How to Train AI Agents: Understanding the GIGO Concept

Have you ever asked yourself how to train AI Agents? And why this might be important? Picture this: Imagine you are a car dealership trying to get more leads and, potentially, making them schedule a driving test. Or even a banking company that wants to automate the qualification process for one of their financial products. At some point, a potential customer visits your website and asks your AI agent a simple question about one of your car models or a mortgage. Instead of providing a clear answer, the bot responds with outdated or incorrect details; or worse, fabricates something entirely. Frustrated for not getting the information he or she needed to make a decision, the customer leaves, ending up with a lost lead. 

And now you might be wondering: why does this happen? The truth is that it’s not because AI agents are unreliable; it’s because they are only as good as the data they are trained on. Here’s where the “Garbage In, Garbage Out” (GIGO) principle comes into play: if an agent is fed inaccurate, inconsistent, or poorly structured data, it will deliver misleading responses, or are often called ‘hallucinations’.

But when trained correctly, AI agents can revolutionize customer experience and operational efficiency by providing instant support, reducing wait times, and improving customer satisfaction. AI agents can also save money and resources by automating repetitive inquiries, freeing up human agents, and lowering support costs. 

Knowing the cost of opportunity of an inaccurate answer from our AI agents, how can we ensure it provides helpful and trustworthy information? In this blog article, we will cover how to avoid the GIGO pitfall and how to train AI agents, how to properly structure your knowledge base for effective agent training, improving agent accuracy, and minimizing hallucinations.

Let’s dive in to transform your AI agent into a powerful, reliable, and cost-saving business asset.

But, before we start, please, note that, when we talk about “training” AI agents in this context, we’re not actually modifying or training the underlying AI model (like OpenAI’s GPT). Instead, we optimize its responses by providing it with structured knowledge and guiding how it retrieves and uses information. For the techies here, rather than fine-tuning a model, we implement a RAG system that enhances the agent's ability to pull contextually relevant data from a curated Knowledge Base, improving accuracy and relevance.

Understanding the GIGO Principle: Garbage In, Garbage Out

AI agents are powerful tools, but as we all know, they don’t have the capability to "think" like humans. Instead, they rely on the data they are given to generate responses. If that data is messy, inaccurate, or outdated, the agent will inevitably produce unreliable answers, what we understand as Garbage In, Garbage Out (GIGO).

The Cost of Bad Data

Imagine you work at a bank that offers a range of financial services, including mortgages, personal loans, and credit cards. To improve customer service, you introduce an AI agent on the website to handle inquiries about loan eligibility, interest rates, and repayment terms, and ultimately ease the load of your sales and support team. 

Here’s what could go wrong if the agent is trained with inconsistent, outdated, or poorly formatted data:

• Outdated loan terms: A customer asks, "What’s the current interest rate for a home loan?", the agent responds with a 3.5% fixed rate, but the actual rate increased to 4.2% last month. Now, the customer is misinformed, leading to frustration and potential compliance issues.
• Contradictory information: One of the documents you used to feed your AI agent states that customers need a minimum credit score of 650 for a personal loan, while another document lists 700 as the minimum. The agent provides both answers at different times, creating confusion and mistrust. ‍
• Poorly formatted AI agent training materials: If the agent's knowledge base consists of long, unstructured PDFs and scattered FAQ documents, it may struggle to extract relevant information. This could result in vague, unhelpful responses like, "Loan eligibility depends on various factors. Please contact support."—which defeats the purpose of having an AI agent.

Now, on the other hand, let’s see how high-quality, structured data can ensure accuracy and reliability, improving lead quality and customer satisfaction. 

High-Quality Data = High-Quality Responses

If the agent is trained on organized, verified, and regularly updated information, the customer experience can improve dramatically:

• Accurate and up-to-date answers: The agent correctly informs a customer that the current mortgage rate is 4.2%, preventing confusion and ensuring compliance with financial regulations, helping you get a qualified lead who’s more prompt to convert. 
• Consistent information across channels: Whether a customer asks via agent, calls a support agent, or checks the website, they receive the same, reliable answer, so you can build a trusting relationship with that prospect. ‍
• Efficient and helpful customer interactions: Instead of vague replies, the agent can confidently guide users through eligibility criteria, required documents, and loan application processes, building a consistent lead qualification process, improving customer satisfaction, and reducing the workload on human agents.

Why Does GIGO Matter?

Let’s continue with the example of a financial services business. In this case, agents errors aren’t just frustrating; they can be costly and even legally risky. Misinformation about loans, credit approvals, or repayment terms could:

• Mislead customers and cause complaints.
• Hurt your brand’s credibility and trustworthiness.
• Lead to regulatory compliance issues and potential penalties.
• Lost leads due to confusing information and compromised business results.

We clearly don’t want that! To prevent this, in the next section, we’ll cover how to ensure high-quality training data that enhances your agent's performance, reduces misinformation, and keeps responses aligned with business and compliance requirements.

How to Ensure High-Quality AI Training Data and Avoid Hallucinations

By now, we know how the accuracy and reliability of an AI agent depend entirely on the quality of the data it’s trained on. Therefore, if you make sure to provide your AI agent with well-structured, fact-checked, and up-to-date information, it will deliver consistent, trustworthy, and helpful responses. 

Reliable data also avoids hallucinations. In AI, hallucinations refer to when agents generate false, misleading, or nonsensical responses that sound plausible but are not based on real or verified information. This happens because AI models predict text based on patterns rather than truly "understanding" facts.

For example, if an AI agent is asked about a new banking regulation that isn't in its training data, it might fabricate an answer instead of admitting it doesn’t know, leading to misinformation.

To avoid hallucinations, businesses must ground AI in structured, fact-checked data and implement retrieval-based methods that ensure responses are pulled from reliable sources.

We understand the theory and the importance of having trustworthy sources of information, but now let’s get into practice by breaking down specific aspects we need to take into account when structuring our AI training data. 

Use Reliable Data Sources

First, we need to guarantee that we gather trusted, verified, and official content that accurately represents our business information. It’ll depend on every company, but normally the best sources include:

• FAQs and Help Center articles: Well-documented responses to common questions from prospects and customers, as well as extensive articles on how our products and services work, provide a strong foundation for a training.
• Official policy documents and product manuals: It’s important to ensure that AI responses align with your company policies, financial services regulations, or product specifications, as we saw with the financial services example before. 
• Support ticket insights: Analyzing resolved customer queries can help identify gaps and common concerns. With that, you can work on specific documentation that later, can be used to train your AI agent. ‍
• CRM and internal databases: Integrating AI with up-to-date internal records ensures personalized and real-time responses based on your existing lead data.

Maintain Data Formatting and Consistency

We sometimes feel that AI can easily read any type of data, even unstructured text. But the truth is that, like us, a clear structure and formatting will help your AI agent understand the information in a better way. Imagine reading pages and pages of plain text without any bullet points or periods; it’d be messy, to say the least. Therefore, here are three best practices you can implement: 

• Keep data clean and organized: Avoid unnecessary complexity, long-winded paragraphs, or duplicate entries.
• Use standardized terminology: Ensure uniform wording across all sources, the same as you would on your website, to avoid confusion (e.g., “home loan” vs. “mortgage” should be consistent).
• Tag and categorize data: Label content by topic (e.g., “loan eligibility,” “interest rates”) to improve agent retrieval accuracy.
• Use headings and sections: Break down topics with clear titles (e.g., “Eligibility Requirements for Home Loans”).
• Avoid redundancy and contradictions: Ensure there’s one source of truth for each topic to prevent conflicting answers.

Updating and Maintaining Knowledge

Nothing is set in stone, not even the information about your company and your products, and a agent isn’t a “set-and-forget” tool. Remember to update your AI training data accordingly, and perform ongoing maintenance to your AI agent to stay accurate:

• Regular content audits: Periodically review and update agent data to reflect changes.
• AI retraining with new inputs: Train your AI agent on the latest documents and customer interactions to improve the learning process of your bot.‍
• Monitor agent performance: Identify if the agent is delivering incorrect responses and, based on that, refine training data consequently.

Common Pitfalls and How to Avoid Them

We’ve gone through the process of building an AI Agent, and we’ve learned how to train our AI agent. But truth being said, even having all these tips we’ve shared in mind, mistakes can happen which can lead to inconsistent, misleading, or outright incorrect responses, frustrating users and damaging trust in our business. Therefore, let’s review what are some of the most common pitfalls, and how to steer clear of them:

Feeding AI Unverified or Outdated Content

We might wrongly assume that any internal document or knowledge source is suitable for agent training. However, outdated policies, conflicting FAQs, or unverified sources can cause misinformation, putting both customer trust and compliance at risk (especially in industries like finance and healthcare).

How can we avoid it?

• Always make sure to verify the information before adding it to your agent's knowledge base.
• Regularly audit and update agent training data to reflect new policies, pricing, or product updates.
• Implement a single source of truth for critical data to avoid contradictions.

Overloading AI with Irrelevant or Unstructured Data

Since we don’t want to feel we’re missing anything when training our AI agent, we tend to think, “the more data an AI agent has, the smarter it will be.” But the truth is that feeding it long-winded reports, raw customer emails, or inconsistent formatting can actually degrade performance. AI agents need structured and relevant data, not a dump of every document available in our company.

How can we avoid it?

• Prioritize quality over quantity and focus on well-structured, relevant, and frequently asked information.
• Break down complex documents into smaller, well-tagged sections for better AI retrieval.
• Use consistent formatting (headings, bullet points, tagged content) so the AI can process data effectively and easily find what the user is asking.

Over-reliance on AI Without a Human Handover

While it’s true that AI agents can handle a high volume of queries, making them more efficient and a huge help for our Support teams, they cannot replace human expertise, especially when it comes to complex scenarios (e.g., loan approvals, medical advice). Therefore, relying solely on AI without human fallback options can be risky and frustrate users, leading to critical errors and missed opportunities.

How can we avoid it?

• Implement human escalation paths by letting customers request to speak to a human agent when needed.
• Define clear agent handoff rules for scenarios AI shouldn’t handle (e.g., sensitive financial or legal questions).

Final Thoughts

Training an AI agent isn’t just about feeding it information, it’s about feeding it the right information, structuring it properly, and continuously refining it. Avoiding these common pitfalls ensures your agent delivers accurate, relevant, and trustworthy responses, improving the overall customer experience, reducing operational efforts and, ultimately, maximizing ROI.

By following the strategies we have shared in this article, you can build an AI agent that doesn’t just answer questions but adds real value to your conversations with both leads and customers.

Frequent Asked Questions About How to Train an AI Agent

1. What are the best practices for training an AI agent?

Training an AI agent effectively involves several key practices:

• Utilize high-quality, relevant data: Ensure the training data is accurate, up-to-date, and pertinent to the agent's intended functions.
• Maintain consistent formatting: Structured and uniformly formatted data helps the agent understand and retrieve information more efficiently.
• Implement regular updates: Continuously refine and update the agent's knowledge base to reflect new information, products, or services.
• Incorporate human feedback: Use insights from user interactions to improve the agent's responses and address any shortcomings.

2. How can I prevent my AI agent from providing incorrect or irrelevant answers?

To minimize inaccuracies and irrelevance in agent responses:

• Avoid unverified or outdated content: Ensure all training materials are current and sourced from reliable information.
• Focus on pertinent data: Exclude irrelevant information that doesn't align with the agent's purpose.
• Implement human oversight: Establish mechanisms for human review of the agent's performance, especially in complex scenarios.

3. What is the GIGO principle, and how does it relate to AI agent training?

The GIGO (Garbage In, Garbage Out) principle emphasizes that the quality of output is determined by the quality of input. In AI agent training, feeding the model inaccurate or poorly structured data leads to unreliable responses. Conversely, high-quality input data results in more accurate and trustworthy agent interactions.

4. How often should I update my AI agent's training data?

The frequency of updates depends on the nature of your business and the rate at which information changes. Regular audits (monthly or quarterly) are recommended to ensure the agent's knowledge base remains current and accurate.

5. Can I train an AI agent without programming skills?

Yes, platforms like Landbot offer user-friendly interfaces that allow individuals without programming expertise to create and train AI agents. These platforms provide step-by-step guides and support to facilitate the process.

6. How do I handle sensitive information when training my AI agent?

When dealing with sensitive data:

• Anonymize personal information: Remove or obscure any identifiable details to protect privacy.
• Implement data security measures: Ensure that the data storage and processing comply with relevant data protection regulations.
• Limit access: Restrict data access to authorized personnel only.

7. What role does human feedback play in improving AI agent performance?

Human feedback is crucial for refining agent responses. By analyzing user interactions and feedback, developers can identify areas where the agent may be underperforming and make necessary adjustments to enhance accuracy and user satisfaction.

8. How can I measure the effectiveness of my AI agent?

Effectiveness can be assessed through various metrics:

• User satisfaction scores: Gather feedback from users regarding their experience.
• Resolution rates: Track the percentage of inquiries successfully handled by the agent without human intervention.
• Response accuracy: Evaluate the correctness of the information provided by the agent.
• Engagement metrics: Monitor user interaction levels and retention rates.