Episodes

  • Deploying and Maintaining Your Custom GPT for Long-Term Use #S11E10

    Deploying and Maintaining Your Custom GPT for Long-Term Use #S11E10
    Jun 21 2025
    This is season eleven, episode ten. In this episode, we will focus on how to deploy and maintain your custom GPT for long-term success. You will learn how to continuously update AI with new product data, monitor response accuracy, and scale AI-powered customer support across multiple platforms. By the end of this episode, you will have a clear plan for keeping your AI assistant up to date and improving its performance over time. So far, we have trained AI to handle customer queries, product recommendations, pricing, and even complex edge cases. Now, we need to ensure that the AI remains reliable and scalable as your business grows. Let’s go step by step on how to deploy your AI assistant, maintain accuracy, and expand AI support across different channels. Step One: Deploying AI for Daily Customer Support Once your custom GPT is trained and fine-tuned, it is time to deploy it in real customer interactions. AI can be integrated into different support channels, including: Live chat systems on your website for instant customer assistance.Email automation tools to draft replies for customer inquiries.CRM systems to help sales and support teams generate responses.E-commerce platforms to provide product recommendations and pricing. Before launching AI, businesses should test real-world performance by allowing AI to generate draft responses for human review. This ensures that responses are accurate before full automation begins. Step Two: Monitoring AI Performance and Accuracy Once AI is deployed, it is important to track performance metrics and ensure that responses meet customer expectations. Some key performance indicators include: Response accuracy – Are AI-generated answers correct and up to date?Customer satisfaction ratings – Are customers happy with AI responses?Escalation rates – How often does AI transfer queries to human agents?Resolution time – Is AI helping customers get answers faster? Businesses should regularly review AI-generated responses and make adjustments where necessary. If AI frequently fails to answer certain questions, this indicates that training data needs improvement. Step Three: Updating AI with New Product Data and Business Information AI needs regular updates to stay accurate. As products, pricing, and policies change, AI must be trained with the latest information. Businesses should set up a routine update process that includes: Refreshing product catalogs – If new products are added or specifications change, AI must be updated.Updating pricing information – AI should always provide the latest pricing details.Adding new customer support scenarios – If new issues arise, AI should be trained with recent customer interactions. Regular updates ensure that AI remains useful and does not provide outdated or incorrect information. Step Four: Scaling AI-Powered Support Across Multiple Platforms Once AI is working well in one customer support channel, businesses can expand AI assistance to other areas. This could include: Social media messaging – AI can assist customers on platforms like Facebook Messenger or WhatsApp.Voice assistants – AI can be adapted for voice-based customer interactions.Self-service knowledge bases – AI can help customers find relevant information without needing direct support. By expanding AI across multiple platforms, businesses enhance customer support efficiency while reducing the workload on human teams. Step Five: Maintaining a Balance Between AI Automation and Human Support Even as AI takes on more customer interactions, businesses should maintain a balance between automation and human assistance. AI should: Handle repetitive and straightforward inquiries.Provide first-level responses but escalate complex cases.Work alongside human support, not replace it. By keeping human agents involved in critical interactions, businesses preserve the personal touch that customers value while benefiting from AI automation. Key Takeaways from This Episode AI deployment should start with monitored testing before full automation.Businesses should track AI performance and adjust responses as needed.AI must be regularly updated with new product, pricing, and business data.Scaling AI across multiple platforms increases customer support efficiency.Maintaining a balance between AI automation and human oversight ensures better customer experiences. Your Action Step for Today If you are planning to deploy AI for customer support, start by: Defining which platform AI should be integrated into first.Setting up a system for reviewing AI-generated responses before full automation.Scheduling regular updates to keep AI responses accurate and relevant. Taking these steps ensures a smooth and successful AI deployment. What’s Next This concludes Season Eleven: Automating Customer Queries with Custom GPTs. If you have followed every episode, you now have a strong understanding of how to build, train, deploy, and maintain an AI-powered customer support...
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    5 mins
  • Handling Edge Cases – Managing Complex or Uncommon Customer Questions #S11E9

    Handling Edge Cases – Managing Complex or Uncommon Customer Questions #S11E9
    Jun 20 2025
    This is season eleven, episode nine. In this episode, we will focus on how to train AI to handle edge cases, manage complex or uncommon customer questions, and recognize when human intervention is needed. You will learn how to identify situations where AI may struggle, how to design fallback mechanisms, and how to train AI to handle objections, complaints, and unexpected queries. By the end of this episode, you will understand how to ensure AI provides reliable responses while avoiding mistakes in difficult customer interactions. So far, we have integrated AI into chat systems and customer support workflows. Now, we need to prepare AI for situations where standard answers may not be enough. Let’s go step by step on how to train AI for complex queries, set up human intervention rules, and improve AI’s ability to manage difficult customer interactions. Step One: Identifying Edge Cases in Customer Inquiries AI can handle common and repetitive questions well, but sometimes customers ask unexpected or complex questions that do not fit into standard response patterns. These edge cases can include: Vague or unclear questions – A customer asks, “Can you help me with this?” without providing details.Multi-part or layered questions – A customer asks, “What are the product dimensions, and do you offer international shipping?” in a single request.Emotional or complaint-based inquiries – A frustrated customer says, “Your product didn’t work as expected. What are you going to do about it?”Requests outside of AI’s knowledge – A customer asks about an outdated product or an uncommon technical issue. To handle these situations, AI needs to be trained to recognize uncertainty and respond appropriately instead of providing incorrect or misleading answers. Step Two: Designing AI Responses for Unclear or Multi-Part Questions When customers ask vague or unclear questions, AI should be trained to ask clarifying questions rather than making assumptions. For example, if a customer types: “I need help with your product.” AI should not guess what they need but instead respond with: “Of course, I’m happy to help! Could you provide more details about what you need assistance with?” For multi-part questions, AI should be trained to break them down and answer them one by one. If a customer asks: “Can you tell me the price and also explain the warranty policy?” AI should structure its response like this: “The price for this product is two hundred and ninety-nine dollars. Regarding the warranty, we offer a two-year manufacturer’s warranty covering defects. Would you like more details about coverage?” This ensures that all parts of the question are answered clearly without overwhelming the customer with too much information at once. Step Three: Training AI to Recognize and De-escalate Customer Complaints When AI detects frustration, dissatisfaction, or an emotional complaint, it should respond with empathy and avoid defensive or robotic-sounding replies. For example, if a customer writes: “I’m really disappointed. I ordered this product two weeks ago, and it still hasn’t arrived.” AI should not respond with: “Shipping typically takes five to seven business days.” Instead, it should acknowledge the frustration first, then provide useful information: “I understand how frustrating delays can be, and I sincerely apologize for the inconvenience. Let me check the status of your order. Can you provide your order number?” By showing empathy first, AI makes the customer feel heard before providing a solution. Step Four: Setting Up Fallback Mechanisms for AI Uncertainty There will be situations where AI does not have enough information to generate a reliable response. Instead of making up an answer, AI should be trained to use fallback responses and escalate to human support if necessary. Here are some effective fallback strategies: Acknowledging uncertainty while offering an alternative solution – If AI does not know the answer, it can redirect the customer: “I’m not completely sure about that, but I can connect you with a team member who can help.” Providing an estimated timeframe for a response – If human input is needed, AI should set expectations: “I’ll check with our support team and get back to you within twenty-four hours.” Directing customers to additional resources – If AI cannot answer a complex technical question, it can suggest checking a help center or documentation: “That’s a great question. I recommend checking our knowledge base for detailed specifications. Would you like a link?” These fallback responses ensure that AI does not create confusion or frustration by providing incomplete or incorrect answers. Step Five: Handling Unexpected or Unusual Requests Customers sometimes ask unusual or unexpected questions that do not fit into normal support categories. AI should be trained to: Recognize when a question is completely outside ...
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    7 mins
  • Automating Chat Queries – Integrating AI with Customer Support Systems #S11E8

    Automating Chat Queries – Integrating AI with Customer Support Systems #S11E8
    Jun 19 2025
    This is season eleven, episode eight. In this episode, we will focus on how to integrate AI into live chat and customer support systems. You will learn how to connect custom GPTs to real-time chat platforms, define escalation triggers for human intervention, and ensure AI delivers fast but accurate responses. By the end of this episode, you will understand how to automate customer chat support while maintaining high response quality. So far, we have fine-tuned AI-generated responses for accuracy and professionalism. Now, we will take the next step by deploying AI in real-time chat environments where customers expect instant answers. Let’s go step by step on how to set up AI-powered chat support, prevent errors, and ensure human oversight when needed. Step One: Choosing the Right Chat Platform for AI Integration Before integrating AI into your customer chat system, you need to determine where AI should be deployed. Businesses typically use AI-powered chat support in: Website chat widgets to assist visitors in real time.Messaging apps like WhatsApp, Facebook Messenger, and Telegram.E-commerce chatbots to help with product recommendations and orders.Customer service ticketing systems to automate initial responses. If your business already has a live chat system, check if it allows custom AI integration. Many modern chat platforms, such as Zendesk, Intercom, and Freshdesk, allow AI to handle the first level of customer inquiries before escalating to a human agent. Step Two: Training AI to Handle Common Chat Inquiries Chat-based conversations differ from email replies because they require fast, direct responses. AI should be trained to: Recognize short, casual questions and respond in a conversational way.Detect urgency and escalate serious issues to human support.Provide structured answers without overwhelming customers with too much text. For example, if a customer asks, "How long does shipping take?", AI should respond concisely: "Standard shipping takes three to five business days. Express options are also available. Let me know if you need more details!" AI should also be trained to ask follow-up questions when needed. If a customer asks, "Do you have this product in stock?", AI should check the inventory and then ask: "Which color or size are you looking for?" This approach makes AI-powered chat feel more natural and interactive. Step Three: Setting Escalation Triggers for Human Intervention While AI can handle many inquiries, there will be cases where human support is necessary. You need to define clear rules for when AI should transfer a chat to a real person. Common triggers for human escalation include: Complex requests – If a customer asks for a detailed consultation, AI should suggest a human agent.Complaints or disputes – If AI detects frustration or negative sentiment, it should escalate immediately.Custom pricing or contract negotiations – If a customer asks for a personalized quote, AI should flag the request for human review. AI should smoothly transition the conversation, saying something like: "I want to make sure you get the best assistance for this. Let me connect you with a team member who can help!" By implementing these escalation triggers, AI can provide support without frustrating customers who need human attention. Step Four: Preventing AI Errors in Live Chat Unlike email replies, chat conversations happen in real time, so AI must avoid mistakes that could lead to customer frustration. Some key safeguards include: Limiting AI responses to verified information – AI should not guess or make assumptions.Avoiding robotic or repetitive answers – AI should recognize when a customer asks the same question multiple times and vary its response.Allowing customers to override AI suggestions – If a customer prefers to speak with a human immediately, AI should not resist. For example, if AI does not have an answer, it should respond honestly instead of generating a misleading reply: "I am not sure about that, but I can check with our support team and get back to you!" This approach ensures that AI remains helpful and trustworthy rather than giving incorrect or unhelpful answers. Step Five: Monitoring AI Performance and Improving Responses Once AI is handling real-time chat queries, you need to track its performance and improve responses based on customer interactions. Key performance indicators include: Response time – How quickly does AI provide answers?Customer satisfaction – Are customers happy with AI responses, or do they frequently request a human agent?Escalation rates – How often does AI transfer conversations to human support? If AI frequently escalates certain types of questions, this indicates that training data needs improvement. For example, if AI cannot answer technical troubleshooting questions, you may need to add more detailed knowledge base articles to its training. Regular monitoring ensures that AI continues to improve over time and becomes ...
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    7 mins
  • Fine-Tuning Responses – How to Make AI Drafts More Accurate #S11E7

    Fine-Tuning Responses – How to Make AI Drafts More Accurate #S11E7
    Jun 18 2025
    This is season eleven, episode seven. In this episode, we will focus on how to fine-tune AI-generated responses to improve accuracy and professionalism. You will learn how to review and refine AI drafts before sending them to customers, implement human-in-the-loop validation, and train AI to adapt based on feedback. By the end of this episode, you will have a clear strategy for improving AI-generated customer replies, ensuring they are well-structured, clear, and aligned with your business communication style. So far, we have trained AI to handle product recommendations and pricing inquiries. Now, we will take the next step by making AI-generated responses as polished and effective as possible. Let’s go step by step on how to review and improve AI drafts, train AI using real-world feedback, and ensure human oversight where necessary. Step One: Reviewing AI-Generated Drafts for Clarity and Accuracy Even though AI can generate relevant and structured responses, it does not always produce perfect answers. Before fully automating responses, businesses should review AI-generated drafts to ensure they meet quality standards. When reviewing AI-generated drafts, focus on these key areas: Clarity: Does the response clearly answer the customer’s question?Accuracy: Is the information correct and up to date?Tone: Does the response align with your brand’s voice?Completeness: Does the response provide all the necessary details, or does it require follow-up clarification? For example, if an AI-generated response is too vague, you might need to refine it. Instead of saying: "Our product has a long battery life." A refined version would be: "Our product has a battery life of ten hours on a full charge, making it ideal for extended use." By reviewing and refining responses, you improve customer trust and reduce misunderstandings. Step Two: Implementing Human-in-the-Loop Validation While AI can handle many customer inquiries, some responses should still be reviewed by a human before they are sent. This process is called human-in-the-loop validation. Here are some situations where human review should be required: High-value transactions or custom quotations – If AI generates a quote for a large order, a human should verify the numbers before finalizing the response.Complex customer inquiries – If the customer’s question is unclear or does not match past queries, AI should flag it for review.Sensitive or complaint-related messages – If the customer is unhappy or filing a complaint, human review is necessary to ensure the response is empathetic and professional. By implementing review checkpoints, AI-generated responses remain accurate, polite, and contextually appropriate. Step Three: Training AI to Improve Based on Real-World Feedback AI models improve over time when they learn from corrections and feedback. To fine-tune responses, businesses should analyze AI-generated drafts and track how they are modified before being sent to customers. Here’s how you can improve AI responses based on feedback: Identify common errors in AI drafts – Are responses too generic? Do they lack details?Track manual edits and improvements – Which words or phrases are being adjusted?Refine AI training data based on past corrections – Provide AI with better examples of well-written responses. For example, if AI frequently generates responses that lack specific details, provide training examples that include fully detailed replies with product names, key features, and pricing. Over time, AI will adapt and generate responses that require fewer human modifications. Step Four: Setting Up Rules for AI Response Consistency AI should follow specific rules to maintain response quality across all customer interactions. These rules should be documented and included in the AI’s instructions. Some important response rules include: Use complete sentences and avoid vague answers.Always mention key product details instead of general descriptions.Keep the tone professional and friendly, avoiding overly robotic language.If the AI does not have enough information, it should ask a clarifying question instead of making assumptions. For example, instead of responding with: "This product might work for you." AI should be trained to say: "This product is designed for your application, but I would need more details to confirm the best option for your needs. Could you provide more information about your use case?" By enforcing these rules, AI-generated responses become more reliable and consistent. Step Five: Automating Continuous AI Improvement AI should not remain static. As customer needs change and product offerings evolve, the AI model must be updated. Businesses should set up a system to monitor AI performance and refine responses regularly. Here are ways to ensure AI continues improving: Regularly update AI training data with new product details and customer feedback.Monitor customer satisfaction with AI-generated responses – ...
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    7 mins
  • Building Product Recommendation Logic Based on Customer Needs #S11E6

    Building Product Recommendation Logic Based on Customer Needs #S11E6
    Jun 17 2025
    This is season eleven, episode six. In this episode, we will focus on how to train a custom GPT to recommend the right products based on customer needs. You will learn how to classify products by application, teach AI how to match customer requirements with the best options, and use structured decision-making models to improve AI-driven recommendations. By the end of this episode, you will know how to create an AI assistant that helps customers choose the right product, just like an experienced salesperson. So far, we have trained AI to handle pricing and quotations. Now, we are moving into a more advanced task—helping customers select the right product based on their needs. Let’s go step by step on how to classify products, define product selection rules, and train AI to provide personalized recommendations. Step One: Categorizing Products by Application and Use Case Before AI can recommend the best product, it needs a clear understanding of how products are grouped and which ones are best suited for different applications. Most businesses sell products that can be categorized by features, intended users, and specific applications. For example: If you sell electronics, products may be categorized by battery life, power output, or connectivity.If you sell medical devices, categories may include patient type, use case, and compliance with regulations.If you sell software, categories may focus on features, subscription levels, and integrations. By grouping products into categories, AI can match customer questions with the right product based on key attributes. Start by reviewing common customer requests and defining which product features are most important in their decision-making process. This will serve as the foundation for AI recommendations. Step Two: Training AI to Recognize Customer Requirements Once products are categorized, AI needs to learn how to understand customer requirements and map them to the right product. For example, customers might describe their needs in different ways: One customer might ask: “Which product is best for high-speed performance?”Another might say: “I need a product that works well in outdoor conditions.” Even though the wording is different, both customers are asking for a specific product feature. AI must be trained to recognize key phrases and match them with the appropriate product category. To do this, AI training should include: Common questions customers ask about product features.Standardized responses that guide customers to the right options.Follow-up questions if AI needs more details before recommending a product. For example, if a customer asks, “What is the best option for cold-weather use?”, the AI should respond with: “To recommend the best product for cold-weather conditions, I need to confirm a few details. Will the product be used for outdoor activities, industrial applications, or personal use?” This approach ensures AI gathers enough information before making a recommendation. Step Three: Creating a Decision Tree Model for AI Recommendations To improve AI-driven recommendations, you need to define a structured process for decision-making. One of the best ways to do this is by using a decision tree model. A decision tree is a set of rules that guide AI through a series of logical steps before recommending a product. For example, if you sell fitness equipment, the AI’s decision process might look like this: If the customer wants cardio training equipment, recommend treadmills or stationary bikes.If the customer prefers strength training, recommend weight sets or resistance bands.If the customer needs compact equipment, suggest foldable or portable options. By defining these selection rules, AI can provide more accurate and tailored product recommendations. Step Four: Refining AI Responses to Sound More Human and Helpful Even when AI provides correct recommendations, it should still sound like a human assistant rather than a search engine. Here are some ways to make AI-generated responses more conversational and engaging: Use natural phrasing. Instead of saying, “The best option based on your request is Model X.”, AI should say, “Based on what you are looking for, I would recommend Model X because it offers high performance and is designed for your specific needs.”Offer comparisons when necessary. If multiple products fit the customer’s needs, AI should explain the key differences. Example: “Model X is great for high-speed performance, while Model Y is better for durability and long battery life.”Encourage further engagement. AI should invite customers to ask follow-up questions or request additional details. Example: “Would you like me to compare two options side by side?” These refinements make AI more helpful and user-friendly, leading to better customer satisfaction. Step Five: Handling Customer Uncertainty and Alternative Suggestions Sometimes, customers are not sure what they need, and their ...
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    7 mins
  • Training the GPT to Handle Quotation Requests and Price Inquiries #S11E5

    Training the GPT to Handle Quotation Requests and Price Inquiries #S11E5
    Jun 16 2025
    This is season eleven, episode five. In this episode, we will focus on how to train a custom GPT to handle quotation requests and price inquiries accurately. You will learn how to structure pricing data, define rules for customized quotes, and ensure AI-generated responses are correct and reliable. By the end of this episode, you will know how to make your AI assistant generate pricing responses that are clear, professional, and aligned with your business policies. So far, we have integrated product specifications and pricing data into our custom GPT. Now, we need to ensure that AI-generated quotations follow business rules and provide the right pricing information based on customer needs. Let’s go step by step on how to structure pricing data, automate quotation requests, and prevent errors in AI-generated pricing responses. Step One: Organizing Pricing Data for AI Use Before training a custom GPT to provide quotations, we need to ensure that pricing information is structured in a way that AI can reference easily. Pricing data can include: Standard pricing for each productBulk pricing discounts based on order volumeCustom pricing for specific customer groups such as resellers or partnersAdditional costs like shipping fees or customization charges If your pricing changes frequently, storing this data in a structured document allows the AI to pull the most up-to-date information. The key is to make sure that each product has a clear price listing along with any conditions that affect pricing. For example, if your business offers different price tiers based on order quantity, AI should be trained to recognize volume-based discounts and apply the correct pricing level. Step Two: Training AI to Recognize Different Pricing Scenarios Customers request pricing in many different ways. Some might ask for a single product price, while others need a bulk order quotation. The AI must understand these differences and provide the correct response based on context. Here are some common pricing scenarios and how AI should handle them: Single product price inquiry – If a customer asks for the price of one specific product, the AI should respond with the standard unit price.Bulk pricing inquiry – If a customer asks for pricing based on order quantity, the AI should reference the appropriate discount tier and provide a breakdown.Custom quotes for large orders – If the order exceeds a certain value, the AI should request additional details before generating a quote.International pricing – If pricing varies based on region, AI should confirm the customer’s location before providing an answer.Shipping cost estimation – If the total price depends on shipping costs, AI should either provide an estimate or request additional location details. By training the AI to recognize these different pricing scenarios, it can provide more relevant and accurate responses. Step Three: Handling Custom Quotations and Special Pricing Requests Not all price inquiries follow a fixed structure. Some customers may ask for personalized quotations based on their specific needs. AI should be trained to gather the necessary details before generating a response. For example, if a customer requests a custom quote for a large order with custom branding, the AI should follow a structured response format, such as: Acknowledge the request and confirm the details.Ask follow-up questions if necessary, such as order quantity, delivery deadline, or customization options.Provide an estimated quote if the conditions are straightforward.If human review is required, let the customer know that a sales representative will follow up. This approach ensures that AI responses remain professional and accurate without over-promising information that requires manual verification. Step Four: Preventing Errors in AI-Generated Price Quotes One of the biggest risks in automating pricing responses is incorrect or misleading quotations. If AI provides the wrong pricing, it can cause confusion and frustration for customers. To prevent this, you need to define safeguards and validation checks. Here are some ways to prevent pricing errors: Set response limits – AI should not provide price quotes beyond a certain threshold without human approval.Include disclaimers where necessary – If prices fluctuate based on market conditions, AI responses should mention that final pricing will be confirmed by the sales team.Use fallback responses – If AI cannot confidently provide a price, it should say: “For a detailed quotation, our team will review your request and get back to you shortly.” These measures ensure that AI remains a useful assistant rather than an independent decision-maker for critical pricing information. Step Five: Training AI to Handle Follow-Up Questions on Pricing Customers often have follow-up questions after receiving a price quote. AI should be trained to anticipate and handle these follow-ups efficiently. Some common follow-up ...
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    7 mins
  • Integrating Product Information, Specifications, and Pricing #S11E4

    Integrating Product Information, Specifications, and Pricing #S11E4
    Jun 15 2025
    This is season eleven, episode four. In this episode, we will focus on how to integrate product information, specifications, and pricing into your custom GPT. You will learn how to structure product sheets, organize data in formats that AI can understand, and ensure that your AI assistant retrieves the correct details for customer queries. By the end of this episode, you will know how to provide customers with accurate and consistent responses about product specifications and pricing without needing to check details manually every time. So far, we have prepared past customer responses and trained a custom GPT with structured knowledge. Now, we need to ensure that AI-generated responses are precise and aligned with business data. This is especially important when customers ask about technical specifications, compatibility, or pricing. Let’s go step by step on how to structure product details for AI use and how to ensure ChatGPT delivers the right answers every time. Step One: Organizing Product Information for AI Use Before your AI can provide accurate answers, it must have a structured way to access product details. Most businesses already have product information in different formats, such as: Product catalogs with technical specificationsInternal documents listing product features and benefitsSpreadsheets containing product dimensions, materials, and capabilitiesPricing sheets with different costs for various customer segments The challenge is that this information is often scattered across multiple files or systems. To make it useful for ChatGPT, you need to consolidate and standardize this data. One way to do this is by creating a structured product sheet. Each row or entry should represent a single product, and each column should include key attributes such as product name, dimensions, weight, materials, compatibility, and unique features. This ensures that when the AI retrieves information, it pulls the correct specifications every time. Step Two: Formatting Product Data for AI Retrieval AI works best when data is structured in a way that is easy to read and reference. Instead of long, unstructured text, organize your product details consistently across all entries. For example, if your business sells electronic devices, the details for each product should include attributes like battery life, charging time, weight, connectivity options, and warranty period. If you are selling industrial equipment, the attributes might include power consumption, operating temperature range, material composition, and compliance with regulations. A consistent format helps the AI recognize patterns and generate accurate and reliable responses when customers ask for product details. Step Three: Teaching AI How to Retrieve Product Specifications Now that your product data is structured, you need to train your custom GPT to reference it correctly. AI needs to understand where the information is stored and how to use it in responses. There are two approaches to doing this: First, embedding product data in the training process. This means including structured product information as part of the AI’s knowledge base. When fine-tuning your AI, provide examples of how product details should be included in responses. For example, if a customer asks about a specific product’s size, the AI should follow a predefined format when answering, such as: “The dimensions of this product are fifteen centimeters in length, ten centimeters in width, and five centimeters in height.” By training the AI with properly formatted responses, you ensure that it pulls data correctly every time. Second, using external references. If your product information changes frequently, it is best to store it in a separate location, such as a cloud-based document or an internal database. This way, the AI can reference the most recent version without requiring manual updates to its training data. Step Four: Integrating Pricing Information and Custom Quotations Pricing is another area where accuracy is critical. Customers often request cost estimates, bulk pricing, or customized quotations based on specific needs. To ensure AI provides the right answers, your pricing data must be: Organized into clear pricing tiers, such as retail pricing, bulk discounts, and partner pricing.Updated regularly to reflect current rates. If pricing changes frequently, ensure AI has access to the latest figures.Flexible enough to account for variations. If different products have different pricing rules, define these clearly so the AI applies them correctly. For businesses that generate custom quotations, AI can be trained to ask follow-up questions before providing a price. Instead of giving an incorrect estimate, the AI can respond with: “To generate an accurate quotation, I need to confirm a few details. How many units do you need, and will you require additional customization?” This approach prevents AI from providing incorrect information while keeping ...
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    7 mins
  • Automating Customer Queries with Custom GPTs (Season 11 Introduction) #S11E0

    Automating Customer Queries with Custom GPTs (Season 11 Introduction) #S11E0
    Jun 14 2025

    Welcome to Season 11 of the ChatGPT Masterclass: AI Skills for Business Success. This season is all about automating customer queries using custom GPTs—helping businesses respond faster, improve customer experience, and reduce manual workload.

    Instead of spending hours answering the same questions, businesses can train a custom AI assistant to handle email replies, chat support, and quotation requests with accuracy and consistency.

    This podcast is made possible with AI text-to-speech technology, allowing me to efficiently share these insights while you focus on implementing them in your business.

    Who Is Season 11 For?

    This season is for you if:

    • You handle customer support, sales, or business inquiries and want to automate repetitive responses.
    • You want to build a custom AI assistant trained on your business data to improve response accuracy.
    • You need faster and more consistent replies to emails, chat messages, and customer requests.

    What You Will Learn in Season 11

    By the end of this season, you will know how to:

    • Train a custom GPT to handle customer emails, chats, and FAQs.
    • Use past email replies and structured data to improve AI-generated responses.
    • Automate quotation requests while keeping control over pricing accuracy.
    • Fine-tune AI-generated customer interactions for better engagement.
    • Integrate AI into chat systems to improve real-time support.

    Why This Season Matters

    Customer support can take up hours of valuable time, but AI can:

    • Reduce response time by generating fast, consistent replies.
    • Improve customer satisfaction with well-structured, human-like responses.
    • Free up human agents to focus on complex or high-priority issues.

    By automating common queries, businesses can scale customer interactions without increasing workload.

    What to Expect in Each Episode

    Each episode is five minutes long and focuses on a specific step in building an AI-powered customer support system. Here’s what’s coming:

    • Episode 1: Why Automate Customer Queries with Custom GPTs?
    • Episode 2: Preparing Data – Collecting and Structuring Past Customer Replies
    • Episode 3: Creating a Custom GPT – First Steps to Training an AI Assistant
    • Episode 4: Integrating Product Information, Specifications, and Pricing
    • Episode 5: Training the GPT to Handle Quotation Requests and Price Inquiries
    • Episode 6: Building Product Recommendation Logic Based on Customer Needs
    • Episode 7: Fine-Tuning Responses – How to Make AI Drafts More Accurate
    • Episode 8: Automating Chat Queries – Integrating AI with Customer Support Systems
    • Episode 9: Handling Edge Cases – Managing Complex or Uncommon Customer Questions
    • Episode 10: Deploying and Maintaining Your Custom GPT for Long-Term Use

    By the end of this season, you’ll have a fully functional AI-powered system for handling customer inquiries, helping you save time, improve accuracy, and scale your customer support.

    If you’re ready to build an AI assistant for customer communication, start with Episode 1 now. Let’s get started.
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    3 mins