Corpus Engineering for Deterministic RAG

Why semantic retrieval only creates value when the corpus is engineered before inference.

The Question That Actually Matters

Most discussions of Retrieval-Augmented Generation focus on how it works. That is rarely the important question.

The real question is why you should care enough to do it correctly—especially in domains where errors, omissions, or fabricated relationships carry real consequences.

If you are considering RAG at all, you already understand that your organization’s intellectual property is embedded in large, heterogeneous corpora accumulated over time. That instinct is correct.

The value of RAG is not convenience. It is leverage.

Similarity Is a New Retrieval Dimension

Traditional text search retrieves information through explicit matches—keywords, phrases, or predefined taxonomies. These systems retrieve what you already know how to ask for.

Semantic retrieval introduces an additional dimension: similarity. Rather than matching surface text, it operates in a high-dimensional representation of meaning that captures relationships between concepts expressed differently.

This enables retrieval of relevant information that was never explicitly cross-referenced and may not share obvious terminology with the query.

Why Most RAG Implementations Fail

Naive RAG

  • Raw document dumps
  • Arbitrary chunking
  • Schema-agnostic embeddings
  • Unbounded similarity search
  • Obscured provenance
High recall, low trust

Engineered Corpus RAG

  • Cleaned and normalized inputs
  • Domain-aligned boundaries
  • Schema derived from corpus review
  • Deterministic filtering before similarity
  • Explicit provenance and traceability
High recall with control

Hallucinations are not an emergent property of language models. They are a symptom of unstructured similarity applied to unqualified data.

The Corpus Engineering Pipeline

Corpus Intake & Characterization

  • Heterogeneous document formats: pdf, docx, odf, txt, jpeg, png
  • Duplication and version drift: headers, footers, page numbering
  • Implicit structure detectable within formats and metadata
  • Missing or unreliable creation metadata
Failure Mode Without Cleaning
  • Indeterminate language degrades sentence-transformer similarity scoring
  • Low similarity permits semantic drift and hallucination risk
  • AI summaries become non-deterministic and professionally unsafe
Design Constraint
  • Novel retrieval value depends on strong similarity across the corpus
⇩

Cleaning & Normalization

  • Remove duplicate and near-duplicate content
  • Normalize text encoding and layout artifacts
  • Repair OCR and extraction errors
  • Detect structural boundaries (sections, clauses)
Design Constraint
  • Normalization must preserve semantic intent while removing noise
⇩

Schema Derivation

  • Empirical review of cleaned corpus
  • Identify stable entities and relationships
  • Define retrieval boundaries and join logic
Failure Mode Without Schema
  • Similarity search crosses logical boundaries
  • Context assembly becomes probabilistic
  • Provenance and traceability are lost
Design Constraint
  • Similarity is applied only within schema-defined bounds
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Prepared Dataset (PostgreSQL)

  • Structured rows derived from schema
  • Metadata indexed for deterministic filtering
  • Embeddings linked to source provenance
Design Constraint
  • All retrieval must be explainable at the row level
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Constrained RAG Inference

  • Context retrieved via schema and metadata filters
  • Similarity applied only to qualified content
  • Model output grounded in retrieved evidence

Every stage exists to protect similarity integrity and preserve provenance.

The model cannot exceed the quality or scope of the prepared dataset. This constraint is intentional.

Compliance Is a Consequence of Design

Deterministic retrieval, explicit provenance, and immutable audit logs are not add-ons. They emerge naturally from systems designed with bounded inference and structured data flows.

In OS3 deployments, the language model operates as a contained component. It does not act as an authority over source material, nor does it introduce external context. Each generation is produced solely from retrieved, pre-qualified data and is fully attributable to its sources.

All model interactions are logged and auditable. The model’s role is limited to producing novel summaries and explanations of curated information, not generating new facts or interpretations outside the corpus.

This design allows OS3 systems to align with NIST 800-171, CMMC, HIPAA, and similar frameworks without reliance on opaque cloud services or post-hoc controls.

Who This Is For

OS3 is designed for technical professionals operating in environments where large corpora must be searched quickly, correctly, and defensibly.

Operational Characteristics

  • High-consequence decision making
  • Large, heterogeneous document collections
  • Requirement for provenance and traceability
  • Low tolerance for non-deterministic output

Common Domains

  • Aerospace and defense engineering
  • Clinical research and healthcare systems
  • Legal, regulatory, and compliance analysis
  • Energy, nuclear, and industrial R&D
  • Government agencies writing bills, laws, regulatory code
  • Contract formation and tracking of changes and updates
  • Accounting systems where expansive auditing analysis and oversight is needed

If your primary requirement is consumer chat interfaces, exploratory prompt experimentation, or uncurated document search, this system is not designed for that use case.