Deterministic RAG: Beyond Probabilistic Retrieval
Most RAG systems today are probabilistic black boxes. You chunk documents, embed them, retrieve the top-k, and hope the LLM cites the right source. In production, hope is not a strategy.
The Problem with Probabilistic Retrieval
When you run the same query twice against a standard vector store, you are not guaranteed the same chunks. Temperature, approximate nearest neighbor (ANN) jitter, and index updates all introduce variance. For compliance-heavy industries—finance, healthcare, legal—this is unacceptable.
What Deterministic RAG Means
A deterministic RAG system guarantees that for a given query and a given knowledge base state, the retrieved context is identical every time. This requires:
- Versioned indices — Immutable snapshots of the knowledge graph.
- Exact retrieval paths — Citation chains that can be replayed and audited.
- Structured extraction — Moving from semantic similarity to graph-traversal logic where relationships are explicit.
Where GraphRAG Helps
GraphRAG shifts the retrieval paradigm from “find similar text” to “traverse verified relationships.” By grounding responses in a knowledge graph with explicit edges, you create an audit trail:
Query → Entity Extraction → Graph Traversal → Citation Bundle → LLM Synthesis
Each step is logged, versioned, and reproducible.
What I am Building
Over the next few months, I am open-sourcing a reproducible evaluation harness for GraphRAG systems. The goal is simple: if you claim your RAG pipeline is deterministic, you should be able to prove it with a CI-generated report.
If this interests you, follow the repository (link coming soon) or reach out on LinkedIn.