Detecting & Mitigating Hallucinations
Hallucinations are confident but false LLM outputs. Learn why they happen, how to detect them, and concrete techniques to reduce them in production.
Detecting & Mitigating Hallucinations is a free Prompt Engineering & LLM Optimization for Developers lesson on CoddyKit — lesson 4 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the Prompt Engineering & LLM Optimization for Developers learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
What is a Hallucination?
A hallucination is when an LLM produces text that is fluent and confident but factually wrong or unsupported by any source.
Because the output looks authoritative, hallucinations are dangerous in user-facing apps.
Why They Happen
LLMs predict the most likely next token, not the truth. With no grounding, they fill gaps with plausible-sounding inventions.
- Missing knowledge in training data
- Ambiguous or leading prompts
- Pressure to always answer
Types of Hallucination
Two broad categories:
- Factual: wrong dates, fake citations, invented APIs
- Faithfulness: answer contradicts the provided context (common in RAG)
Grounding with Context
The strongest defense is grounding: give the model trusted source text and instruct it to answer only from that text.
Answer ONLY using the context below.
If the answer is not in the context, say "I do not know".
Context:
{retrieved_docs}Forcing Citations
Ask the model to cite which passage supports each claim. Unsupported sentences become easy to spot and verify.
For each sentence, add a [doc_id] citation.
Do not make claims you cannot cite.Lowering Temperature
Higher temperature increases creativity — and invention. For factual tasks, set a low temperature so the model stays close to high-probability, well-grounded tokens.
const res = await client.chat.completions.create({
model: "gpt-4o-mini",
temperature: 0,
messages
});Self-Consistency Checks
Generate the answer several times. If the model gives different facts each run, the claim is likely a hallucination. Agreement is a weak but useful signal of reliability.
LLM-as-a-Judge Verification
Use a second model call to check whether the answer is supported by the context. The judge returns a faithfulness verdict you can act on.
Does the ANSWER follow only from the CONTEXT?
Reply: SUPPORTED, PARTIAL, or UNSUPPORTED.Programmatic Validation
When outputs are structured, validate them. A cited URL should resolve, a quoted number should match the source, a JSON field should match a schema.
function validateCitation(cit, docs) {
return docs.some(d => d.id === cit.doc_id);
}Letting the Model Abstain
Give the model permission to say I do not know. Removing the pressure to always answer measurably reduces fabricated content.
Human Review for High Stakes
For medical, legal, or financial outputs, route low-confidence or uncited answers to a human before showing them to users. Automation plus oversight beats either alone.
Quick Check
Test your understanding.
Recap
You learned to fight hallucinations: ground answers in trusted context, force citations, lower temperature, use self-consistency and LLM-as-a-judge checks, validate outputs programmatically, allow abstention, and add human review for high-stakes cases.
Frequently asked questions
Is the “Detecting & Mitigating Hallucinations” lesson free?
Yes — the full text of “Detecting & Mitigating Hallucinations” is free to read here on the web, and the Prompt Engineering & LLM Optimization for Developers course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the Prompt Engineering & LLM Optimization for Developers course, upgrade to CoddyKit PRO.
What will I learn in “Detecting & Mitigating Hallucinations”?
Hallucinations are confident but false LLM outputs. Learn why they happen, how to detect them, and concrete techniques to reduce them in production. You practise Prompt Engineering & LLM Optimization for Developers with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start Prompt Engineering & LLM Optimization for Developers?
No prior experience is required. Prompt Engineering & LLM Optimization for Developers on CoddyKit is structured for beginners through advanced learners; this is — lesson 4 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Detecting & Mitigating Hallucinations” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this Prompt Engineering & LLM Optimization for Developers lesson?
Yes. Every Prompt Engineering & LLM Optimization for Developers lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- LLM Evaluation Metrics & Benchmarks
- Human-in-the-Loop Feedback Systems
- Prompt Injection & Security Best Practices
- Detecting & Mitigating Hallucinations