How to Reduce Hallucination for Enterprise Gen AI Deployments
Hallucination has been the biggest problem in translating AI Curiosity into AI Capability, especially at enterprise level. And “Hallucination” definition varies widely across organizations and studies. At GraaS, we define “hallucination” in AI systems as the “generated content that is nonsensical or unfaithful to the provided source material”. The reason chose this is because it encompasses the two main categories that dominate the essence:
- Factuality Violation: Output that isn’t specific or supported by input context (intrinsic hallucination), verifiable facts (extrinsic hallucination) and source documents in RAG systems (factual hallucination)
- Coherence Failures: Output that is internally contradictory, logically inconsistent, or nonsensical
The core unifying thread is lack of groundedness: the generated output isn’t properly anchored to either the input, retrievable facts or logical consistency.
What cause hallucinations?
| Factor | Contribution to Hallucinations | Notes |
| Knowledge Base Quality (Information Gaps + Comprehensibility Issues) | ~40% | Poorly structured, ambiguous, incoherent content; Missing facts in corpus |
| Poor Retrieval (RAG) | ~30% | Wrong chunks retrieved, low recall |
| Model Limitations (LLM) | ~15% | LLM’s inherent tendency to generate plausible-sounding text |
| Prompt/System Design (User) | ~15% | Weak grounding instructions, no citation enforcement |
| Validation Layer | Prevents | This is not a source, but detection layer |
In Enterprise AI deployment models, especially Technical Documentation based AI projects, there are four sources of hallucinations:
1. Knowledge Base (KB) – Problem: Garbage in, garbage out
2. RAG – Problem: Right info exists but isn’t retrieved or wrong info is retrieved
3. LLM Choice – Problem: LLM adds, modifies or misinterprets despite having correct context
4. Prompt Design – Problem: This is user behaviour, you have little to moderate control on their interaction patterns
5. Validation Failure – This is not an hallucination source but it’s a detection failure
BTW, these do not work independently, they have interaction effects, each layer adding risk multiplicatively.
How do we reduce hallucinations?
As you can see there are 4 components that cause hallucination and one governance layer. But here’s the critical distinction:
- Sources 1 – Corpus
- Sources 2-3 – Non Corpus
- Source 4 – User Behavior
- Gate – Detects hallucinations
Assuming that validation layer is already at its optimum level and user behavior is beyond control, we rely on Corpus and Non-Corpus hallucination mitigation approaches. Of these two, ensuring the Knowledge Base is fully comprehensible to LLMs is foundational because improvements here propagate across all upstream layers. This delivers the highest RoI and helps you achieve reliable enterprise-grade results in your AI deployments.
Advantage CohGent
CohGent is our Anti-Hallucination as a Service product for large & medium enterprises. CohGent turns unreliable AI into a trustworthy operational asset by preventing hallucinations. Our platform transforms knowledge bases, monitors retrieval quality and verifies every AI output against your authoritative sources. As a result, every AI-generated technical response is grounded in validated, conflict-free knowledge, preventing errors before they cause L3 customer calls, equipment damage, safety incidents or regulatory violations. Net effect – accelerating AI projects deployment and enabling productivity gains worth 10-50x our cost.
You can confidently deploy AI tools & adopt Agentic workflows that actually improve operations instead of creating new liabilities. With CohGent, you stop treating AI as experimental and start treating it as mission-critical infrastructure. Relevant. Reliable. Repeatable.
Customer Success Story
Industry-leading Semiconductor Organization deployed CohGent and reduced AI hallucinations from 30% to 5%. This resulted in their internal AI projects going live 12 months faster & customer support teams saw a drop in L3 tickets by 37%. Here is KPIs of the deployment
| Metric | Without CohGent | With CohGent | Improvement |
| Hallucination Rate | 30% | 5% | 84% improvement |
| Tech Documentation | 10 days/Publish | 4 days/Publish | 60% faster & better |
| L3 Calls Volume | Baseline | -37% | $1.2 M saved |
| AI Tool Deployment | 12-18 months | 6 months | 12 months faster |
| Validation Overhead | 100% manual review | 40% manual review | 60% time savings |
If you are, an AI Ops professional, an executive sponsor for AI programs or senior leader for Customer Support & Success, struggling with hallucination problems in your AI projects, lets talk. [email protected]