Artificial intelligence makes things up.
If you’ve used AI for any amount of time, you may have noticed that AI can produce outputs that confidently sound true but are misleading, illogical, incorrect, or fake. These are known as AI hallucinations.
AI hallucinations are frustrating when you’re chatting with an AI model like ChatGPT, but they’re a much bigger problem if you’re trying to create business-critical AI-powered automated workflows. Research from OpenAI explains that LLMs hallucinate because they are trained to prioritize fast, confident answers rather than producing an “I don’t know” output. To combat this, we can build in better guardrails and more structured instructions.
Creating AI-powered software, agents, and automated workflows enables businesses to use AI in much more impactful ways than simply chatting with an external LLM platform. However, when you embed AI in your systems, you need a lot more reassurance that it’s not going to expose your data or cause serious problems within your business operations.
Glide’s State of AI in Operations report found that 51% of businesses reported that data privacy and security concerns were their biggest barrier to AI adoption. If you’re creating software or automation systems for your business that use AI, there’s no room for hallucinations in your processes.
Building custom no-code apps with built-in AI guardrails using a platform like Glide can help businesses use AI more safely and accurately. Here’s how you can put AI to work for you, without risking hallucinations impacting your business.
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Businesses using AI can reduce the risk of AI hallucinations by creating structured systems that prevent them from affecting critical processes and building in human-in-the-loop processes to improve accuracy. Using no-code software development gives you additional security, as you gain the assurance of your platform’s security engineers and add a layer of control to your processes instead of using raw AI outputs.
Glide is a no-code platform that allows businesses to build custom AI-powered apps that embed AI workflows directly into their business processes. With a Glide app, AI can safely interact with your business data, automate repetitive tasks, and give your team new abilities directly within the tools they use for work.
There are numerous built-in safety measures for working with the native Glide AI, but there are additional steps you can take to make your apps even safer and more accurate. Here are some of the most impactful steps you can take to reduce the impact of AI hallucinations in business process automation and software development.
Choose the appropriate type of AI for the task
Before designing your AI workflows, decide how you’ll use each kind of AI in your Glide app. Many apps will use both Glide’s native AI and external AI integrations, as each is suited for different tasks.
The techniques in this article apply to both Glide AI and integrations, with notes on where implementation differs.
Glide AI
Glide’s native AI is a managed AI, meaning it handles the technical aspects of using AI models for you. When you incorporate AI into your app, you don’t need to choose which model to use or configure settings. Glide automatically selects the appropriate model based on the task you’re asking it to do.
It also manages updates to AI models. When new models become available or existing models are improved, Glide updates them automatically without requiring any changes to your app. Since the AI actions run through Glide’s enterprise-level accounts, it generates outputs faster, which means your app runs faster.
Glide AI works only with the data you explicitly connect through your app’s columns. It can’t browse the web or access external sources. This makes it safer and more controlled for avoiding hallucinations and working with private data. However, if you need information that’s not in your connected data, you’ll need to use an integration instead.
Integrations
You can use Glide’s native integrations to connect your app directly to AI platforms like Google Gemini, Claude, Azure, or Replicate. You can also make a custom connection to any other AI platform using an API.
These integrations require you to configure and manage the technical settings yourself, such as which AI model to use, the temperature settings, and whether the AI can access external data.
Move to integrations when you need specific capabilities that Glide AI doesn’t offer, like accessing real-time external data, like weather information, or using specialized AI models for your industry.
1. Set AI up with reliable inputs
AI responds based on what it has access to. When you feed it current data and specific instructions, you limit opportunities for it to make guesses or fabricate information.
Design prompts that eliminate ambiguity
Vague prompts leave AI to decide which information is relevant, and it may make things up or pull from the wrong sources. The more specific you are about which data to reference, the less AI has to guess about what information matters.
Tell AI exactly what data to use. In Glide, include specific columns from your data sources as inputs in your AI workflows. If you don’t specify which columns to use, AI will still complete the task, and it’ll fill gaps with what sounds plausible rather than saying “I don’t know.”
If you’re generating work orders in your Glide app, specify that AI should pull from the [unit], [address], [issue type], and [issue description] columns. If certain data, such as tenant information, shouldn’t be included, tell the AI to exclude those columns. AI now knows exactly what data to use and what to leave out.
If you’re using AI through a third-party integration like Google Gemini, being specific also means telling AI whether it can browse the web for information. If your prompt says “Generate a safety inspection checklist,” AI might pull generic OSHA guidelines that don’t apply to your facility. Add “Use only the safety requirements from the [compliance standards] column. Do not browse the web,” and AI stays within your approved procedures.
For more detailed guidance on writing effective prompts, Glide’s prompt engineering guide covers techniques for getting better outputs from AI.
Learn how to instruct AI models more effectively
Read the AI prompt engineering guideSet limits on what external data AI can use
When AI can browse the web freely, it might reference outdated articles, misinterpret data from low-quality sources, or cite sources that don’t actually contain the information it claims. Limiting which external sources AI can reference reduces the risk that it will pull from unreliable information or fabricate sources altogether.
For your Glide app, whether you allow it to browse the web or restrict it to your business data depends on two things:
- the task at hand
- how you integrate AI into your app
Your inventory management app already includes stock levels, supplier information, and reorder thresholds. AI doesn’t need to browse the web to place an order when stock levels are low. However, if you’d like your app to generate a competitive analysis report, you’ll need to pull current market data from the web because that information changes daily and isn’t in your system.
Glide AI doesn’t browse the web and works only with the data you provide in your prompt. If you’re using Glide AI and need external data in your workflow, connect to specific APIs. Add the information from the API into a new column, which you can input into a prompt for the Glide AI action. This gives you control over what sources AI works with.
Commercial construction company Build-360 built an AI workflow that uses Glide AI and an API for external data. The workflow auto-generates project update emails for clients, which include the next day’s weather forecast, pulled from an API. This information is added to the email so clients know whether on-site work will be impacted. Since the generated email pulls only specific and limited information (the day’s weather) from the AI and is otherwise highly structured, they don’t risk sending hallucinations to the customer.
If you use AI integrations like OpenAI that can browse the web, give specific, pre-approved sources to use rather than letting it browse freely. In a prompt that looks like: “Reference pricing data only from [supplier website] and [industry database]. Do not use other sources.” This reduces the chances that AI will grab information from random forums or outdated articles.
Allowing web access may slow down the workflow, but it may be necessary when the information doesn’t exist in your business data. Set these constraints so AI can generate a more accurate response rather than make assumptions because it’s working with incomplete data.
Build-360 cut time spent on customer updates by 90% with an AI agent
Read their story2. Control what outputs AI produces
Setting constraints on AI outputs reduces hallucinations by limiting the creative liberties AI takes when generating responses. When you define boundaries around format, precision, and complexity, AI has less room to improvise or embellish.
Define what a valid output looks like
Defining what AI’s output should look like forces it to work within specific boundaries instead of improvising. When AI must fit its response into predefined fields or formats, it can’t make up details to sound more complete or fill space.
Without constraints, AI generates whatever format seems reasonable, which might not match what your workflow needs. Ask it to summarize a project, and you might get two sentences, five paragraphs, or a bulleted list. Ask it to extract a date, and you might get “March 2026,” “3/15/24,” or “this spring.” These inconsistencies make it impossible to build reliable workflows.
Specify the format in your prompt: maximum character count, number of sentences, choice from predefined options, date format, or structured fields. For a project summary, specify: “Provide a three-sentence summary. Maximum 500 characters.” For date extraction: “Extract the completion date in YYYY-MM-DD format.”
If the information AI needs doesn’t exist in your data, give it explicit permission to say so: “If the conversation does not contain enough information to determine sentiment, respond with ‘Insufficient data for classification’ instead of guessing.”
One powerful constraint is limiting AI to predefined options. In Glide, text-to-choice columns enforce this automatically. For example, when you set up an AI action to analyze customer sentiment, you could require AI to choose between pre-defined options: positive, negative, or neutral. This structural guarantee means you can build on these workflows with follow-up actions, like automatically sending thank-you emails for positive feedback or assigning a support representative to investigate an issue that resulted in negative feedback.
Set how precise the output should be
Lower temperature settings limit how much AI improvises when generating responses. Temperature settings control how creative or literal AI is: higher settings yield more varied, unpredictable outputs, while lower settings yield more consistent, less creative responses.
When AI generates a performance dashboard summary with high temperature settings, it could characterize customer retention as “solid performance” when your team needs to know it dropped from 96% to 89%. Lower temperature settings keep AI focused on the data. The report may state “Customer retention decreased from 96% to 89%” without adding editorial commentary. Your team gets factual information they can act on.
You can manually adjust temperature settings if you use integrations like OpenAI.
Choose the right AI model for the job
Using the right AI model for a task decreases the chances of inaccurate or irrelevant outputs.
Different AI models are built for different tasks. For straightforward tasks like categorizing form responses or extracting specific fields from a document, a lighter, faster model can capably handle the job. If your tasks require reasoning across multiple data points, like identifying patterns in inspection reports across multiple warehouses, a model designed for complex analysis is better suited.
Model cost is also a practical consideration. Models with more complex capabilities cost more to run, and using them for simple, high-volume tasks adds up quickly. If your app processes hundreds of routine data entries daily, a lightweight model keeps costs manageable without sacrificing accuracy.
Glide AI automatically matches the task to the most appropriate model, so your team doesn't need to evaluate different models or understand their individual strengths. If you prefer, you can also integrate other AI models, such as Azure, directly into your Glide app or connect them via an API.
3. Add verification layers
To build trust in AI workflows, verify their accuracy through testing and review processes. Verification layers work alongside good system design to catch hallucinations before they reach customers or affect business decisions.
Use a “human-in-the-loop” workflow
For workflows where AI outputs drive important decisions, adding a human review step acts as a safeguard against hallucinations. AI handles the time-consuming work of generating the output, while a person verifies accuracy before it goes live.
Businesses are adding human review checkpoints in their AI workflows without slowing down the team. Build-360’s workflow uses AI to compile a summarized report for their clients, and then uses an automation to create an email draft that’s already fully filled out right in their field manager’s inbox. However, the AI compiled email doesn’t send automatically. Instead, the last step keeps a human in the loop. The field manager reviews the draft, makes any needed changes, and hits send.
This process saves field managers 8 hours a week compiling reports. This review step takes just a few seconds, and it prevents emails with mistakes in project details from reaching clients.
Break complex workflows into smaller steps
When AI needs to handle a complex task with many steps, break each step into a separate, sequential workflow, with the output of one task serving as the input to the next. AI research shows that AI models get “lost in the middle”: they’re good at remembering the beginning and end of the prompt, but things in the middle get overlooked or forgotten.
Creating chained workflows helps keep AI focused, reduces the risk of hallucinations, and lets you set a human review at each stage to catch mistakes early and improve your process.
A retail customer returns workflow might chain three steps:
- When your team scans a return receipt and adds a photo of the returned item, AI first extracts the original purchase order and the reason for the return, and then analyzes the product’s condition.
- That output acts as the input for a second workflow where AI determines whether the return qualifies for a refund, store credit, or rejection based on your return policy.
- The outcome then triggers a third step where AI drafts the customer notification with the relevant details for your team to review and send.
Glide Agency V88 built a chained AI workflow for a property management firm that prioritizes urgent repair tickets, generates cost estimates, and routes repairs to the right contractor. “What we have here, without any human intervention, is the same process a human would typically carry out, except the AI does 80% to 90% of the work,” says V88 founder Oscar Brooks. “It just requires a human to check over and make sure that things are accurate.”
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Read the interviewBuild reliable AI workflows in your Glide apps
Look for opportunities to use AI to automate processes in your operations. Repetitive or time-consuming tasks like data formatting and data entry, extracting text from images, workflows requiring pattern analysis, or flagging time-sensitive issues are all good use cases for AI workflows.
When you build AI workflows for these processes, apply these hallucination-reduction techniques to make them more reliable.
First, work backward from what you need AI to produce. Define what the output should look like. Is it a three-sentence paragraph, a choice from pre-defined options, a rating on a scale of 1 to 5, or a JSON output with specific fields?
Then identify the data and instructions AI needs to produce this output. Build a specific prompt around which data columns in your Glide app AI should reference, whether AI needs data from external sources, and the output you’ve defined. If you use Glide AI, model selection and temperature control are handled automatically.
Lastly, add review steps to workflows where AI outputs require human decision-making. Test your workflow with real data, refine your prompts based on results, and adjust as needed.
The techniques in this article apply wherever you use AI in your Glide app.
To learn more about using AI in your apps, explore Glide University’s Building AI Business Software certification. Or, if you’d like support from someone who has built these systems before, a Glide Expert can help you design and build workflows specific to your operations.
