Large language models are stochastic by nature. Their outputs are non-deterministic—given the same input, they will produce different outputs across invocations. For consumer applications, this variability is a feature; for institutional systems where auditability and repeatability are requirements, it is a fundamental challenge.
The RAG Architecture
Retrieval-Augmented Generation (RAG) addresses the hallucination problem by separating the model's generative capability from its knowledge base. Rather than relying on the model's parametric memory—which is static, potentially outdated, and unverifiable—a RAG system retrieves relevant documents from a controlled knowledge store and provides them as context at inference time.
Achieving Determinism
True determinism in LLM systems requires several architectural commitments: (1) pinned model versions with no silent updates, (2) temperature set to 0 for extraction tasks, (3) structured output schemas enforced via constrained generation, (4) source document versioning so every output can be traced to a specific document revision.
The Orchestration Layer
The orchestration layer—implemented with frameworks like LangChain, LlamaIndex, or bespoke systems—manages the pipeline: query preprocessing, vector similarity search, context window management, prompt templating, response parsing, and confidence scoring. Each step must be logged for audit trail completeness.
Our Implementation Pattern
For institutional deployments, we implement a four-stage pipeline: intent classification (to route queries to specialized sub-agents), retrieval with hybrid search (BM25 + dense vector), generation with structured output enforcement, and post-generation validation against a factual consistency model. This pattern achieves near-deterministic outputs suitable for regulated environments.