Hello everyone. It's a pleasure to be here to talk about a topic that I'm very excited about, prompt engineering for e-commerce ai. Today I will be exploring how strategic prompt design can harness the power of AI to transform the tools we provide to online sellers, and in turn, enhance the customer experience across the e-commerce industry. By 2030, we envision a landscape where a driven assistance are embedded in every aspect of online retail, and effective, prompt design will be a key to making that happen. For a AI to really drive this benefit, we need to give it the right guidance. And that's where prompt engineering comes in. What do I mean by prompt engineering? Essentially, it's the practice of grafting clear strategic instruction or questions for an AA model so that it produce the output we want. Instead of treating the AI like a block box, we guided step by step with well-written prompts. In e-commerce, AI isn't just a nice to have anymore. It's becoming a, it's becoming a competitive necessity. Companies are using AI across the board to personalize the customer experiences, to forecast the demand, to automate marketing and logistic and more and much more. And with the emergence of powerful generative AI tools like Open As GBT or like RO Cloud, even smaller sellers can tap into AI for things like content generation, answering customer questions and making product recommendation. But the effectiveness of all these application hinges on how we prompt them. If you give an AI a poorly word or vague prompt, you will get a sub power result. Conversely, your well designed prompt can unlock incredibly useful responses. In short, prompt engineering is essential to leverage as power in e-commerce and drive the real business value from it. To structure our discussion today, here is a overview of our agenda. We'll start by looking at why prompt engineering matters in this contexts. Essentially, how good prompts amplify the impact of e-commerce ai. I will then start some prompt strategies and best practices. For example, a framework called CoStar. To answer your prompts are effective. We will dive into personalization showing how prompt driven AI can tailor product recommendation and experiences for individual softwares, and we will cover how AI can help with inventory forecasting and dynamic price optimization to improve operations. There is lot to cover, so let's drive in after that. We will circle back to how AI can generate content, things like product listings and description and what kind of issue o benefits that brings in. Then we'll discuss conversational commerce, which covers chat bots and wise assistant, and how prompt design guides those interactions. Next, I also some of the RIY and advantages. And finally we will look into the future outlook toward 2030, things like that. Let's dive in. Why does prompt engineering matter so much in e-commerce? AI put simply and a's output depends directly on prompt guiding it, the quality and relevance of what AI produce are tied to how we ask for it. If our instructions are vague or poorly thought about, we will likely get suboptimal. S conversely, a clear, grafted prompt, yield more useful outputs. Prompt engineering also allows us to inject our domain knowledge into the as process. We can incorporate our company's specific expertise, our brand wise, and context of our customers, right into the prompt that makes the a response more relevant and on brand. Without that, the A might give generic answers and don't quite fit our business or audience. We should also recognize that generative AI is already transforming e-commerce. New AA tools like GPT and Cloud are enabling wide range of capabilities from personalization, product recommendation to automated content generation and customer service. Every major e-commerce player is exploring these technologies because they can dramatically enhance customer experience and streamline operations. By mastering prompt engineering companies can truly harness this generative AI revolution. It lets you to take these powerful AI models and aim them precisely at your business. Problems they pay off is better customer experience, higher sales, and more efficient operations. In soft prompt engineering turns a generic AI into tailored AI for your e-commerce needs, which is why it is so crucial. So how can we craft gra great prompts? There are few best practices and even frameworks to guide us. One useful checklist goes by an acronym, CoStar. Which I will explain in a moment, but let's start with simple general principles First, it's important to set constraints and be ready to iterate. When I, when you write a prompt, include any necessary limits or requirements right in the text. For example, if you need the a's response to be under a hundred words, or you wanted to avoid certain content. Say that upfront. Then treat prompting as an iterative process. Review a's output, and refine your prompt if needed. You might not get the perfect answer on the first try, but try tweaking the prompt. You can hone in on a great result. Next, consider using a structured framework like CoStar. To cover all key element of a prompt. CoStar stands for context, objective style tone, audience and response format. It is basically a prompt writing checklist. Ensure your prompt addresses. Each of these element can lead to more on target results. For example, context means providing background information. The AA should know objective means stating clearly what outcome you are looking for, style and tone referred to the manner or voice you want. The answer audience reminds you to indicate who is the answer is for, and response format means telling ai. If you want a list, a narrative bullet point or JSON format. By covering all these bases, you give the AI your well-defined job, which usually means a better output. Another best practice is to always provide enough context and clarity. If you are prompt is too short or too vague, the A might fill in the blanks in the way you don't intend. So include relevant details. For instance, rather than asking what product should I recommend, you might say you are a virtual assistant helping a customer who likes outdoor activities based on their browsing history. What product would you recommend and why? The second prompt use the ai, a role, a background, and a specific task, and you are more likely to get a useful answer. This type. This ties into defining the A's role tone and style explicitly. As I hinted, you can start a prompt by setting a persona. Finally, use examples and format guidelines to steer the ai. If you can show the ai, what do you expect? Even in a small way, it helps. This could be as simple as providing a sample output or explicitly saying, please respond in three blood point. For instance, you might include in your prompt. Here is an example of good answer, our answer with a sort paragraph followed by a bullet list of three key features. Supplying these cues and prompts helps the AI follow your content closely. Remember, the AI can only do what we ask it to do, so the more precisely we ask, the better the results. Now let's switch into some concrete use cases, starting with personalization. A powered personalization is indeed changing the game in e-commerce. With the latest generative models, we can build very rich customer profiles and deliver highly tailored product recommendation and shopping experiences. It's, it is as if each shopper has their own personal assistant guiding them through your store. Here is how it works. Sellers can incorporate specific customer contacts. And preferences us into their prompt given to the ai. For example, I could prompt an AI with something like the customer recently bought Running source and has been browsing trial running gear. Give that context, suggest three other products that this customer is likely to be interested in. With a one sentence rationally for each. By guiding the AI with kind of a detailed contracts, it'll generate recommendation that truly resonate with that individual saw person's need and taste. Each customer in effect gets suggestions that feel handpicked for them. This level of personalization increases. Engagement and the likelihood that the customer finds exactly what they're looking for. So what kind of impact can personalization have? The numbers are pretty impressive. Retailers who excel at personalization product recommendation have seen up to 40% higher revenue compared to those who don't leverage personalization. Customer loyalty and retention are also a huge boost. Some business report or 50% higher customer retention rates when they get personalization right, and personalized suggestions often lead to bigger BA sizes. Isis, for instance, one study found roughly 10% increase in average order value from the effective product recommendation. In short, personalization isn't just nice to have. It's a significant driver of business results when customers feels like so shopping experience is tailored to them. They tend to buy more, stick around longer and stay more loyal. These data points underscore why investing in a driven personalization is important. Next, let's look at inventory and demand forecasting. Another area where AI combined with the right prompts is making huge difference. Traditionally forecasting demand was often only about 60% accurate, but companies that have adopted a driven forecasting have significantly improved in that. With accuracy rising to 80%, that's a big jump, meaning you are predict your prediction of what stock is needed are much closer to reality. As a result, you can align stock levels much better with actual customer demand, and when you have a better handle on demand. You avoid those detailed dreaded stock stockouts, the situations where a product runs out. In fact, a driven forecasting has been able to cut down stockouts by up to 65%. In some cases, think about 65% fewer out of stock messages. That means customers are far more li, far more likely to find the item they want is available, which of course translated to more sales, happier customer. It is also worth noting that a forecasting was in a much wider range of data than traditional methods, a human or legacy system. Might look only at past sales trends, but AI can factor in unstructured data like social media, bus weather forecast, or news events that could influence demand. By analyzing these additional signals alongside the historical sales, the AI builds a more holistic view of what might drive demand up or down. This often leads to more accurate forecast. For example, you could prompt AI given last year sales data and the recent virtual, so social media maintenance of our product predict next month's demand for each category. A well-designed a system will consider both the past results and the current social media trend to produce a nuanced forecast. It might catch, say that a surgeon online interest will likely to translate into higher sales next month. Something a traditional model would easily miss. This kind of smarter context of our forecasting is becoming a real competitive advantage in retail alongside the forecasting. AI can help optimize pricing in real time. Dynamic pricing means adjusting prices on the fly based on various factors, and AI can juggle all those factors for better than a person can. For example, an AI driven pricing model will consider current inventory levels. If a product is overstocked, it might suggest a slight discount to move it faster. Whereas if the inventory is low and demand is high, it could recommend holding the price steady or even nudging it up slightly. It'll look at market trends. If there is a surge demand for a category or a seasoned seasonal uptake, then AI reacts accordingly. It'll monitor competitor pricing. If competitive drops their price, a can quickly advise a price adjustment to stay competitive, and it can even factor in customer sentiment from reviews or social media. For instance, if a product is getting rave reviews and a lot of bus, the AI might recognize that people are willing to pay a premium for it. Whereas the negative sentiment might trigger a price drop or a promotional offer. By synthesizing all these inputs, inventory, demand, competitor pricing, sentiment, et cetera, a can suggest an optimal price at any given moment to maximize revenue or profit. This level of tiny, big pricing responds much faster to market changes. Than any manual pricing team could. It helps since you are not leaving the money on the table during the high demand and not stuck with unsold stock during low demand. In start. AI driven dynamic pricing can protect margins while also keeping customer satisfied that prices are far responsive to the market. All these improvements in forecasting and pricing ultimately source up on the bottom line. In fact, a study by Zi Company found that companies using companies using AI in their supply chain and operations saw striking results. They managed to reduce. Inventory levels by 20 to 30% on average, which means less capital tied up in stock and lower storage costs. They also cut warehousing costs by about 15 to 20%. Since with linear inventory, you need to less space and you can operate more effectively. Logistic expense fell by a similar 15 to 20% as well due to a smarter routing. And fewer last minute urgent shipments. Perhaps more impressively, these companies decrease stockout incidents by up to 65%. This aligns with what we discussed earlier. Better demand forecasting means self stay stocked with what customer wants, so you capture more sales instead of losing customer to an out of stock message. They also reported improvements in. Sell through rates. All of these metrics add up to a major ROI. From A Driven Operations, you are cutting cost in inventory and logistic and simultaneously increasing revenue by selling more of what you are, what you stock. It's great illustration that investing in AI and pro engineering isn't just about. Cool tech. It has tangible business benefits from efficiency guide to revenue growth. Now sift our focus to content creation, specifically how AI can generate product listing and description and what that means for ses. As many of writing good product copy for every item. The titles, the description, the bullet point features can be very time consuming. Generative AI has gotten surprisingly good at producing this kind of content today. Instead of writing each description from scratch, a seller can provide an AI with a few key details, say a list of feature, some keywords. Or even an image and the AI will draft a complete engaging product listing for them. It's like having a junior copywriter working 24 by seven on your catalog. The efficiency gains here are huge. One small business reported that a cut their product listing creation time from 60 minutes per item to just 15 minutes. Imagine going from an hour of human work to a quarter of an hour with the AI assistant. That's a 75% time saving, multiplied across hundreds of thousands of product. This is a MA massive boost in productivity, and if it is not just about speed, the AI content often produce an Siva boost as well. The AI naturally weaves in relevant keyword to product copy, which helps those listing rank higher in search results. In one case, after switching to age and listing, a retailer saw a 20% increase in our kind of tracker to organic traffic and about 15% higher sales for those products. So the AI was effectively doing some EO optimization on top of saving time. AI can also enhance the shopping experience in another base. One example is summarizing customer reviews. Popular products might have hundreds or thousands of reviews, far too many for any shopper to read through. A can help by reading all those reviews and generating a concise summary of common points. Essentially, I produce a high highlight reel of pros, cons, and recurring themes that customers mention. Another frontier of e-commerce AI is conversational commerce. Using chatbots and wise assistance to engage customer. This is all about making the online shopping experience more interactive and human-like. Human-like through conversation for a chat bot or a wise assistant To be truly effective, it needs two things well grafted prompts, driving its dialogue and access to the right data in real time. In other words, behind every helpful chat bot response, there is a carefully designed prompt or script and a connection to UpToDate information like product details, order statuses, or customer profiles to ground that response. When done right, the result is a bot that responds in a very natural. Helpful manner and can retrieve the information it needs on the fly. Retailers today are using chatbots for more than basic FAQs. These bots are starting to handle fairly sophisticated tasks. For example, a customer might tell a chatbot, I am looking for a gift for my 10-year-old niece. She loves science kits. The chat bot can ask a few smarter follow up questions and then recommend a suitable product. It is not just about answering questions, it's guiding product discovery. These chat bots can also handle upselling and cross-selling. For instance, out helping you to find a laptop for a graphic design, the bot might suggest a compatible monitor. Or a productive case, much like an in-store sales assistant might do. Analysts predict that by 2027, a significant chunk of customer service interaction will be handled by AI chat bots. As companies embrace their capabilities, we are also seeing a shift from a chat bot being used only for support. Into being used as sales assistance. Modern a h Bots powered by advanced language models can maintain context over multiple back and forth interactions. They remember what the customer said earlier in the conversation, which shows the interaction to flow naturally just like a human conversation. These bots are getting quite good at understanding. Nuanced inquiries and providing personalized advice. It is not hard to imagine that soon when you are on an e-commerce site. The chat bot might feel as capable as a well-trained human salesperson who knows all the products. In fact, some online retailers have already moved beyond basic customer service bots to deeply sophisticated a agent. That can handle anything from product recommendation to closing a sale. The common thread with both chat bots and voice assistant is that their effectiveness comes down to the prompt and their design of their conversational flows the better we design this interaction, giving the AI clear instruction plenty of context and access to the data. The more helpful and natural the experience become for the customer. Now, to make these conversational agent really effective, we have to be smart about how we set them up. In other words, how we engineer the prompts and incorporate customer data, one key strategy is to inject. Relevant data into the prompt. We also make use of system roles and personas when configuring these assistance. This means we explicitly tell AI what its role is on how to behave. Another important topic is guiding the a's action through the conversation. We don't just let the bot. Wing it entirely. We provide a sort of conversational blueprint, and we definitely want the AI to maintain context and reasoning throughout the conversation. In practical terms, this means as conversation goes on, we keep giving the AI the relevant history. When companies implement these sophisticated AI assistance, they are seeing measurable improvement in customer related metrics. For instance, a large set of customer inquiries can now be handled automatically by chat bots, especially the routine repetitive questions. On average, about 70% of. Routine customer inquiries can be handled or solved by AI without needing a human agent. Customer can get instant answer at any time, and your support team is freed up. Toggle the more complex issues. There is also evidence that having immediate AI assistance during the shopping experience. Process or reduces court amendment. Some retailers have been roughly, yeah, 12% reduction in court amendment rates. After introducing AA system and more importantly, customer satisfaction, CSAT tends to go up. Softwares appreciate getting quick, helpful services. In fact, companies have reported that. Their CSAT scores improving significantly with a assisted support, in some cases raging around 90 per 95% satisfaction. Let's step back and talk big picture for a moment. Specifically, competitive advantage. Adopting AI and prompt engineering early can give your company a mood. That late adapters will struggle to cross being a first mover in developing a strong AI and prompting strategies yield compounding benefits. You get to lean early IT rate and refine what works over time. You build up a library of effective prompts and process that are tailored to your business. If a competitor waits a few years to start, they will not only be playing catch up on technology, but they will miss all those hard earned lessons. You have a accumulated. In other words, the gap wides and early adopters can pull further and further ahead. This dynamic is why we see so much urgency among execution about ai. In fact, roughly 80% of executives expect AI to sign significantly, transform their business in next few years, and there are investing heavily to make sure they are not left behind. Those who integrate AI deeply into their operations now will develop expertise and data advantage that become very hard to match. The competitive gap will become, will widen between companies building their AI and data playbooks today, and those sitting on the sites, the A model themselves, are available to everyone. But how you can use them can start you to can set you apart your proprietary customer data, plus your unique prompt engineering know how, essentially form a secret recipe that others can copy. So embarrassing prompt engineering early isn't just about short term wins. It's about securing your piece as a leader long term. With AI capabilities that become a true competitive mode. Now let's look at to the future. What might e-commerce a tools look like by 2030? A lot of experts predict that the way we interact with technology in retail will evolve dramatically, and a will be the center of it. One big change will be. The rise of natural language interfaces. We have touched on this idea. Instead of using complex dashboard or forms, we will interact with our business systems by simply talking or typing in plain language. We also expect digital domains of customers to become a key digital domain is like a virtual model of a customer built. From all their data by 2030, companies might maintain a digital twin for each shopper, an a representation that predicts their preference and behavior. This would enable hyper-personalization. Another concept is autonomous workflows. This means end-to-end process in your business that an AI handles automatically. We already see. Pieces of this yeah, auto recording stock. So when inventory is low, but in the future, entire multi-step workflows might run with minimum human oversight. We can't forget AI Coates for employees. Think of an AI coate as a smart agent working alongside your team or even. Taking a certain task for operation managers, omnichannel orchestration will likely be powered by ai. As the line blurs between online and offline shopping, AI will help ensure a seamless customer journey. And finally, we'll see AI providing perspective insights. Routinely today, a lot of analytics are descriptive. A also will help very good at prescriptive analytics telling us what we should do. It would say, you are sales are down 5% this week. It'll add and I recommend you to X to fix it. This turns AI into not just an analyst, but strategic advisor for business. To make this future a bit more concrete, let's zoom in on the idea of natural language interfaces replacing dashboard. Today, if a manager wants a specific report or insight, they type, they typically have to navigate a dashboard or a run inquiry. In some analytics tool, it's doable, but often only those with some training can get the data they need. In the future, the manager might ask an AA assistant in plain English or whatever their preferred language. And get answer with an explanation. Begin the screens. It might pull up last week sale data. Identify the top sell list, and then analyze various factors to answer why. Maybe it finds that one of the top products sold well, because there was a promotion. Another because of social media posts went viral and so on. All of that would come as a conversational answer in second with no one having to manually compile a report. This kind of interfaces could really democratize data access within the organization. So how do we get from today's capabilities to that 2030 vision it requires. Investing few key areas right now. First, build prompt engineering expertise on your team. Upskill your people so they know how to work with a tools craft effective prompts. This might involve training programs or work have focused on AI and prompt design. Second, strengthen your data foundation. A is only as good as the data its feed. Take a hard look at ev, your e-commerce data, product data, customer data, sales, analytics. All of it is it well organized? Is it accessible to the AI when needed? Are we capturing the right information? Investing in clean and structuring your data, perhaps integrating your data system across will pay. Huge dividend the third, adopt an AI first culture within your organization. Encourage your teams to ask how can we use AI to dackle this problem? Finally, start pilot project and build a playbook. By focusing on people skills, data culture, and pilot project, you will prepare your organization to fully leverage prompt. Prompt guided AI in coming years. To wrap up, I would like to leave you this thought By embracing prompt engineering and weaving AI into fabric of your operation, e-commerce business can unlock unprecedented gains in effective efficiency, personalization, and competitiveness. Thank you.