RAG stands for Retrieval-Augmented Generation. It’s an approach that combines traditional data retrieval with GenAI models to include proprietary data and to produce custom reports.
In fact, RAG does:
- Retrieval: Instead of relying solely on what the GenAI model has learned during training, RAG retrieves relevant documents and pieces of information from proprietary or specific external sources, like a CRM, database, document store, or search engine.
- Augmentation: The retrieved information is then used to “augment” the prompt given to the GenAI model. This gives the model access to proprietary, custom and/or more up-to-date and domain-specific context.
- Generation: Finally, the GenAI model uses both the original user input and the retrieved information to generate a more accurate and informed response.
Typical use cases are:
- Chatbots with access to company-specific knowledge bases
- Legal or scientific question answering systems
- Customer support with dynamic knowledge retrieval
Since enterprise may significantly depend on RAGs, it’s crucial to get RAG “under control”. Controlled RAG pipelines are not only safer; they’re also more reliable, trustworthy, and adaptable to enterprise-grade use cases. By combining guardrails at the input, retrieval, generation, and output layers along with robust observability and policy enforcement, organisations can confidently deploy RAG-based systems that align with both their operational needs and governance obligations.
To obtain control over RAG, it’s input, its output, its use of data and its connection with LLM, the following techniques should be deployed.
1. Pre-Retrieval Controls: steer the input
Controlling the quality and intent of user input is the first step in ensuring safe and effective RAG behaviour. This can involve query rewriting to standardise ambiguous terms (such as converting “last month” to a specific date range), or using prompt classification to route queries to the appropriate vector index or knowledge source. Additionally, access controls can restrict who can ask what, based on identity, role, or context—particularly relevant in multi-tenant or enterprise deployments.
2. Retrieval Layer Controls: manage what the model sees
The retrieval phase is central to grounding the model’s response in real, verifiable content. Key controls include:
- Metadata-based filtering, where documents are tagged and filtered based on trust level, classification, or source.
- Semantic re-ranking, to exclude irrelevant or hallucination-prone content post-retrieval.
- Whitelisting, which ensures only verified and up-to-date sources are used, ideally with provenance or document fingerprinting.
- Clearance-based filtering, which ensures sensitive documents are only accessible to users with the right clearance or role.
These techniques ensure the model operates only on curated, relevant, and appropriate content.
3. Prompt Construction: guide the model’s behaviour
When constructing the final prompt that the model will respond to, care must be taken to preserve coherence and enforce grounding. Best practices include:
- Smart context management (e.g., section-aware chunking) to avoid injecting partial sentences or unrelated content.
- Instructional prompts that explicitly tell the model to answer only based on the retrieved documents.
- Role conditioning to keep the model “in character” (e.g., a compliance advisor, or technical assistant), reducing the likelihood of freeform or speculative answers.
These controls help constrain the generation to what is factually supported.
4. Post-Generation Controls: validate what comes out
Even with proper retrieval and prompt design, output must be verified before it reaches the end user. This involves:
- Enforcing source attribution or citations for all factual claims.
- Running outputs through filters or classifiers to check for toxicity, bias, or policy violations.
- Introducing fallback mechanisms: if confidence is low or the grounding context is weak, the system can refuse to answer or route to a human reviewer.
- Using entailment models or semantic validators to cross-check whether the output is logically supported by the retrieved context.
Such layers provide confidence that the system is not hallucinating, misleading, or overstepping its intended scope.
5. Monitoring and Governance: enable oversight
Governance mechanisms are essential for long-term control, auditability, and compliance. These include:
- Logging every step of the RAG pipeline—query, retrievals, prompt construction, outputs—with metadata for traceability.
- Version control over vector indexes, prompt templates, and content sources to ensure consistent and reproducible behaviour.
- Anomaly detection and access logging to monitor for abuse, misuse, or information probing attempts.
These mechanisms are crucial in high-assurance domains, such as finance, legal services, or regulated industry contexts.
6. Policy-Based and Agentic Control: install dynamic guardrails
In more advanced implementations, the entire RAG pipeline can be embedded in a workflow engine (such as n8n, LangChain, or enterprise-grade orchestrators), where each step is policy-enforced. Policies can dynamically determine what actions are allowed, based on organisational rules (e.g., OPA/REGO), tenant context, or regulatory requirements.
Agentic layers can further enhance control: instead of blindly executing a RAG flow, an agent decides whether to retrieve, re-ask, escalate, or reframe a query—adding another layer of judgment and safety.
Controlled AI services 