How I Built a RAG System Over more than 100 USCIS Administrative Appeals Office Decisions with Gemini

USCIS denial rates for EB-1A petitions nearly doubled in one year - from 25.6% to 46.6%. NIW denial rates hit 64.3%. Immigration attorneys charge $5,000 to $15,000 for case preparation that most applicants can't afford.

I'm building PetitionIQ, an immigration case preparation platform that analyzes visa petitions the way USCIS actually reviews them. The core of the platform is a RAG pipeline over 107 real USCIS Administrative Appeals Office (AAO) non-precedent decisions - not generic legal knowledge, not LLM training data, but actual adjudication outcomes with full provenance.

This post walks through every design decision in the RAG system: why the corpus is biased and how I handle it, why category isolation matters more than you'd think, and how hybrid retrieval with hard filters prevents the kind of cross-contamination that makes legal AI dangerous.

Why AAO decisions?

The Administrative Appeals Office publishes non-precedent decisions on uscis.gov. These are real adjudication outcomes - cases where someone filed an I-140 petition, got denied, and appealed. The AAO either sustained the appeal (overturned the denial), dismissed it (upheld the denial), or remanded it (sent it back for further review).