How to Mitigate AI Hallucinations in Small Business Workflows

What Are AI Hallucinations?

AI hallucinations occur when a model generates information that sounds plausible but isn’t true.  Large language models (LLMs) are trained to predict the next most likely word, not to fact-check their own responses.  That’s why they sometimes produce statements that look confident but aren’t grounded in real data.

Understanding how to handle LLM hallucinations starts with recognizing that they’re a design challenge, not just a model flaw.

Real Examples of Hallucinations in Action

• Legal errors: In 2023, two New York lawyers were sanctioned for submitting fake ChatGPT-generated case citations in the Avianca lawsuit.
• Customer support: In 2024, Air Canada’s chatbot invented refund terms, leading to a tribunal ruling against the airline.
• Professional tools: A 2025 Stanford study found that leading AI legal research products still produced hallucinated answers 17–33% of the time.

These stories illustrate why mitigating AI hallucinations is so important before AI-generated output reaches customers.

Why Do AI Hallucinations Happen?

In simple terms, LLMs like ChatGPT or Gemini generate text based on patterns, not understanding.  When they face a question that’s unclear, outside their training data, or ambiguous, they may “fill in the blanks.”  Factors that increase the risk include:

• Asking broad, open-ended questions without constraints.
• Using the model for decisions instead of draft generation.
• Not grounding it in your company’s verified data.

Knowing these triggers helps you design systems that reduce hallucinations in AI applications before they start.

What Companies Are Doing to Fix AI Hallucinations

• OpenAI: Researches “honesty-rewarded” training and retrieval grounding to encourage factual answers.
• Google: Gemini models now offer grounding with Google Search to verify factual responses.
• Microsoft: Introduced VeriTrail, tracing where information originates in multi-step AI workflows to flag potential hallucinations.

These efforts show progress, but effective mitigation also depends on how businesses use and integrate AI.

How to Mitigate AI Hallucinations in Real Workflows

Hallucinations can’t be eliminated entirely, but they can be minimized through careful design. Here are the most effective ways to build reliable AI systems:

1. Tune the Temperature Setting

Temperature controls how “creative” a model is.  This is not a setting that is usually provided in consumer-facing apps like ChatGPT or Gemini, but can be implemented by professional software developers through integration platforms.

Lower temperatures produce consistent, repeatable outputs: best for tasks like document preparation, quotes, or invoices.  Higher temperatures encourage creativity, which is useful for marketing ideas or brainstorming content.  The key is matching temperature to the task; professionals experienced in AI implementations can manage this behind the scenes so users don’t have to – they just get output that works.

2. Keep Applications Narrow and Specific

AI performs better when the scope is clear.  Instead of asking “summarize our business performance,” specify “extract last month’s revenue and expenses from this spreadsheet.”  Narrow focus means fewer opportunities for compounding mistakes and a much lower chance of hallucination.  Instead of using one large prompt, it is often better to create a chain of smaller prompts focused on smaller parts of the problem, building to a bigger whole.  A custom-developed solution can make this seamless since you do not have to handle each prompt manually – instead, data flows through the system and is acted upon by a series of prompts as needed.  For example, data could be extracted from an image, then passed through various steps, then used to fill in specific sections of an email template.

3. Use Prompt Engineering and Reasoning Steps

• Structure your prompts as checklists or instructions: “If you can’t find an answer, say ‘unknown.’”
• Break complex tasks into multiple steps (gather → draft → verify).
• Ask the model to cite sources or flag uncertainty when confidence is low.

4. Add Human-in-the-Loop Review

Before sending sensitive communications, invoices, or estimates, route drafts for quick manager approval.  Human oversight catches the rare incorrect output before it reaches a customer… but it’s more than a safeguard.  It’s a way to scale judgment while keeping people focused on high-value work instead of routine typing.

One study found that the average employee spends nearly 11 hours each week drafting emails, many of which are skimmed or ignored.  If those messages are auto-drafted in line with company policy and tone, then even spending a few hours a week on review still represents a massive net time savings.  The reviewer’s job shifts from writing every sentence to simply approving, tweaking, or rejecting with one click.

A well-designed human-in-the-loop process makes this seamless:

• Approval queues: Drafts appear in a shared dashboard or chat thread where managers can approve, edit, or flag for follow-up in seconds.
• Confidence thresholds: Routine, low-risk outputs (like appointment confirmations or internal notes) can auto-send, while higher-impact items pause for review.
• Continuous learning: Each approval or edit teaches the system what “right” looks like, so over time it drafts closer to your voice and reduces review workload naturally.

Keeping a human in the loop doesn’t slow you down; it keeps your brand, policies, and judgment in the driver’s seat.  Think of it as oversight by design: people remain accountable for intent and tone, while AI handles the repetitive composition.  The result is faster output, fewer mistakes, and a workflow that blends reliability with human sense-checking.

5. Combine AI with Clear Rules and Direct Integrations

AI should be viewed as one tool in a larger toolkit: not the entire system.  The strongest automation combines AI for interpretation with deterministic logic for execution.  That means letting integrations, APIs, and workflow tools transmit data directly between systems whenever possible, while using AI only where human-style reasoning or flexible language understanding adds value.

• Validate automatically: Set rules to confirm totals, units, and date ranges before results move downstream.
• Use AI selectively: Let it draft text or extract meaning from unstructured inputs, but prefer handing off clean, structured data to your other systems.
• Favor direct connections: Integration platforms like n8n, Make, or Zapier can move data straight from one app to another, reducing the risk of a digital “game of telephone” where each hand-off adds small distortions.  This avoids compounded errors – even if the AI is used for initial extraction of disparate data sources, it will subsequently be replicated exactly.  (Again, a human-in-the-loop step could be added before information is piped into a database or system of record.)
• Layer policy filters: Block unsupported statements, require citations for factual claims, and enforce “I’m not sure” responses when sources are missing.
• Keep logic outside the model: Let deterministic software handle math, validation, and routing; reserve AI for the human-like reasoning pieces (such as image or audio analysis.)

6. Ground Responses in Verified Sources (RAG)

Even the best rules work best when paired with grounding, also known as Retrieval-Augmented Generation (RAG).  This approach connects the model to approved company documents, databases, or live integrations so its answers are based on your verified data rather than guesses.  By showing citations or linking back to the original records, RAG makes AI outputs explainable, traceable, and far less prone to error.

FAQs: How to Handle LLM Hallucinations in Practice

How Ravensight AI Builds Reliable Automation

At Ravensight AI, we’ve deployed workflows where hallucinations are not a major issue because systems are narrow, grounded, and validated.
Examples include:

• Smart Email Automation: draft replies based on internal policies and approved FAQs.
• Bookkeeping Automation: extract totals and generate reports with numeric validation.
• Document Preparation: fill templates with verified data and pre-approved text.
• Sales & Quote Follow-Up: use AI for drafts but require review before sending.

Explore more on our Solutions page or contact us to discuss a safe, customized AI workflow for your business.

Sources

1. OpenAI: “Why language models hallucinate.” openai.com
2. Google Developers: “Grounding with Google Search in Gemini API.” developers.googleblog.com
3. Google DeepMind: “FACTS Grounding Benchmark.” deepmind.google
4. JMIR: “Hallucination Rates and Reference Accuracy of ChatGPT, GPT-4, and Bard.” jmir.org
5. Stanford: “Assessing the Reliability of Leading AI Legal Research Tools.” dho.stanford.edu
6. Reuters: “New York Lawyers Sanctioned for Using Fake ChatGPT Cases.” reuters.com
7. Forbes: “Air Canada Lost in ‘Lying AI Chatbot’ Case.” forbes.com
8. Microsoft Research: “VeriTrail: Detecting Hallucinations in Multi-Step AI Workflows.” microsoft.com
9. Signshop: “Average Employee Takes This Long To Draft an Email.”  signshop.com