Two hard problems in production AI:

Accuracy : RAG systems giving wrong answers 48% of the time

Cost : LLM API bills hitting $47K/month

We solved both. Here's how.

Part 1: RAG Accuracy (52% → 89%)

Our RAG system was confidently wrong. Users asked "What were Q2 healthcare results?" and got Q1 data, footnotes, and chapter titles with zero content.

High similarity scores. Completely useless context.

The LLM wasn't the problem. Retrieval was broken.

The 6-Stage Pipeline

Stage 1: Query Processing

Problem: "Show me Q2 results" has no semantic information.

Solution: Query expansion + metadata extraction

def process_query ( raw_query : str ) -> ProcessedQuery : metadata = extract_metadata ( raw_query ) # dates, entities expanded = expand_query ( raw_query , metadata ) embedding = embed_with_context ( expanded , metadata ) return ProcessedQuery ( expanded , metadata , embedding )

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Transformation:

Input: "Show me Q2 results"

Output: "quarterly financial…