Tutorials Prompt Engineering Tutorial
Mitigating AI Hallucinations — Complete Guide
Mitigating AI Hallucinations — Complete Guide: free step-by-step lesson with examples, common mistakes, and interview tips — part of Prompt Engineering Tutorial on Toolliyo Academy.
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Prompt Engineering Tutorial · Lesson 73 of 100
Mitigating AI Hallucinations
Prompts ✓ → Apps
Apps · 2 — RAG & agents · ~10 min · Module 8: Prompt Security & Ethics
What is this?
Mitigation stacks retrieval, citation rules, low temperature, schema validation, human review, and eval suites — not one magic phrase.
Why should you care?
PromptVerse Support uses RAG + cite-or-refuse + weekly golden-set eval to track hallucination rate.
See it live — copy this example
Copy the prompt into ChatGPT, Claude, or your LLM API playground and compare outputs.
def answer(q):
ctx = retrieve(q, k=4)
if max_score(ctx) < 0.72: return "No approved answer"
out = llm(cite_prompt(q, ctx), temperature=0.1)
if not cites_valid(out, ctx): return escalate_human(q)
return outWhat happened?
- Low retrieval score → refuse early.
- Low temp for facts.
- cites_valid checks every claim maps to a chunk.
Practice next
- Build 30 Q&A golden set with source docs.
- Measure hallucination rate baseline.
- Add cite-or-refuse rule.
- Add contradiction check between chunks.
- Shadow traffic compare prompt versions.
Remember
Layer retrieve, cite, validate, human. Measure on golden set. Refuse when retrieval weak.
Hallucination OKR
Support bot inventing refund windows.
Outcome: Cite-or-refuse drops fabrications under 3% on eval.
Interview prep for this lesson
Practice these questions aloud after reading—each links to a full structured answer.
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