Are AI hallucinations undermining trust in machine learning, and can Retrieval-Augmented Generation (RAG) offer a solution? As we invite Rahul Pradhan, VP of Product and Strategy at Couchbase, to our podcast, we delve into the fascinating yet challenging issue of AI hallucinations—situations where AI systems generate plausible but factually incorrect content. This phenomenon poses risks to AI's reliability and threatens its adoption across critical sectors like healthcare and legal industries, where precision is paramount.
In this episode, Rahul will explain how these hallucinations occur in AI models that operate on probability, often simulating understanding without genuine comprehension. The consequences? A potential erosion of trust in automated systems is a barrier that is particularly significant in domains where the stakes are high, and errors can have profound implications. But fear not, there's a beacon of hope on the horizon—Retrieval-Augmented Generation (RAG).
Rahul will discuss how RAG integrates a retrieval component that pulls real-time, relevant data before generating responses, thereby grounding AI outputs in reality and significantly mitigating the risk of hallucinations. He will also show how Couchbase's innovative data management capabilities enable this technology by combining operational and training data to enhance accuracy and relevance.
Moreover, Rahul will explore RAG's broader implications. From enhancing personalization in content generation to facilitating sophisticated decision-making across various industries, RAG stands out as a pivotal innovation in promoting more transparent, accountable, and responsible AI applications.
Join us as we navigate the labyrinth of AI hallucinations and the transformative power of the Retrieval-Augmented Generation. How might this technology reshape the landscape of AI deployment across different sectors? After listening, we eagerly await your thoughts on whether RAG could be the key to building more trustworthy AI systems.
[00:00:00] Have you ever found yourself marveling at the sheer brilliance of AI or Gen AI?
[00:00:07] Only to be suddenly jolted by outputs that seem to be from a parallel universe.
[00:00:14] This juxtaposition raises the question, how can AI so capable of mimicking human intelligence
[00:00:21] sometimes get it so wrong?
[00:00:24] Well in today's episode I'm going to be joined by Rahul Pratam, VP of Product and Strategy
[00:00:31] at Couchbase and we're going to peel back the layers of an issue right at the heart
[00:00:36] of AI technology.
[00:00:37] Yep, you guessed it, hallucinations.
[00:00:41] These are not just the figments of imagination that you might think of, instead AI hallucinations
[00:00:47] are outputs not grounded in reality.
[00:00:50] They're posing significant risks, especially in critical fields that require the utmost
[00:00:56] accuracy.
[00:00:57] And to make this episode valuable to everybody, whether you're a techie or a business leader
[00:01:02] or have no prior knowledge to AI hallucinations, we'll describe exactly what they are, we'll
[00:01:08] delve into the challenges that they can pose and also a promising solution known as
[00:01:14] Retrieval Augmented Generation or RAG for short.
[00:01:20] Now Couchbase stands at the forefront of this innovation and they're offering a glimpse
[00:01:24] into how data management capabilities are key to enabling RAG.
[00:01:29] So let's unfold this narrative of AI hallucinations with Rahul and discover just how close we
[00:01:35] are to mitigating one of AI's most perplexing challenges.
[00:01:39] But before we get today's guest on, I need to pay the bills.
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[00:02:35] That's kiteworks.com to get you started today.
[00:02:38] So buckle up and hold on tight as I beam your ears all the way to San Francisco
[00:02:43] where Rahul is waiting to join us today.
[00:02:47] So a massive welcome to the show, can you tell everyone listening a little about who you are
[00:02:53] and what you do?
[00:02:55] Sure, thanks Niran, thanks for having me on.
[00:02:59] So my name is Rahul Radhan and the VP of Product and Strategy here at Cow Space.
[00:03:04] Being at Cow Space for more than six years now came here to build our Cow Space
[00:03:12] appellage is our database of service offering and it's been an incredibly
[00:03:17] fulfilling journey for me kind of starting with the first slide on what
[00:03:21] the product should look like to building a product in an engineering team
[00:03:26] and then kind of skating it to where it is.
[00:03:28] So obviously this is all backed by a phenomenal team here at Cow Space
[00:03:34] that have been working and eating.
[00:03:37] And I think I've kind of stepped back into more of a product role,
[00:03:42] really focused on our product roadmap, leading a product team and then our
[00:03:48] middle and long term strategy, especially around AI and then enabling
[00:03:53] our customers to unlock the power of AI in a much more safe, secure
[00:03:59] and reliable manner.
[00:04:02] Well, there's so much I'm looking forward to talking with you about today
[00:04:05] and every day on this podcast, I try and demystify a particular area
[00:04:09] in business or technology that people have been talking about a lot
[00:04:13] and try and put it in a language that everyone can understand.
[00:04:16] And I know that there's a huge topic out there.
[00:04:18] It has been for the last 18 months.
[00:04:20] So that is AI hallucinations.
[00:04:22] So I suppose just to set the scene for our conversation and make sure
[00:04:27] we keep everybody on board with us today.
[00:04:29] Can you just explain exactly what AI hallucinations are
[00:04:33] and why they pose a significant challenge for the development
[00:04:37] and deployment of large language models in so many different industries right now?
[00:04:41] I think hallucinations, and I'll talk more about how they happen
[00:04:46] and how we can reduce them or mitigate some of these.
[00:04:51] But all AI models, including language models and image
[00:04:56] generation models, can produce outputs that are not
[00:05:00] grounded in reality or the context that is provided to them.
[00:05:05] A phenomenon that's known as hallucination.
[00:05:08] So the output of these models often what happens is it can appear
[00:05:12] semantically or syntactically plausible, but it's actually just incorrect.
[00:05:17] Right. And this can range from and we've all had some examples
[00:05:21] in the past where it ranging from fabrication
[00:05:24] detail about fabrication, fabricating details about people
[00:05:29] creating non-existing peoples and research reports that do not exist.
[00:05:36] All of those can have a significant impact on the
[00:05:40] perception of the model itself on the user as people then
[00:05:45] start start doubting the integrity and the trust in the model.
[00:05:51] So this behavior, if you think about what reverse this behavior,
[00:05:55] it's really a consequence of several factors that are intrinsic
[00:06:01] to the design training and the operation of the LLN itself.
[00:06:05] And they arise from the fact that these models are fundamentally
[00:06:09] probabilistic in nature.
[00:06:11] So if you think of it like despite what seems like
[00:06:15] that the model is able to generate human-like text or
[00:06:20] can chat with you and seems to possess
[00:06:25] understanding and consciousness or access to information
[00:06:29] that is going to help you make decisions or generate content.
[00:06:35] There are many sub-relations of understanding based on the patterns
[00:06:39] and the information that is quoted in their data.
[00:06:43] So as I said before, I think this
[00:06:47] that the whole concept of hallucination basically
[00:06:52] can essentially possess, opposes a significant challenge
[00:06:56] for the deployment of these models across various industries.
[00:07:01] In misinformation, erosion of trust in the generated content
[00:07:06] and that risk is particularly acute in fields that require higher
[00:07:11] levels of accuracy, think of like health care, finance law
[00:07:16] where incorrect information can have potentially serious consequences.
[00:07:22] And I can't thank you enough for setting the scene perfectly there.
[00:07:25] And the reason I wanted to make sure everyone was on the same page
[00:07:29] is the concept of retrieval augmented generation or RAG
[00:07:33] is pivotal in your strategy to combat these AI hallucinations
[00:07:38] that you just talked about.
[00:07:39] So can you tell me a bit more about how this works
[00:07:42] and some of its advantages over traditional
[00:07:44] generative AI models that people are familiar with working?
[00:07:48] Yeah, absolutely.
[00:07:49] So the retrieval of augmented generation is probably one of the most
[00:07:53] promising techniques that have been demonstrated to significantly
[00:07:58] reduce AI hallucinations.
[00:08:00] So typically, all AI models are trained on a set
[00:08:07] corpus of data.
[00:08:09] That is that that is that is a point in time of what the
[00:08:14] of the of point in time.
[00:08:17] In terms of where the data was updated, as a result of it,
[00:08:24] what they don't have is they don't have the they don't have
[00:08:28] the data that has or information that has happened since
[00:08:33] the action that the model was actually trained.
[00:08:37] So as a result of that, they end up making up the
[00:08:43] the information which needs to hallucination.
[00:08:47] So what RAG does is is actively retrieves and uses real time
[00:08:52] data or preexisting knowledge based during the generation
[00:08:56] process.
[00:08:57] And this approach enables AI to produce more relevant and
[00:09:02] contextually appropriate responses.
[00:09:06] So if you really get into like the technical details of what
[00:09:09] how RAG versus what it does is it introduces an information
[00:09:15] retrieval component that utilizes the user input to first
[00:09:19] pull information from a new data source and then augmenting
[00:09:23] the user query and the relevant information before it is sent
[00:09:29] to the to the AI model.
[00:09:33] The AI model can now then use this additional knowledge
[00:09:36] and context in order to create better responses.
[00:09:40] So think of it like you're you're you're trying to you put a
[00:09:43] worry in a in a chat board.
[00:09:47] And if the chat board is only trained on your data, then it
[00:09:52] is not not trained on your data rather than it is not
[00:09:56] going to give you the most relevant or up to date
[00:09:59] information that you need and can potentially hallucinate.
[00:10:03] This is where informing the model with that extra set
[00:10:06] of information enables the model to give you or generate
[00:10:12] content that's going to be much more contextual and relevant
[00:10:17] and potentially mitigate hallucinations as well.
[00:10:22] So at couch base, is that if you can share around how
[00:10:25] you're incorporating this with real time contextual data
[00:10:29] to maybe enhance that accuracy and reliability of AI generated
[00:10:33] content and also if you are able to share what kind of
[00:10:37] impact is this having on the user experience?
[00:10:40] I'm sure you've had a few stories because it isn't a big
[00:10:43] talking point right now, isn't it?
[00:10:45] So we at couch base are really focused on making AI safe,
[00:10:50] secure and reliable for our customers.
[00:10:53] And then what we we strongly believe that enabling our
[00:10:58] customers to have a data platform that can make them make
[00:11:05] the AI safe and secure and leverage them in the ways
[00:11:08] that they wanted is going to be extremely beneficial to them.
[00:11:12] So we recently raised key capabilities to support
[00:11:15] rank to enable our customers to ground these models with
[00:11:20] real time contextual data, which has the accuracy
[00:11:24] and reliability of the generated content.
[00:11:27] We launched a vector store and search capability
[00:11:31] on the product just this month, where we ensured that
[00:11:36] the generated content is not only contextually relevant,
[00:11:40] but it also based on the latest available information.
[00:11:44] And this integration improves the user experience
[00:11:48] significantly by delivering more accurate,
[00:11:52] trustworthy and relevant content, which addresses
[00:11:56] one of the critical challenges posed by AI hallucinations.
[00:12:01] And since our large effect several customers who are also
[00:12:05] part of our internal beta for the for the product
[00:12:10] was the ability for them to now leverage AI in a secure
[00:12:15] manner, knowing that they have they have their data
[00:12:20] that that is confined within their security and parameter
[00:12:26] and can be used to inform these wanted to eliminate hallucinations
[00:12:31] is a is a big is a big game for them to to develop
[00:12:36] AI powered applications.
[00:12:39] Absolutely love it. I love what you're doing here in in
[00:12:43] practical terms, are there any other examples of how AI
[00:12:46] hallucinations or especially in the conversation you're
[00:12:50] having with your customers, how they've affected businesses
[00:12:53] or decision making processes and how it's been able to
[00:12:57] mitigate some of these issues? Because I think there's a lot
[00:13:00] of people that a lot of businesses that have gone head
[00:13:02] first into this, and only just starting to realize the
[00:13:05] scale of the hallucinations. I mean, it'd be great to
[00:13:08] bring to life what we're talking about here with some
[00:13:10] real world issues.
[00:13:12] Yeah, no, I mean, there have been lots of practical
[00:13:15] examples, and it's in all of the hallucinations, I mean,
[00:13:19] affecting inaccuracies in in like customer service,
[00:13:23] car responses, erroneous financial advice is given out
[00:13:29] by robot advisors or, or even in the legal briefings
[00:13:34] and stuff. I'll kind of pick on some of the most famous
[00:13:39] examples that I think I'm sure most of your listeners
[00:13:44] have heard about, including an incident when Google's AI
[00:13:48] chatbot, BARD was launched, which incorrectly claimed that
[00:13:54] James Webb Space Telescope took the first image of an
[00:13:57] exoplanet. Whereas the accurate answer in that case was
[00:14:01] was the European observatories were in a telescope
[00:14:04] would happen way back in 2004. Or like the Bing AI
[00:14:09] in early on, providing an incorrect summary of an
[00:14:13] learning statement from from about a map of gap.
[00:14:17] And these are the these are the examples where I think
[00:14:22] when they happen, people people laugh at that, but
[00:14:25] when you start making business decisions based on those
[00:14:29] testing your AI model, they can have a significant
[00:14:33] impact there. More recently, there have been
[00:14:36] a couple of cases where an attorney used a
[00:14:40] JMAI chatbot for legal research. The chatbot fabricated
[00:14:45] non existing legal precedents, which they're turning
[00:14:49] put put it in their in their breeze, leading to
[00:14:52] inaccuracies and impacting basically the
[00:14:55] integrity of the TX itself. Another example that
[00:14:59] happened with with was with an airline, a chatbot
[00:15:04] that they had online that hallucinated and then
[00:15:07] informed the password of a policy that was
[00:15:09] actually contradicted by the airlines documentation
[00:15:13] and their website. And the the customer
[00:15:18] ended up suing the airline for that. So these
[00:15:21] are just kind of some examples where the
[00:15:25] generated model output, though hallucinated, but
[00:15:30] had a significant business or or or a
[00:15:33] customary impact. And that's what rapidly
[00:15:36] comes in. Like I said, one of the things we
[00:15:39] are doing it nearly is enabling our customers
[00:15:42] to combine their operational real time data
[00:15:47] with the with the data that the AI model is
[00:15:50] trained on. And that's what RAG enables them
[00:15:53] to do it. And it can be used to mitigate
[00:15:55] these issues by ensuring that the AI system
[00:15:59] accesses and incorporates the most current
[00:16:01] and relevant data before generating their responses.
[00:16:05] I just love this. And I think the integration
[00:16:08] of retrieval augmented generation into AI
[00:16:11] systems, he seems like a crucial step forward
[00:16:14] for businesses and many business leaders
[00:16:16] avoid a gen AI because of company data
[00:16:19] training large language models. Of course,
[00:16:21] we've since gotten over that by being able
[00:16:23] to isolate that data with APIs, etc. But
[00:16:26] when it comes to RAG, what are the
[00:16:29] technical challenges involved in implementing
[00:16:31] AI and how are you helping at CouchBase
[00:16:34] address some of these challenges?
[00:16:36] Yeah. And so as great as RAG is from
[00:16:41] from a technology and a framework
[00:16:43] with respect to implementing that does have
[00:16:45] several technical challenges, including
[00:16:48] the need for really highly performed
[00:16:51] data platform that's capable of
[00:16:55] efficiently searching and retrieving
[00:16:58] data that can actually span multiple
[00:17:01] modalities, right? So you think of text,
[00:17:03] images, video, and you need
[00:17:07] something to be able to do that at a really
[00:17:10] high speed in real time. And the true
[00:17:13] value of RAG really combines with
[00:17:17] really comes with combining that real
[00:17:19] time and fresh data in the context
[00:17:23] to open the prompt to the model.
[00:17:26] And this strongly
[00:17:29] feels we're in a really unique
[00:17:31] position to help our customers with.
[00:17:34] So if I look at our customers today,
[00:17:37] our customer largely use CouchBase as
[00:17:39] their operational and transactional
[00:17:41] data store, which is basically where
[00:17:43] the fractious data reside. This is
[00:17:46] data with things like profile
[00:17:50] stores and product catalogs that
[00:17:53] our customers run that on, which is
[00:17:55] the one that can be used to
[00:17:57] inform an AI model. And then
[00:18:01] what we have brought to the market now
[00:18:03] with some of the key innovations
[00:18:06] with vector search is enabling
[00:18:09] them to store data in the same format
[00:18:12] in what these AI models
[00:18:15] process them, which are basically
[00:18:17] mathematical vectors or
[00:18:19] the mathematical data or vectors.
[00:18:22] And we provide a robust platform for
[00:18:25] storing and managing data
[00:18:26] efficiently, enabling
[00:18:30] the overall grad end to end
[00:18:33] rack system to access and retrieve
[00:18:35] information really fast.
[00:18:38] The other unique capability we bring
[00:18:40] is we have a memory first
[00:18:43] architecture. So we enable
[00:18:45] customers to improve the performance
[00:18:47] of not just storing the data
[00:18:50] in so that they can search
[00:18:52] through and query quickly, but
[00:18:54] also storing kind of conversation
[00:18:56] histories for a particular
[00:18:59] session leading to faster
[00:19:01] execution time and more
[00:19:03] enhanced user experience.
[00:19:06] In addition, we have
[00:19:09] you talked about APIs. We have
[00:19:11] we have ecosystem integration
[00:19:14] across the data end to end
[00:19:16] rack ecosystem. So we have
[00:19:18] integrations with frameworks
[00:19:20] like land chain and Lama index,
[00:19:22] as well as the
[00:19:24] mortar platforms. If you think about
[00:19:26] when more of the mortars reside,
[00:19:30] whether it's EWS,
[00:19:32] Bedrock and SageMaker
[00:19:34] or Google's word XAI,
[00:19:36] we have integrations there to
[00:19:39] enable our customers to build
[00:19:42] an end to end rack solution.
[00:19:45] And if we were to look beyond
[00:19:47] reducing AI hallucinations,
[00:19:49] are there any other broader
[00:19:51] implications of RAG for the
[00:19:52] future of AI in areas such as
[00:19:55] data management, personalization,
[00:19:57] predictive analytics, etc?
[00:20:00] Yeah. I mean, once you once
[00:20:02] you start leveraging this
[00:20:03] technology, I think the
[00:20:06] the opportunities and the
[00:20:09] that you can you can start
[00:20:10] using this are are pretty
[00:20:13] significant.
[00:20:15] So if what RAG
[00:20:18] enables you to do is it enables
[00:20:20] you to inform your AI models
[00:20:23] with with essentially real time
[00:20:25] contextual data and that
[00:20:27] can first and retake more dynamic
[00:20:29] and personalized user experiences
[00:20:32] and enable more sophisticated
[00:20:34] decision making across industry.
[00:20:36] Right. And we hear that a lot
[00:20:38] from our customers as well
[00:20:40] as a potential for the AI
[00:20:42] and what they can do
[00:20:43] and where they can leverage
[00:20:45] that to solve some critical
[00:20:47] business challenges or even saw
[00:20:49] some challenges that they didn't
[00:20:50] even think would be solved.
[00:20:53] So beyond reducing the destinations,
[00:20:56] RAG models enable the generation
[00:20:59] of personalized content
[00:21:01] on the fly that is tailored
[00:21:04] to to the users who suggest
[00:21:06] we tailor news articles,
[00:21:08] customers learning materials
[00:21:10] or personalized stories and give
[00:21:12] narratives.
[00:21:13] And this sort of enhances
[00:21:15] the user engagement
[00:21:16] but but also it taps to
[00:21:19] individual preferences and behaviors
[00:21:21] over time.
[00:21:23] So by providing responses
[00:21:25] that are both contextually
[00:21:26] relevant and highly personalized
[00:21:28] personalized,
[00:21:30] RAG models can prove user
[00:21:32] interaction in applications like
[00:21:34] chatbot, virtual assistants
[00:21:36] and recommendation systems.
[00:21:39] And that leads to a much more
[00:21:40] as you can imagine, engaging in
[00:21:42] and a satisfying user experience.
[00:21:46] Other than that, I mean, there
[00:21:47] are broader implications of
[00:21:49] using the RAC to
[00:21:50] make sure that
[00:21:53] the AI models are generating
[00:21:55] content that is ethical,
[00:21:58] non-biased and responsible
[00:22:02] by ensuring the
[00:22:04] transparency, fairness
[00:22:07] of the model themselves.
[00:22:10] And as VP of product and
[00:22:12] strategy at Couchbase, you're
[00:22:13] at the helm of steering the
[00:22:14] company's technological
[00:22:16] advancement. So I've got to
[00:22:17] ask, how do you envision the
[00:22:19] evolution of AI and
[00:22:20] database technology?
[00:22:22] So how do you see them
[00:22:23] converging in the next few years
[00:22:24] because it seems like we're heading
[00:22:26] into an exciting new
[00:22:28] territory and uncharted
[00:22:29] digital waters almost.
[00:22:31] Oh, absolutely.
[00:22:32] And then it's it's an
[00:22:34] it's an exciting diamond
[00:22:36] to be to be in this space.
[00:22:39] Janice, so if you look into
[00:22:41] the future,
[00:22:43] we envision a future
[00:22:45] applications with used
[00:22:47] live-double models.
[00:22:48] And it's not going to be a
[00:22:49] single Uber model
[00:22:52] that that everybody would be
[00:22:53] using.
[00:22:55] And there's a there's a
[00:22:55] significant amount
[00:22:57] of research that has gone
[00:22:59] through in across various
[00:23:01] industries, although
[00:23:02] industries and universities all
[00:23:04] over the world, which
[00:23:06] point to the efficacy of
[00:23:08] having composite
[00:23:10] or compound AI system
[00:23:12] that integrates multiple AI
[00:23:15] models to accomplish complex
[00:23:17] tasks or solve multifaceted
[00:23:19] problems.
[00:23:20] So unlike simple AI
[00:23:22] simple or a single
[00:23:24] AI system that
[00:23:25] may rely on one type of
[00:23:27] algorithm or approach,
[00:23:29] compound AI systems leverage
[00:23:31] the strength of different
[00:23:32] AI components
[00:23:34] to achieve greater functionality,
[00:23:36] efficiency and accuracy.
[00:23:38] What you can imagine this
[00:23:40] as combining various
[00:23:42] different form of AI,
[00:23:44] you know, your traditional
[00:23:45] machine learning models
[00:23:47] that national language
[00:23:50] processing models,
[00:23:51] Gem AI models as well as
[00:23:54] models for
[00:23:56] that that can that are tailored
[00:23:58] to a domain like
[00:23:59] like the health care
[00:24:01] computer vision models
[00:24:03] to get to a better
[00:24:05] decision making outcome
[00:24:08] from the AI itself.
[00:24:10] They have applications that
[00:24:12] that can leverage both
[00:24:14] both of these are more
[00:24:16] than one of these models
[00:24:19] to gather insights from,
[00:24:21] for example, from the user's
[00:24:22] profile and to give them
[00:24:25] contextual and real time
[00:24:27] recommendations
[00:24:29] and a generative model
[00:24:30] that generates and matches
[00:24:32] data in real time.
[00:24:34] So let me let me make that
[00:24:37] a little bit real in terms of
[00:24:39] an example. So imagine
[00:24:41] you're trying to book
[00:24:43] travel for vacation
[00:24:45] and you go to your
[00:24:47] to your favorite travel site
[00:24:50] and you have a chatbot there.
[00:24:52] The chatbot is very aware of
[00:24:54] your preferences, where you
[00:24:56] like flying the kind of
[00:24:58] the kind of weather
[00:25:00] you like, the kind of food you
[00:25:02] like and how many,
[00:25:04] how many people fly with you
[00:25:05] or who usually flies with you
[00:25:07] based on the profile that
[00:25:08] that you have with that
[00:25:09] website to be able
[00:25:11] to give you an itinerary
[00:25:14] or give you options for an itinerary
[00:25:17] that it can go that it can
[00:25:20] it can generate based
[00:25:22] on your preferences
[00:25:23] and then letting us select it
[00:25:26] is a huge
[00:25:28] customer experience improvement
[00:25:31] as well as it enables
[00:25:34] you as the end user
[00:25:36] and the customer to save
[00:25:39] a boatload of time
[00:25:40] in terms of researching
[00:25:41] and figuring that out
[00:25:44] on your own through multiple searches.
[00:25:47] So those are the kind of
[00:25:49] use cases that we see
[00:25:51] coming up quite frequently
[00:25:54] as now as customers
[00:25:56] start thinking about crossing over
[00:25:59] with the
[00:26:02] the hallucination
[00:26:04] armed with the
[00:26:05] with the mitigation with the rat
[00:26:08] is what they are excited about
[00:26:09] and that's the
[00:26:11] that's the
[00:26:12] the future that I think we are headed towards
[00:26:14] is leveraging this composer
[00:26:17] to be our models in order to
[00:26:19] enable applications to be much more insightful
[00:26:22] to do for them to be much more
[00:26:26] hyper personalized and contextual.
[00:26:31] And I think 18 months ago,
[00:26:33] many, many business leaders
[00:26:34] were afraid of Gen A.I.
[00:26:36] some completely banned it
[00:26:37] in the organization straight away.
[00:26:39] But here in 2024
[00:26:41] we now accept it's here to stay
[00:26:43] and we can keep company data safe
[00:26:46] and isolated.
[00:26:47] We can stop A.I. hallucination
[00:26:49] so that any organization
[00:26:51] or business leader
[00:26:52] that could be listening to this podcast today
[00:26:54] and they're now looking to
[00:26:55] dip their toes in the water, leverage A.I.
[00:26:57] more effectively, more safely.
[00:27:00] What advice would you give to them
[00:27:01] regarding the adoption of technologies
[00:27:03] like rag and of course
[00:27:04] the management of all those A.I. related risks?
[00:27:08] Yeah, so for organizations
[00:27:11] looking to leverage A.I. more effectively,
[00:27:14] safely and safely
[00:27:16] adopting technologies like rag
[00:27:18] absolutely is credible
[00:27:20] and it involves
[00:27:21] but it involves a comprehensive approach
[00:27:24] like I would say
[00:27:26] that includes understanding the specific needs
[00:27:29] and challenges of their domain
[00:27:32] investing in a robust data management infrastructure
[00:27:35] because at the end of the day
[00:27:37] your A.I. models are only going to be
[00:27:40] as good as the data that they are informed with
[00:27:43] so integrating real time data
[00:27:46] to for more accurate
[00:27:49] output is extremely critical.
[00:27:51] Additionally, it's crucial to maintain
[00:27:54] an ongoing evaluation of A.I. generated content
[00:27:57] for accuracy
[00:27:59] implementing feedback loops
[00:28:01] to continuously improve the A.I. models
[00:28:04] to ensure transparency
[00:28:06] and accountability
[00:28:07] in these A.I. deployments
[00:28:10] can help them really manage the A.I. generated risk
[00:28:15] extremely effectively.
[00:28:18] Well, I love chatting with you today
[00:28:20] we've covered so much in a short amount of time
[00:28:22] and I can't thank you enough
[00:28:24] for just sitting down
[00:28:25] taking the time to share your insights
[00:28:27] it'd be so helpful for business leaders
[00:28:29] listening all around the world
[00:28:30] but before I let you go
[00:28:32] I want to ask you to leave one final gift to my listeners
[00:28:35] and that is a book
[00:28:36] that means something to you
[00:28:37] that we can add to our virtual bookshelf
[00:28:39] Amazon wishlist
[00:28:40] that people listening can check out
[00:28:42] but what would that book be and why?
[00:28:45] Gosh, being one is difficult
[00:28:48] I'm an average leader
[00:28:50] but so there are quite a few
[00:28:53] that sum to my mind
[00:28:55] it's a had to pick one
[00:28:57] probably a book that's really close to my heart
[00:29:00] is one
[00:29:02] one of my favorite professors
[00:29:04] the leader of the Christiansen
[00:29:07] he in the business world
[00:29:09] I think we all know today as an expert
[00:29:11] but a strategic business thinker
[00:29:14] but one of his books
[00:29:16] that has probably inspired me the most
[00:29:20] actually not a business or a strategy book
[00:29:23] but it's a book called
[00:29:25] How will you measure your life?
[00:29:27] and the book connects to a period
[00:29:30] of introspection of transition
[00:29:32] in my own life
[00:29:33] with the arrival of my first born
[00:29:36] and at that time
[00:29:38] I was achieving great career success
[00:29:41] in the conventional sense
[00:29:45] however as it often happens
[00:29:47] there's always a persistent feeling of
[00:29:50] fulfillment and adding question of
[00:29:53] where I'm really spending time
[00:29:55] on my energy
[00:29:56] and what mattered most to me
[00:29:58] and which is my family
[00:29:59] and my growing family
[00:30:01] so what struck me the most about the book
[00:30:05] was kind of the application of the business principle
[00:30:09] which I was very well versed with
[00:30:12] to making those personal life decisions
[00:30:15] the idea of strategically allocating resources
[00:30:19] with its time, effort and passion
[00:30:21] toward what truly fills us
[00:30:25] resonated deeply with me
[00:30:28] and it just encouraged me to reflect on
[00:30:30] my destination of success
[00:30:32] and then basically charting my own path
[00:30:35] so if I look back now
[00:30:38] it was
[00:30:39] it kind of prompted
[00:30:42] back then and shifted in how I approached my career
[00:30:45] and personal life
[00:30:46] and then I think it has steered me towards
[00:30:49] decisions that probably led to
[00:30:52] a lot more personal growth
[00:30:54] I mean
[00:30:55] building enduring relationships
[00:30:57] with people that mattered most to me
[00:30:59] so yeah I would say
[00:31:00] I would have to think that
[00:31:02] it's not really businessy or technical book
[00:31:05] but
[00:31:06] it's a great read
[00:31:09] Fantastic
[00:31:09] well I'll get that
[00:31:11] on our Amazon wishlist
[00:31:12] so everybody listening couldn't check that out
[00:31:14] and
[00:31:15] but anybody listening that just wants to find out more
[00:31:17] information about couch base
[00:31:19] about rag that we've talked about today
[00:31:21] or connect with you or your team
[00:31:23] what's the best starting point for everything
[00:31:26] at gulfbis.com
[00:31:27] does it exist where you can find
[00:31:29] we have a lot of resources on our website
[00:31:32] talking about
[00:31:33] rag web discharge
[00:31:35] and how you can leverage
[00:31:38] couch base to support that
[00:31:41] and
[00:31:42] your listeners can always
[00:31:44] look me up on LinkedIn and Twitter
[00:31:46] and
[00:31:48] just drop me an email or message on this platforms
[00:31:53] Oh so I'll get that added to the show notes as well
[00:31:56] so people can find you
[00:31:57] and so much I love chatting with you today
[00:31:59] but especially I think
[00:32:01] tackling that issue of AI hallucinations
[00:32:04] but also
[00:32:05] demystifying it
[00:32:06] talking about the solution
[00:32:07] the retrieval augmented generation
[00:32:09] or rag and the role that that can play
[00:32:12] in preventing hallucinations
[00:32:13] and
[00:32:13] the great work you're doing at couch base as well
[00:32:16] in helping businesses get to grips with that
[00:32:18] so just a big thank you for your time today
[00:32:19] really appreciate it
[00:32:21] That thanks for having me on Neil
[00:32:23] it was great to be here
[00:32:24] I think it's clear that the path to reliable AI
[00:32:27] is both challenging
[00:32:29] as it is fascinating
[00:32:30] and the phenomenon of AI hallucinations
[00:32:33] I think just underscores the importance of grounding
[00:32:36] AI in reality
[00:32:38] it's the only way we're going to ensure that its outputs are accurate
[00:32:42] relevant and trustworthy
[00:32:44] and
[00:32:44] retrieval augmented generation
[00:32:47] or rag
[00:32:48] is emerging as somewhat of a beacon of hope
[00:32:51] with the potential of
[00:32:52] significantly reducing AI hallucinations
[00:32:55] by integrating real-time data for context
[00:32:58] during that content generation
[00:33:01] and I think his deep dive into how couch base is
[00:33:04] helping enable that capability
[00:33:06] is also shedding light on a future where
[00:33:08] AI can finally be deployed
[00:33:10] with confidence across a variety of fields
[00:33:13] from healthcare to law and beyond
[00:33:16] areas where accuracy
[00:33:18] is so important
[00:33:19] and the journey towards responsible AI
[00:33:21] yes it is long and winding
[00:33:23] but with innovations like Ragger
[00:33:25] it feels like we're getting one step closer
[00:33:28] to harnessing that true potential of AI
[00:33:30] responsibly
[00:33:32] and effectively
[00:33:34] but what are your thoughts on AI hallucinations
[00:33:37] and the solutions we discussed today
[00:33:40] have you encountered instances where AI's creativity
[00:33:43] crossed into the realm of fiction
[00:33:45] I know I have
[00:33:47] especially when I asked chat GPT to write a bio for me
[00:33:50] just from what information it found online
[00:33:52] there was a lot of great things in there
[00:33:53] but was it me?
[00:33:55] I'm not so sure
[00:33:56] so please share your thoughts with me
[00:33:58] let's continue this conversation beyond the podcast
[00:34:00] by emailing me techblongwriteroutlook.com
[00:34:03] Twitter, LinkedIn
[00:34:05] Instagram just at Neil C Hughes
[00:34:08] but that's it for today
[00:34:09] so thanks for listening as always
[00:34:10] and until next time
[00:34:13] don't be a stranger