How Retrieval-Augmented Generation Could Stop AI Hallucinations

Forbes

23-06-2025

Sagar Gupta, EST03 Inc., is an ERP Implementation Leader with over 20 years of experience in enterprise-scale technology transformations.
Large language models (LLMs) like OpenAI's GPT-4 and Google's PaLM have captured the imagination of industries ranging from healthcare to law. Their ability to generate human-like text has opened the doors to unprecedented automation and productivity. But there's a problem: Sometimes, these models make things up. This phenomenon—known as hallucination—is one of the most pressing issues in the AI space today.
The Hallucination Challenge
At its core, an LLM generates responses based on statistical associations learned from massive datasets. It's like a parrot with access to all the books ever written—but no real understanding of what's true or relevant. That's why hallucinations happen: The model is trained to sound plausible, not necessarily be accurate.
Researchers classify hallucinations into two main types:
• Intrinsic: These contradict known facts or include logical inconsistencies.
• Extrinsic: These are unverifiable, meaning there's no reliable source to back them up.
The root causes lie in incomplete training data, ambiguous prompts and the lack of real-time access to reliable information.
The RAG Solution
Retrieval-augmented generation (RAG) enriches traditional LLMs with a system that fetches relevant documents from a trusted database in real time. The model then uses these documents to generate responses grounded in actual content, rather than relying solely on what it 'remembers' from training.
The architecture typically includes:
• A retriever, often based on technologies like dense passage retrieval (DPR) or best matching 25 (BM25)
• A generator, usually a transformer-based model that crafts the response based on the retrieved data
This combination essentially transforms the LLM into an open-book test-taker rather than a guesser.
RAG In Action
Real-world experiments show promise. A 2021 study reported a 35% reduction in hallucinations in question-answering tasks using RAG. Similarly, models like DeepMind's RETRO and Meta's Atlas demonstrate significantly better factual accuracy by incorporating retrieval systems.
Innovations like the fusion-in-decoder (FiD) and REPLUG models take this further by improving how the model processes multiple retrieved documents or integrates them into frozen models for faster deployment.
But even RAG has its limits. If the retriever pulls the wrong information or the generator misinterprets it, hallucinations can still occur. And there's an added trade-off: Retrieval increases system complexity and inference time—no small issue in real-time applications.
Rethinking Evaluations
Evaluating hallucinations is another hurdle. Existing metrics like FactCC and FEVER try to measure factual consistency, but they often miss nuances. Human evaluations remain the gold standard, but they're costly and slow.
Researchers are now exploring reference-free factuality metrics and better ways to assess whether the retrieved documents actually support the generated answer.
What's Next?
Three exciting directions could further improve how we tackle hallucinations:
1. Differentiable Retrieval: Instead of separating the retriever and generator, future systems might train both components together in a fully end-to-end fashion. This could tighten the alignment between what's retrieved and what's generated.
2. Memory-Augmented Models: Some experts are exploring how AI can maintain long-term memory internally, reducing the need for external retrieval or complementing it when appropriate.
3. Fact-Aware Training: By incorporating factual correctness into the training objective itself—via techniques like reinforcement learning from human feedback—models might learn to prioritize truth over plausibility.
How RAG Helps Enforce Departmental Private Policies
Here's how RAG systems can support department-specific policies in real enterprise environments:
With RAG, AI assistants can answer employee questions about HR policies using only internal documents—like the company's official handbook or compliance playbook—ensuring no public or outdated data leaks into responses.
Examples: Confidential grievance reporting, DEI guidelines and code of conduct.
Use Case: An employee asks about the process for reporting harassment. Instead of guessing or fabricating, the AI pulls directly from the current internal grievance protocol.
Financial departments are governed by strict rules, often tailored to the business and changing frequently. RAG systems can help ensure AI-generated summaries, reports or answers reflect the latest finance policies pulled from internal financial controls documents or regulatory compliance handbooks.
Examples: Internal audit procedures, expense reimbursement rules and compliance with SOX (Sarbanes–Oxley).
Use Case: A junior accountant asks, 'Can I reimburse a client dinner without itemized receipts?' The AI retrieves the latest expense policy and provides an accurate, compliance-approved response.
LLMs trained on public data should never guess legal advice. RAG enables law departments to control which internal documents are used, like NDAs, internal counsel memos or state-specific guidelines.
Examples: Confidentiality agreements, IP handling protocols and litigation hold instructions.
Use Case: A manager asks if they can share a prototype with a vendor. The AI accesses the legal department's approved NDA workflow and provides the required preconditions for IP protection.
RAG helps enforce brand consistency and confidentiality. AI writing assistants can generate content only using approved brand tone documents, messaging guidelines or embargoed launch timelines.
Examples: Brand tone guidelines, embargoed campaign details and competitive comparison policies.
Use Case: A content writer asks, 'What's our positioning against competitor X?' Instead of hallucinating risky comparisons, the AI references an internal competitive intelligence deck.
Sales reps often operate on tight timelines and ambiguous inputs. RAG-equipped AI assistants can ground responses in the official sales playbook, quoting rules and commission policies.
Examples: Discount approval thresholds, territory conflict resolution and lead qualification rules.
Use Case: A rep asks, 'Can I offer a 25% discount to a client in EMEA?' The AI checks the discount matrix and responds based on regional approval flows.
Security-related queries are risky when answered with public data. RAG ensures internal policies guide responses.
Examples: Data access controls, employee onboarding/offboarding protocols and acceptable use policy.
Use Case: An employee asks how to report a phishing attempt. The AI retrieves and relays the internal incident response protocol and contact escalation path.
Final Word
In an age where trust, privacy and compliance are business-critical, RAG doesn't just reduce hallucinations—it helps operationalize private knowledge safely across departments. For enterprises betting big on generative AI, grounding outputs in real, governed data isn't optional—it's the foundation of responsible innovation.
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