Learn how your Text-to-SQL LLM App may be vulnerable to Prompt Injections, and mitigation measures you could adopt to protect your data
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Text-to-SQL LLM Applications: Prompt Injections
Go Here to Read this Fast! Text-to-SQL LLM Applications: Prompt Injections
For decades, product builders long accepted a seemingly unbreakable rule: as the capabilities of a product increase, so does its complexity. For users, this has often meant choosing between simplicity and power. Anyone who has grappled with advanced softwares knows the frustration of navigating through countless menus and options to find that one feature they need! It’s the classic trade-off that has, until now, dictated the user experience.
But the rise of generative AI promises to disrupt this trade-off.
Imagine the Adobe Photoshop of yesteryears: while the product boasted a staggering array of rich design features, the product became so complex that only experts could use it. Today, with generative AI, Photoshop can enable users to make requests in plain speech like “remove background” or “make this portrait pop with sharper contrast.” This is a glimpse into how AI is making powerful tools more accessible for everyone.
By interpreting natural language commands, advanced feature-rich products can now make their user experience more accessible and intuitive for users without sacrificing their sophisticated capabilities. Conversely, the existing feature-light products aimed at novice users, which typically favor simple user experience, can now offer a newfound depth of Generative AI capabilities without adding complicating the user interface.
This is the essence of the paradigm shift — where complexity is no longer the cost of capability.
As generative AI redefines the product design landscape, it’s clear that established companies with strong user bases and domain expertise have a head-start. However, success is far from guaranteed.
You can integrate a generative AI API today, but where is the moat?
I have talked with half a dozen product leaders builders just this month. Every believes that paradigm shift of Generative AI has kick started a race. In the end, there will be winners and losers. This article, brings out some of the key strategies that product leaders are leveraging to use Generative AI for delivering a differentiated offerings to their customers.
In Generative AI, “one size fits all” approach doesn’t make the cut for specialized use cases. Generic foundation models are trained on internet data, which lacks industry-specific nuanced knowledge.
Take large vision models (LVMs) as example. LVMs are typically trained on internet images — which include pictures of pets, people, landmarks and every day objects. However, many practical vision applications (manufacturing, aerial imagery, life sciences, etc.) use images that look nothing like most internet images.
Adapting foundation models with proprietary data can vastly improve performance.
“A large vision model trained with domain-specific data performed 36–52% better than generic models for industry specific use cases.” — Andrew NG, Founder DeepLearning.AI
The narrative is similar for text-based large language models (LLMs). For instance, Bloomberg trained an LLM with proprietary financial data to build BloombergGPT, which outperforms other generic models of similar size on most finance NLP tasks. By augmenting foundation models with proprietary, in-domain data, companies can develop tailored generative AI that understands the nuances of the industry and delivers differentiated experience to meet users’ specialized needs.
For all their impressive abilities, generative AI models are a far from being reliable enough for most real-world applications. This gap between “wow!” demos and dependable deployments is what technologists refer to as the “last mile” problem. Generative AI produces probabilistic output and have tendency to hallucinate. This is a cause of concern in many business, finance, medicine, and other high-stakes use cases. As generative models become more capable, implementing practices to ensure fairness, transparency, privacy, and security grows increasingly important.
The framework below lists various initiatives that leading companies are prioritizing to manage the “last mile” risks pertinent to their industry.
By solving difficult responsible AI challenges unique to their industry, companies can successfully integrate these powerful technologies into critical real-world applications. Leading in AI ethics will earn user trust and gain a competitive advantage.
The cornerstone of crafting an exceptional, human-centered user experience is to design products that don’t just respond to users but grows and adapts with them. Leading Generative AI products will implement tight feedback loops between users and AI to enable continuous learning and deliver personalized experience.
“To build an ever-improving AI service, begin with the end-user goals in mind. Build a data-flywheel that continuously captures actionable data points which helps you assess and improve AI to better meet those goals.” — AWS, Director
Consider Grammarly, a tool designed to refine and improve users’ writing. It has recently launched Generative AI features to provide users with personalized writing suggestions.
Here’s a conceptual breakdown of how Grammarly can implement feedback loop to enhance its product aligned with different user goals:
A successful implementation requires:
The result is an AI that becomes increasingly customized to individual needs — without compromising privacy.
Prioritizing these human-centered feedback loops creates living products that continuously improve through real user engagement. This cycle of learning will become a core competitive advantage.
Realizing the full potential of generative AI requires rethinking user experience from the ground up. Bolting an AI chatbot in the application as an afterthought is unlikely to provide a cohesive experience. This requires cross-functional collaboration and rethinking of interactions across the entire product stack right from the beginning.
“Think from first principles. If you had these generative AI capabilities from the start, how would you design the ideal experience?” — Ideo, Design Lead
Consider a bike repair app. A chatbot that allows users to upload pictures of their bike and receive text instructions can be a good MVP. But the ideal UX will likely be a visual interface where:
Delivering this experience requires collaboration across teams:
By bringing these perspectives together from the outset, products can deliver fluid, human-centered user experience. Companies leveraging “AI-first” design thinking and full stack product optimization will be best placed to provide differentiated value to their customers.
As generative AI becomes ubiquitous, product leaders have an enormous opportunity — and responsibility — to shape its impact. Companies who take a human-centered, ethical approach will earn users trust. The key is embracing AI not just for its functionality, but for its potential to augment human creativity and positively transform user experiences. With thoughtful implementation, generative AI can expand access to sophisticated tools, unlock new levels of personalization, and enable products to continuously learn from real-world use.
By keeping the human at the heart of generative product design, forward-thinking companies can form authentic connections with users and deliver truly differentiated value. This human-AI symbiosis is the hallmark of transformative product experiences yet to come.
Thanks for reading! If these insights resonate with you or spark new thoughts, let’s continue the conversation.
Share your perspectives in the comments below or connect with me on LinkedIn.
The Generative AI Advantage: Product Strategies to Differentiate was originally published in Towards Data Science on Medium, where people are continuing the conversation by highlighting and responding to this story.
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The Generative AI Advantage: Product Strategies to Differentiate
Go Here to Read this Fast! The Generative AI Advantage: Product Strategies to Differentiate
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Designing generative AI workloads for resilience
Go Here to Read this Fast! Designing generative AI workloads for resilience
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Go Here to Read this Fast! ChatGPT Enterprise vs. ChatGPT Team: Which is the best for your business?
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ChatGPT Enterprise vs. ChatGPT Team: Which is the best for your business?
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Go Here to Read this Fast! How these $400 XR glasses cured my Apple Vision Pro FOMO
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How these $400 XR glasses cured my Apple Vision Pro FOMO
The rumors were true: a remake of Until Dawn is coming to PlayStation 5 and PC later this year. As revealed during the latest State of Play showcase, the upgraded version of the interactive horror game is being built in Unreal Engine 5 and it will have several new bells and whistles.
You’ll be able to experience the action from a third-person perspective for the first time. Ballistic Moon, which is handling the remake in the stead of original developer Supermassive Games, is expanding the color palette to make it more cinematic and bringing in contextual character movement animations.
There’s also a new score from horror composer Mark Korven (The Witch), while the characters, visual effects and environments are all getting upgrades. “We’ve endeavored to keep the fantastic narrative integratory of the original, but we have seized the opportunity to expand upon unexplored emotional parts of the story,” Ballistic Moon creative director Neil McEwan wrote on the PlayStation Blog.
The remake should arrive before the movie adaptation of Until Dawn that was announced in January. Unless the cast members all reprise their roles, the new version of the game will likely have bragging rights in at least one respect: the presence of a bonafide Academy Award winner in the form of Rami Malek.
This article originally appeared on Engadget at https://www.engadget.com/an-until-dawn-remake-is-coming-to-ps5-and-pc-this-year-191524477.html?src=rss
Go Here to Read this Fast! An Until Dawn remake is coming to PS5 and PC this year
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An Until Dawn remake is coming to PS5 and PC this year
Comcast is discontinuing its its “Xfinity 10G Network” branding to describe its internet service after a National Advertising Review Board (NARB) panel found that the term could mislead consumers into thinking that Comcast’s cellular and broadband services would offer much faster speeds than current-generation networks. Comcast rivals T-Mobile and Verizon had challenged the branding with the National Advertising Division (NAD), an ad industry watchdog, which had recommended that Comcast get rid of it in October 2023. Comcast’s confusing branding is at the heart of this challenge: “5G” refers to mobile internet, while “10G” refers to 10-gigabit broadband speeds typically delivered to homes through physical infrastructure.
On Wednesday, the NARB said that it agreed with the NAD’s decision and recommended that Comcast “discontinue use of the term 10G in the product service name ‘Xfinity 10G Network’ and when 10G is used descriptively to describe the Xfinity network.” The NARB found that the branding could mislead consumers into thinking that “10G” offered significantly faster speeds than current-generation 5G networks
The NARB also decided that using “10G” to refer to home broadband, as Comcast did, was misleading because consumers would assume that they would get 10-gigabit internet speeds on every Xfinity connection. In reality, as Ars Technica pointed out, getting those speeds requires getting Xfinity’s fiber-to-the-home connection, which typically costs hundreds of dollars more in monthly fees, installation, and activation over Xifnity’s regular cable broadband plans.
In a statement that Comcast provided to the NARB, the company agreed to stop using the misleading branding in its marketing. “Although Comcast strongly disagrees with NARB’s analysis and approach, Comcast will discontinue use of the brand name ‘Xfinity 10G Network’ and will not use the term ’10G’ in a manner that misleadingly describes the Xfinity network itself,” Comcast said.
The company said, however, that it still “reserves the right” to use both “10G” and “Xifnity 10G” in ways that do “not misleadingly describe the Xfinity network itself”, so expect both terms to still show up in Xfinity marketing, just, hopefully, in less misleading ways.
This article originally appeared on Engadget at https://www.engadget.com/comcast-agrees-to-kill-10g-branding-after-advertising-watchdogs-said-it-was-misleading-185550194.html?src=rss
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Comcast agrees to kill 10G branding after advertising watchdogs said it was misleading