Without a source, the user cannot verify the answer.

Enterprise RAG (Retrieval-Augmented Generation) grounds large language model answers in an organization's own documents and data. Sanal Çekirdek builds it as a complete system: connected to source systems, respectful of access permissions, and citing its sources.

Set up well, RAG turns scattered institutional knowledge into a single queryable layer; set up poorly, it leaks past permission boundaries and produces unsupported answers.

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When RAG fits — and when it doesn't

RAG connects enterprise knowledge to the model at answer time, without retraining; when the source changes, the answer changes with it. It is usually the right first step for knowledge that updates frequently, must be attributable, and is governed by access permissions. For fixed task formats or deep domain-language adaptation, fine-tuning (SFT) or continued pre-training (CPT) is evaluated instead.

First choice for current, frequently changing knowledge
A fit wherever answers must cite their sources
SFT is weighed for fixed task formats
Decisions are made by evaluation, not preference

Permission-aware data foundation

RAG quality cannot exceed the quality of the data foundation beneath it. Document stores, intranet content, and business applications are indexed through connectors, with chunking and metadata strategies designed per document type. Access rights are inherited from the source system into the query: a document the user has no permission to see cannot enter the answer.

Connectors for document stores and business applications
Chunking and metadata designed per document type
Permission inheritance preserved at query time
Content lifecycle: updates flow into the index

Citations and answer accuracy

In production, an answer is worth as much as the source behind it. Answers are generated bound to retrieved sources, and users see which document and section each answer relies on. Use-case-specific evaluation sets track the rate of unsupported answers, and when no source is found the system declines to answer rather than guess.

Document- and section-level citations
Use-case-specific evaluation sets
No source, no answer: constrained behavior

Internal search and assistant scenarios

One RAG foundation supports multiple scenarios. A knowledge-base assistant gives employees a single point of access to procedures and product information; support teams draft responses from cited content; legal and procurement teams query contracts and policies in natural language. Permission boundaries and citation discipline stay the same in every scenario.

Knowledge-base assistant for procedures and product information
Support response drafting from cited content
Contract and policy queries with clause-level citations

How we work

The use case, knowledge sources, and success criteria are defined together.
Source systems are connected; chunking, indexing, and permission inheritance are designed.
Answer quality is measured against an evaluation set within a pilot scope.
The system goes live in a controlled manner once acceptance criteria are met.
Content lifecycle, user feedback, and cost are monitored while the service runs.

How success is measured

Source alignment and citation accuracy are measured against evaluation sets.
The rate of unsupported or incorrect answers is tracked over time.
Permission audits test whether restricted content can leak into answers.
Latency and cost per request are reported alongside usage volume.

Frequently asked questions

How is enterprise RAG different from a general-purpose chatbot?

Enterprise RAG grounds its answers in the organization's own documents and cites its sources; a general-purpose chatbot answers from the model's training data. That difference determines auditability and organization-specific accuracy.

Does RAG require training the model on our data?

No — in RAG, documents are not baked into model weights; they are retrieved at answer time and remain in the index. Where the data resides and who can see it stays under the organization's control.

Can hallucinations be eliminated entirely?

No — the risk of unsupported answers cannot be removed entirely; it is measured and constrained. Source-bound generation, evaluation sets, and decline-to-answer behavior keep the risk manageable.

A well-built RAG foundation turns institutional knowledge into an auditable answer layer. The first step is defining the use case and its success criteria.

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