HalalFinanx hardcodes refusals, skips prompt engineering

// 45d agoINFRASTRUCTURE

HalalFinanx hardcodes refusals, skips prompt engineering

HalalFinanx is a high-stakes Islamic finance RAG project that refuses to answer when top-k retrieval scores fall below a cosine similarity threshold, rather than relying on prompt instructions to “refuse if unsure.” The post also calls out chunk-level jurisdiction metadata, FAISS persistence on HuggingFace Spaces, and cleaner ingestion paths for scanned versus HTML sources.

// ANALYSIS

This is the right instinct for domains where a wrong answer has real consequences: if the evidence is weak, make the system unable to answer instead of asking the model to self-police.

–A hard retrieval cutoff is more reliable than prompt-based abstention because the LLM never gets a chance to speculate
–Jurisdiction metadata on every chunk is essential in Islamic finance, where valid answers can differ by school, region, and authority
–The data pipeline matters as much as the model: scanned PDFs, ephemeral vector stores, and bad source extraction will poison the retrieval layer
–The 0.7 cosine threshold reads like a sensible prototype, but it should be calibrated per question type and measured against false-refusal rates

// TAGS

halalfinanxragllmvector-dbinferenceprompt-engineeringsafety

DISCOVERED

45d ago

2026-04-24

PUBLISHED

45d ago

2026-04-24

RELEVANCE

8/ 10

AUTHOR

Particular-Plate7051

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