Why RAG Becomes Unstable in Enterprises — and How Domain Knowledge Solves It

Introduction: RAG is not equally reliable

Retrieval-Augmented Generation (RAG) is touted as a fast way to connect unstructured data with LLMs. In many proofs-of-concept, it produces impressive prototypes quickly. But in enterprise environments—where reliability, auditability and governance matter—classic RAG setups reveal systemic weaknesses: ambiguity, irrelevant chunk selection, lack of revision control and uncontrollable linkages lead to unstable, non-auditable systems.

At Reruption we see this boundary every day: prototypes that shine in demos produce unpredictable answers in production use. That's why we established a different approach — Domain-Knowledge-Capture — to build reliable, auditable knowledge models for mid-market and large enterprises.

The four structural problems of classic RAG architectures

RAG combines retrieval from documents with a generative model instance — that sounds logical, but it has four typical failure modes:

1. Ambiguity and loss of context

Retrieval processes often operate with short chunks. Without a semantic structure this creates loss of context: a paragraph from an instruction manual can be correct in isolation, but when taken out of sequence with other chunks it can force an incorrect conclusion. As a result, LLMs deliver ambiguous or contradictory answers, especially in multi-step decision processes.

2. Irrelevant chunk selection

Search-based retrieval algorithms prioritize statistical proximity. That regularly leads the model to include **irrelevant or outdated documents** — thereby corrupting the answer. In sensitive domains like recruiting or machine maintenance, that is unacceptable.

3. Lack of revision control

RAG mixtures produce answers that cannot be traced directly to a single, verifiable source. For compliance, audit and liability there is no clean trail: who contributed which knowledge? When was it changed? Without these answers, corrections and responsibilities are difficult to enforce.

4. Uncontrollable linkages and hallucinations

LLMs tend to fill gaps in understanding with plausible but false information — the classical hallucination. In RAG systems these risks multiply because linked chunks can form unpredictable combinations. The result: an illusion of knowledge without real revision control.

Why Domain Knowledge Capture is the more stable approach

Instead of haphazardly stitching documents together, at Reruption we create structured knowledge models explicitly built for reliability, auditability and governance. Domain Knowledge Capture means: knowledge is extracted from experts, converted into standardized objects, enriched with role logic and constraints, and managed centrally.

The benefits are clear:

• Deterministic answer corridors instead of open-ended generation
• Complete audit trails and versioning
• Tenant separation and fine-grained access control
• Testability via automated regression tests on knowledge instances

That makes the difference between an impressive prototype and a production-ready, accountable application.

How we extract knowledge from experts

Knowledge lives in people's heads, processes and documents. Our extraction method combines qualitative interviews, structured workshops and iterative validation:

• Kickoff & Scope: We define the use case, answer metrics and compliance requirements.
• Shadowing & Interviews: Experts are observed and processes are analyzed for decision flows.
• Knowledge Modelling Workshops: In facilitated sessions we transform implicit knowledge into decision trees, rules and knowledge objects.
• Iterative Validation: Prototype answers are reviewed and refined together with experts.

This process ensures that knowledge does not lie loosely in documents, but is available as a verifiable, versioned unit.

How we structure knowledge: building blocks of a reliable knowledge model

Our structuring philosophy is pragmatic and engineering-focused. We use several complementary artifacts:

Decision trees

For processes with clear paths (e.g., candidate screening in recruiting) we explicitly encode decision paths. Each node is tested, documented and assigned owners. This prevents an LLM from making implicit jumps that no one can trace.

Knowledge objects

Knowledge objects are structured data and text components: definitions, policies, formulas, exceptions. Each object has metadata (author, version, scope) and can be referenced or substituted. This keeps the origin of statements transparent.

Role logic & access control

Answers are often context-dependent: what a recruiter is allowed to see may not be visible to an external consultant. We define role logic clearly and link it to knowledge objects. That creates tenant separation and security-compliant responses.

Constraints & business rules

Constraints are hard rules that limit an answer: no pricing information without approval, no medical advice without a vetted source. These rules are enforced programmatically before an answer corridor can be left.

Answer corridors: safe, traceable, testable

Instead of open-ended generation, at Reruption we define one or more answer corridors for each request. A corridor contains:

• Permitted knowledge objects
• Preformatted templates
• Security and compliance checks
• Fallback strategies with declarative handoff to humans

Example: a recruiting chat may provide applicants with information about the process, but not definitive contractual advice. The corridor allows communicable answers and routes complex cases to HR operations — with a single click and a complete audit log.

Architecture diagram of the Domain Knowledge Module

A picture often says more than a thousand words. Here is a simplified architecture diagram of our Domain-Knowledge-Module in text form:

+-------------------------------------------------------------+
|  Client Apps (Chatbot, CRM, Ticketing)                      |
+------------------------------+------------------------------+
                               |
+------------------------------v------------------------------+
|  API-Gateway / Auth (OAuth, SAML)                           |
+------------------------------+------------------------------+
                               |
+------------------------------v------------------------------+
|  Domain-Knowledge-Module                                     |
|  - Knowledge Repository (versioned)                          |
|  - Knowledge Compiler (Decision Trees, Templates)            |
|  - Roles & Constraints Engine                                |
|  - Test & Regression Suite                                   |
|  - Audit Log & Governance UI                                 |
+------------------------------+------------------------------+
                               |
+------------------------------v------------------------------+
|  LLM Layer (optional)                                        |
|  - Controlled Prompting                                        |
|  - Reranker / Verifier                                        |
|  - Hallucination Guard                                        |
+------------------------------+------------------------------+
                               |
+------------------------------v------------------------------+
|  Data Stores / Connectors                                     |
|  - Source Docs (verifiziert)                                  |
|  - HR/ERP/CRM Systems                                          |
|  - Observability / Monitoring                                 |
+-------------------------------------------------------------+

Important: the LLM here is a available tool, not the sole source of truth. The answer is typically composed from the Knowledge Module and only lightly refined by an LLM — never left uncontrolled.

Versioning, security and tenant separation — best practices

Companies expect traceability. Our best practices are based on engineering standards:

Versioning

Each knowledge object receives semantic versioning (MAJOR.MINOR.PATCH) and change logs. Changes go through a review workflow (Author → Subject-Matter-Expert → Compliance). Automated regression tests run before every deployment.

Security & data protection

We implement role-based access control (RBAC), audit logs and data encryption at rest and in transit. Sensitive data is tokenized or never sent to LLMs — instead we deliver only the permitted, masked information.

Tenant separation

Multi-tenancy is enforced strictly at the data level: separate repositories or cryptographically isolated storage segments per tenant. We also define mandatory per-tenant governance policies so settings cannot be overwritten.

Internal governance

We recommend a small cross-functional board (AI owner, Legal, Security, Domain Lead) that approves changes. This board maintains a change calendar and defines SLAs for corrections.

Practical examples: Recruiting, Real Estate, Mechanical Engineering

Recruiting — example: Mercedes Benz

In collaboration with Mercedes Benz we saw an NLP-based candidate communication setup where the difference between RAG and Domain Knowledge was very visible. With a classic RAG approach chatbots sometimes produced inaccurate statements about application statuses or overly positive assessments. With a Domain Knowledge approach we model the interview and screening process as a decision tree, link it to HR roles and define answer corridors. Result: precise, 24/7 communication with clear audit trails and fewer escalations to HR.

Real estate — generic use case

In real estate, pricing data, contract clauses and local regulations are sensitive and dynamic. A RAG system would easily reference outdated clauses. Our Domain Knowledge strategy defines standardized knowledge objects for rental-law FAQs, pricing calculation methods and regional specifics. Answers are delivered through vetted templates; uncertain cases trigger a structured handoff to experts. This keeps answers robust and legally verifiable.

Mechanical engineering — STIHL & Eberspächer

In manufacturing and maintenance, incorrect information is dangerous. For clients like STIHL and Eberspächer we built knowledge models for maintenance, fault diagnosis and safety requirements. Instead of unstructured document search we provide reproducible troubleshooting paths (key checks, measurements, approval levels). These paths are testable, versioned and auditable — a must when safety and liability are at stake.

Implementation roadmap: from PoC to production

We recommend a pragmatic, risk-based roadmap:

1. PoC (2–4 weeks): We validate feasibility for a critical process. Our AI PoC offering (€9,900) delivers a technical proof-of-concept with a functional prototype and metrics.
2. Modelling & Pilot (6–12 weeks): Expert workshops, knowledge modelling, first test deployments for one business area.
3. Scaling & Governance (3–6 months): Multi-tenant setup, integrations, security hardening, governance processes.
4. Production & Continuous Improvement: Regular reviews, automated regression tests and continuous knowledge maintenance.

As co‑entrepreneurs we work embedded with your teams: we take responsibility, provide engineering depth and build the system so your organization can operate it long-term.

Takeaway: reliability over quick glamour

RAG is a powerful tool — but not the sole answer for enterprise requirements. In production environments deterministic answers, auditability and governance matter more than free-form generation. Domain-Knowledge-Capture provides this foundation: knowledge is extracted, structured, versioned and delivered in a controlled way.

If you want to build reliable, scalable AI applications, talk to us. We bring the technical depth and the business ownership to turn ideas and prototypes into real, accountable products — fast, secure and auditable.

Call to Action

Would you like to know how your use case is affected by RAG instabilities — and whether Domain Knowledge Capture is the better solution? Contact us for a non-binding assessment or use our AI PoC offering (€9,900) as a fast first step. We support you from use-case definition to production handover.