How to Reduce LLM Hallucination in Production Apps

Yusuf Ishola· March 19, 2025

LLM hallucinations can turn an otherwise impressive AI application into an unreliable mess. When language models confidently generate incorrect information or contradict themselves, they undermine user trust and introduce potential risks to your application.

As an engineer building with LLMs, addressing hallucinations is essential for building reliable AI applications.

In this guide, we will cover a comprehensive, step-by-step approach to reducing hallucinations in your LLM applications. Let's start with simple techniques that you can implement today and gradually progress to more sophisticated solutions for production environments.

Understanding LLM Hallucinations

Before diving into solutions, let's clarify what we're dealing with. Large Language Models hallucinate when they generate information that is incorrect, nonsensical, or unrelated to the input. Common types include:

1. Fact-conflicting hallucinations: When LLMs generate information that contradicts known facts. (e.g. Claiming a triangle has four sides, or that strawberries have 2 'r's.)

2. Input-conflicting hallucinations: When outputs diverge from what was specifically requested or contradict the input material. (e.g. Including statistics in a summary that weren't in the original text.)

3. Context-conflicting hallucinations: When LLMs produce self-contradictory responses, particularly notable in longer outputs. (e.g. "Abraham Lincoln was born on February 12, 1809, in LaRue County, Kentucky" - conflicting with an earlier stated fact that he was born in Hardin County.)

Step 1: Optimize Your Prompts

The simplest place to start is with your prompts. Using strategic prompt engineering to reduce hallucinations is a lot easier than it sounds.

// Less effective: Vague prompt that invites hallucination
const vaguePrompt = "Tell me about the Treaty of Versailles.";

// More effective: Specific prompt with guardrails
const betterPrompt = `Provide key facts about the Treaty of Versailles from 1919.
If you're uncertain about any detail, explicitly state "I'm not certain about this specific detail."
Structure your response with these exact headings:
- Date Signed
- Key Participants
- Major Provisions
- Historical Significance`;

Here are some helpful tips:

• Be specific and clear: Vague prompts lead to vague (and often hallucinated) responses. Include specific instructions about accuracy requirements and how to handle uncertainty.

• Use structured formats: Request outputs in structured formats to constrain the model's freedom to hallucinate.

• Implement Chain-of-Thought: Ask the model to break down its reasoning. This can help you reveal and prevent logical errors. You can also use a reasoning model like OpenAI's o3 or Google's Gemini 2.0 Flash which has reasoning built in. Reasoning often uses more tokens, you can also consider techniques like Chain-of-Draft to save on costs.

We wrote about other prompt engineering techniques, too.

These techniques work by providing the model with clearer guardrails and expectations, reducing the "creative freedom" that often leads to hallucinations.

Step 2: Implement Effective RAG

Retrieval-Augmented Generation (RAG) addresses hallucinations by grounding LLM responses in factual information from reliable sources. The effectiveness of RAG depends heavily on:

1. Content quality: Use authoritative, well-vetted sources as your knowledge base.
2. Retrieval effectiveness: Tune your similarity thresholds and search algorithms
3. Clear instructions: Provide explicit directions for how the model should use the retrieved context
4. Monitoring: Track when and where RAG is failing in your system

Using Helicone's Sessions feature, you can trace the entire RAG process:

// Track your RAG workflow with Helicone Sessions
const response = await openai.chat.completions.create({
  model: "gpt-4o-mini",
  messages: [{ role: "user", content: ragPrompt }],
  headers: {
    "Helicone-Session-Id": sessionId,
    "Helicone-Session-Path": "/rag/retrieval-step",
  }
});

The monitoring step is especially important as without visibility into what data an LLM is retrieving—especially in a multi-step/multi-LLM workflow—confidently deploying it to production is impossible.

Helicone can provide this much-needed visibility with its Sessions feature. Read more about how to debug RAG chatbots with Helicone.

Step 3: Implement Robust Evaluation

You can't improve what you don't measure. This is why setting up robust evaluation methods is critical for identifying and reducing hallucinations in production.

Consider:

• Adding user feedback collection: One of the simplest ways to start tracking hallucinations is by collecting user feedback. Helicone provides built-in tools to collect and analyze this feedback.

• Scoring LLM outputs: Use prebuilt scoring metrics or define ones specific to your domain needs and log them with desired requests. With Helicone you can score outputs manually or via the API. These scores can then be reviewed later to detect hallucination rates.

• Use LLM-as-a-judge evaluators: Set up automated evaluators to score responses for hallucinations without requiring human review for every response.

• Running prompt experiments: Test different prompting approaches side-by-side with Helicone's experiments to quantify which techniques reduce hallucinations most effectively.

• Setting up an alerting system: With Helicone's alerts, you can track when hallucinations are occurring more than usual and swiftly take action.

Step 4: Advanced Techniques

Once you've implemented the basics, these advanced techniques can further minimize hallucinations:

• Fine-tune with high-quality data: Create targeted datasets focusing on areas where your model frequently hallucinates. Try including explicit examples showing when to say "I don't know" rather than guess. Helicone's Datasets feature helps you curate high-quality requests for fine-tuning.

• Implement guardrails: Add rule-based safety controls that monitor interactions and verify factual accuracy against trusted sources. These guardrails can ensure outputs remain grounded in source material.

• A/B test hallucination reduction strategies: Experiment with different approaches by directing traffic to system variants (e.g. a fine-tuned vs. RAG-based system) and comparing hallucination rates. Helicone's Experiments features make this straightforward to implement.

• Combine RAG with fine-tuning: While each approach has strengths, using both retrieval and fine-tuned models together can provide better results than either alone for domain-specific applications.

Conclusion

Reducing LLM hallucinations requires a multi-faceted approach that evolves with your application's needs. Start with the simple techniques—better prompting and basic RAG—and progressively implement more sophisticated solutions as you scale.

Remember that hallucination reduction is an ongoing process—there's currently no one-time fix. Continuously monitor and evaluate your LLMs' outputs, gather user feedback, update your strategies accordingly, and you should be just fine.

You might also like

• How to test your LLM prompts (with examples)
• The case against fine-tuning
• Prompt evaluation explained: Random sampling vs. golden datasets