ashwini|Bharani". Per-planet degrees: "Sun:5-25|Saturn:28-30" or shorthand 5-25 for uniform. Sign / Gender: single value or empty.
rule_id.
Rules you've created — your IP. These are stored permanently and weighted by user answers over time.
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Questions generated by Bedrock Llama 4 Scout from your 41 uploaded books — RAG-grounded, fresh per chart, never stored. Llama's commercial license permits these outputs to be used in your product and in future model training. Roadmap (Task #17) — empty for now.
Build pending. The validation flow will show only your own rules until this is built.
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