Beyond RAG: Deterministic Knowledge Retrieval in Unstructured Data

Retrieval-Augmented Generation has become the default pattern for enterprise AI systems. But RAG has a fundamental limitation: it's probabilistic. The retrieval step returns k neighbors based on vector similarity, which may or may not contain the information needed. For compliance-critical applications, this is unacceptable. This paper introduces Deterministic Knowledge Retrieval (DKR), a pattern that guarantees retrieval of relevant information when it exists in the corpus. The key insight: semantic chunking with overlapping windows and multi-granularity indexing. DKR operates in three phases: (1) corpus preprocessing with hierarchical chunking at 512, 1024, and 4096 token windows, (2) multi-index querying with exact match fallbacks, and (3) retrieval verification through LLM-aided relevance scoring. Production results on a 2.3M document corpus: 99.4% retrieval accuracy for known-answer queries, compared to 87.2% for standard RAG with equivalent latency (23ms P50).