AI Reliability

Hallucination Mitigation: Designing AI for Grounded Enterprise Answers

A practical reliability framework for reducing unsupported model outputs by improving retrieval, knowledge versioning, provenance, guardrails and human escalation.

Hallucination is a system problem

Unsupported answers can originate from weak retrieval, stale knowledge, ambiguous prompts, missing business rules or overconfident generation. Reliability therefore needs controls across the entire system rather than a single prompt fix.

Ground the answer before generating

Classify intent
Retrieve approved evidence
Check confidence & policy
Answer, act or escalate

Use effective-dated knowledge

Policies, prices, contracts and operating procedures change. Knowledge should carry active dates and version metadata so an agent does not mix obsolete and current instructions in the same answer.

Preserve provenance

Record which source passages, systems and tool calls supported an answer or action. Provenance gives reviewers a path to inspect why the system responded the way it did and supports auditability.

Human-in-the-loop boundaries

  • Escalate when retrieval confidence is weak or evidence conflicts.
  • Require approval for high-impact actions.
  • Separate recommendation from execution where policy demands it.
  • Continuously evaluate real production conversations, not only curated test prompts.

Build this into an enterprise workflow

Slikk combines grounded knowledge, governed agents, workflow orchestration and human review for production enterprise use cases.

Request a call back