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Speaker 1: dot com. Hey, welcome back to dot net rocks. I'm
Speaker 1: Carl Franklin and I'materi Comp. You are here for your
Speaker 1: dot net keeeking out, listening, pleasure and all that stuff,
Speaker 1: all the things, all the things anything you want to
Speaker 1: announce today, buddy.
Speaker 2: No, Well let me I told you earlier. But yeah,
Speaker 2: I know we got a puppy. Okay, because you know,
Speaker 2: having a grandchild wasn't enough, we also need a puppy. Yeah,
Speaker 2: so I've managed to tie this while she's at yoga.
Speaker 2: Got to have the puppy asleep.
Speaker 1: So wait, Manute, I thought you were swearing off dogs
Speaker 1: after Zach.
Speaker 2: Listen, I already had the perfect dog was seventeen years.
Speaker 2: Don't need it down a dog. I didn't need that dog,
Speaker 2: to be clear. But sometimes sometimes you're out numbered one
Speaker 2: to one, you know, Oh that's great that it's really
Speaker 2: got to be her dog this time. So I'm doing
Speaker 2: the only logical thing I can do, which is good
Speaker 2: to Australia for three weeks. Okay, I'm leaving on Thursday,
Speaker 2: so I'm literally I'm gonna go away for three weeks
Speaker 2: and hopefully.
Speaker 1: What kind of puppy is it?
Speaker 2: It's a it's a mix of Ausi Shepherd and border
Speaker 2: collie and some poodle and some burnies, a little bit
Speaker 2: of everything.
Speaker 1: It's Oh my gosh, it's a cute little.
Speaker 2: Dog, no two ways about it.
Speaker 1: But you know, does it have an identity complex?
Speaker 2: It's got springs and its legs is when it's gone.
Speaker 1: Wow.
Speaker 2: Okay, man, we're in the early stage needle teeth and
Speaker 2: pee on the floor and bit by bit figuring it out.
Speaker 2: I already took a tick off for her because she
Speaker 2: was out in the lawn.
Speaker 1: What's her name, Jojo short for Josephine Lily. Oh she
Speaker 1: doesn't look like a deep fried potato wedge then.
Speaker 2: No, not like some dogs, you know. Oh, she's a
Speaker 2: cute little dog but cool. And she's got that French
Speaker 2: twying tour so that hits the crazy name, right, anyway
Speaker 2: I got, I got worse problems like, yeah, it makes her.
Speaker 2: If it makes she who must be obeyed happy, then
Speaker 2: I'm happy to do fantastic.
Speaker 1: All right, Well, let's jump into it with a better
Speaker 1: NO framework.
Speaker 2: All right, what do you got?
Speaker 1: All right? Well, in honor of our AI guest today, Vishwaesle,
Speaker 1: who's been on the show several times before, many times,
Speaker 1: I found a trending repo on GitHub. It's called krillin
Speaker 1: ai k r I l l I n AI or
Speaker 1: a one, depending on your point of reference. It's an
Speaker 1: AI audio and video translation and dubbing tool. So let
Speaker 1: me just read the paragraph here now, just does a caveat.
Speaker 1: I have not downloaded it. I don't know if it's
Speaker 1: any good, but it is trending, so that tells me
Speaker 1: that people kind of like it. Crillin ai is an
Speaker 1: all in one solution for effortless video localization and enhancement.
Speaker 1: This minimalist yet powerful tool handles everything from translation, dubbing
Speaker 1: to voice cloning. I see there's no the commas don't
Speaker 1: work here. Translation comma dubbing to voice cloning comma. I
Speaker 1: would say, everything from translation and dubbing to voice cloning.
Speaker 1: Formatting seamlessly converting videos between landscape in portrait modes for
Speaker 1: optimal display across all content platforms. YouTube TikTok, Billy Billy, Duian,
Speaker 1: we chat channel, Red Note Kawai show. With its end
Speaker 1: to end workflow, crillion Ai transforms raw footage into polished,
Speaker 1: platform ready content and just a few clicks. So that's interesting.
Speaker 1: You know, translation a localization anyway is one thing in
Speaker 1: the world of software and web apps that we know
Speaker 1: very well. But dubbing and you know, translating video that
Speaker 1: has audio in it.
Speaker 2: Well, the voice cloning one is the craziest one where
Speaker 2: I take the voice of the person speaking and then
Speaker 2: change their language, but it still.
Speaker 1: Sounds like them change the language.
Speaker 2: Yeah, that's nuts, but it's very jen Ai.
Speaker 1: Yeah, it's nuts, but you know that's the stuff that
Speaker 1: we get to see. This is the world we live
Speaker 1: in now, so world we live in. So that's what
Speaker 1: I got. Richard, who's talking to us.
Speaker 2: Today, grabbed a comment off of show nineteen forty four
Speaker 2: just a few weeks ago with our friend Jody Bershall,
Speaker 2: doctor Jody Bernshall. Yeah's got a few comments on the show,
Speaker 2: and this one's from Joshua hillar Up, one of our regulars.
Speaker 2: I'm pretty sure already has a copy of Means to
Speaker 2: go By, But you know, Joshua, thank you. And he said,
Speaker 2: I'm thinking about the non deterministing aspects of LMS, and
Speaker 2: what's frustrating is that there's been many decades of computer
Speaker 2: science research on software correctness. It just never seems to
Speaker 2: get used in the industry because it's too hard to learn.
Speaker 2: If you did that in air quotes, too hard to learn.
Speaker 2: LLLMS work really well at covering that hard work, and
Speaker 2: I don't think it's as hard as people claim, at
Speaker 2: least in the beginning. My hope is assuming enough money
Speaker 2: put into developing these languages and tools to make the
Speaker 2: use of that research. Now there's such an obvious use
Speaker 2: case for it. And we probably should have talked about
Speaker 2: this more with Jody too, Joshua, because she does come
Speaker 2: from that I hate to say old school, but because
Speaker 2: that would be like five years ago form of machine
Speaker 2: learning where quality and accuracy, you know, validation was a
Speaker 2: huge part of the job.
Speaker 1: That's right. I remember talking to Seth Warez and he said,
Speaker 1: ninety percent of my time is cleaning the data.
Speaker 2: Yeah, to get to a meaningful quality. So there is
Speaker 2: certainly some pressure on that, but we're we're bringing a
Speaker 2: lot of people in without a lot of experience the space,
Speaker 2: and they are not concerned about these things and they
Speaker 2: should be.
Speaker 3: M H.
Speaker 2: So as usual, Josha, thank you so much for your
Speaker 2: great comment, and a coffee of music. Cobi is on
Speaker 2: its way to you. And if you'd like a copy music,
Speaker 2: go bay rte a comment on the website at dot
Speaker 2: netrongs dot com or on the facebooks. We publish every
Speaker 2: show there and if you comment there and I've read
Speaker 2: on the show, we'll send you a copy music O
Speaker 2: bay music to.
Speaker 1: Code by dot net. That is how you can find it.
Speaker 1: It's still going strong. Track twenty two is available now
Speaker 1: and of course you get the whole collection MP three
Speaker 1: Flak or Wave. This is episode nineteen forty seven, and
Speaker 1: so I can just give a little brief summary here
Speaker 1: about what happened that year. It was a year of
Speaker 1: significant global transformations. Of course, India and Pakistan gained independence
Speaker 1: from British rule, and the Marshall Plan was initiated to
Speaker 1: help rebuild war torn Europe. In the United States, Jackie
Speaker 1: Robinson broke baseball's color barrier, and Chuck Yeger broke the
Speaker 1: sound barrier. Also, the Truman Doctrine, which pledged US support
Speaker 1: for nations threatened by communism, was established, which signaled the
Speaker 1: beginning of the Cold War. You got anything to add?
Speaker 2: Nineteen forty seven is the year the invention of the transistors.
Speaker 2: That's bartying Britain and Shockley with the first point content
Speaker 2: transi should have been talked about for years, but semiconductor
Speaker 2: making pure material semi conducts were hard. A lot of
Speaker 2: that came out of the war. And so what's funny
Speaker 2: is reading some of the documentation at the time and
Speaker 2: they weren't sure if it was actually going to be
Speaker 2: all that useful. Interesting, you know, because vacuum tubes did
Speaker 2: such a good job and this thing did not have
Speaker 2: the potential the vacuum two did because the materials weren't
Speaker 2: quite good enough. Yet they had no idea what was
Speaker 2: about to happen. Huh No, but they you know, Shockley
Speaker 2: would go on with Fairshire Semiconductor to help make the
Speaker 2: originalized sees with Robert Noise and all those like. All
Speaker 2: of that comes from there. But there's the beginning and
Speaker 2: one other fallout of the war raytheon's radar range, the
Speaker 2: first microwave oven.
Speaker 1: Wow, yeah, radar range, that's what they called it.
Speaker 2: They called it. They called it the radar range literally
Speaker 2: because some of the early earlier technicians working on radar
Speaker 2: noticed that the chocolate bars in their pockets would melt
Speaker 2: when they got too close to the magnetrons.
Speaker 1: Okay, so how safe was this thing? It doesn't sound
Speaker 1: like it was very safe.
Speaker 2: It's it can be is superheating the water, so it
Speaker 2: will you will get water burns if you get too
Speaker 2: close to it.
Speaker 1: Yeah, right, And but radiation poisoning. Were they worried about that?
Speaker 2: No? No, still, there's no I you know, ionizing radiation
Speaker 2: is more complicated than this. This is just microwaves, just
Speaker 2: like a bright light, and it can burn you because
Speaker 2: it vibrates the water molecules, and those water moleoscules, once
Speaker 2: they get to a certain temperature, are going to damage
Speaker 2: the tissues they're in.
Speaker 1: Okay, all right, let's bring Vishwaz back on dot net
Speaker 1: rocks for the empteenth time.
Speaker 2: It's at number seventeen, by the way I counted. Hey,
Speaker 2: well that's umpteen, isn't it. That's umpteenth. Yeah, we're ext
Speaker 2: the umpteeth category.
Speaker 1: Bishwaslele serves as co founder and CEO at p wind
Speaker 1: dot AI, A noted the industry speaker and author. Mister
Speaker 1: Lealey is the Microsoft Regional Director for Washington, DC. Welcome back, Vishwas.
Speaker 1: So it's new in the world of AI in vishwas Land.
Speaker 3: Well, i've been. We talked to you last year, very
Speaker 3: focused still on trying to grow this Jennai startup idea
Speaker 3: that we started working.
Speaker 2: Yeah, you left your longtime company to do this startup.
Speaker 3: Yes, I left. I was there for thirty years the
Speaker 3: previous company. How's it going last sixteen years? Is the CTO? Well,
Speaker 3: it's going. It's going well. I mean, as you can
Speaker 3: expect as a startup. World is all about good days
Speaker 3: and bad days. But it has been very exciting a
Speaker 3: lot of learning and we are growing.
Speaker 1: That's cool.
Speaker 2: Now. If I recall when we talked last time, you
Speaker 2: were focused on generating responses to requests for proposal for
Speaker 2: the RFPs. That's right, right, which in government land can
Speaker 2: be absolutely massive, like hundreds of pages.
Speaker 3: That's right, that's right, and that's what we have been
Speaker 3: focused on. We call ourselves a thoughtful copilot. And I'll
Speaker 3: explain the word thoughtful in a second. We call ourselves
Speaker 3: thoughtful copilot for proposal writers. So what that means is
Speaker 3: we will take your information. You're you know in the
Speaker 3: years past you have responded to RFP documents that you've done.
Speaker 3: You have competency documents, you have some fancy documents like
Speaker 3: spars and for those who don't know this already, se
Speaker 3: parts are documents that government agencies give you to validate
Speaker 3: your experience. So, needless to say, you have a collection
Speaker 3: of documents and now a new RFP comes along. And
Speaker 3: these RFP documents can be, as you said, Richard, can
Speaker 3: be really complex, can be fifteen hundred pages long with
Speaker 3: different sections in them. So for example, they might be
Speaker 3: a section which describes how should you respond to the RFP,
Speaker 3: maybe a section which talks about what would be the
Speaker 3: evaluation criteria for somebody looking at your RFB. And then
Speaker 3: there may be another section which talks about all the
Speaker 3: things that you must do.
Speaker 1: So that's all the boring stuff.
Speaker 3: Our job is other boring.
Speaker 1: You Usually the stuff that you want to read last,
Speaker 1: you have to read it first.
Speaker 3: Yes, yes, and all this is part of something called
Speaker 3: the Federal Acquisition regulation, which something came out many many
Speaker 3: years ago, has different sections related to different parts of solicitation.
Speaker 3: So what we specialize in is, now that we have
Speaker 3: your knowledge repository built out, we deeply understand the solicitations
Speaker 3: that comes in, and then we help you write a
Speaker 3: draft quickly solely based on your information, so that we
Speaker 3: can give you this first draft quickly and then you
Speaker 3: can build on it. And there's a very interesting saying
Speaker 3: in the proposal world that proposals are often one at
Speaker 3: the end Richard that you know, you leave yourself enough
Speaker 3: time to do some creative thinking, then you goos to
Speaker 3: your competition, you do some innovative thinking. But the sad
Speaker 3: reality of proposal writing is you never reach the end.
Speaker 3: You spend all your time in the middle, sure, just
Speaker 3: trying to get a good proposal out. So our value
Speaker 3: proposition is very simple. Can we accelerate the initial stages
Speaker 3: so that we give you more polishing and thinking time.
Speaker 3: And that's ultimately what improves your probability of winning And
Speaker 3: which is the name of per company, peven dot Ai.
Speaker 1: The probability of winning. Mm hmm, that's really good.
Speaker 2: Well, part of this again, we're calling the previous show
Speaker 2: was being able to respond to more RFP so you
Speaker 2: have more chances at work opportunities. The thing, of course,
Speaker 2: has scared me is you're committing to stuff inside this
Speaker 2: RFP yep. And so if the tool is already the
Speaker 2: text for it, and it creates a commitment for you, you
Speaker 2: can't deliver on like you could be in serious trouble.
Speaker 1: I think that's why you said creates a rough draft, right, Yes,
Speaker 1: you can't just like say, okay, here it is, send
Speaker 1: it off.
Speaker 3: See. Yeah, And that's that's the thing.
Speaker 1: Yeah, you certainly have to know what you're saying.
Speaker 3: Yeah, no, Carl, that's a very good, good catch there.
Speaker 3: That's why I said draft. We're trying to improve the
Speaker 3: quality of drafts so that you know, you can focus
Speaker 3: on other things. But at the end of the day,
Speaker 3: make no mistake, you're putting your name down on the proposal.
Speaker 2: Sure, so you should read it.
Speaker 3: You should read it.
Speaker 1: So it's kind of like having a you know, a
Speaker 1: manager under you or a secretary or somebody who knows
Speaker 1: your business say hey, you can just say look through
Speaker 1: this RFP and give me a rough draft and it
Speaker 1: might have all the accurate information from your side, but
Speaker 1: you know, you're the one that's got to commit to
Speaker 1: what you're agreeing to. And so I guess my question
Speaker 1: here is can you go back. I can have a
Speaker 1: conversation with this bot if you will, and say, you know,
Speaker 1: why did you why did you commit me to doing X?
Speaker 1: You know, and have it give you an answer from
Speaker 1: your data.
Speaker 3: That's a that's a good point. We have an intermediate stage,
Speaker 3: and this is the pattern patent should say that we filed.
Speaker 3: We have a provisional patent. Yeah, so what's It's not
Speaker 3: like the bot is just or the copilot in this
Speaker 3: case of our proposal, copilot is just going to look
Speaker 3: at your documents and just spit out one hundred page
Speaker 3: document that it thinks that you should be doing. That's
Speaker 3: a recipe for disaster because you go pick up things
Speaker 3: that you don't want it to do. And there's a
Speaker 3: very important threshold where you really have to save time
Speaker 3: to people. If people say, hey, I have to first
Speaker 3: read your silly forty documents that you generated and then
Speaker 3: I have to rewrite it. Thank you, I'm not going
Speaker 3: to use this tool. So there's a very important threshold
Speaker 3: that you have to cross. So in our model. The
Speaker 3: patent that we have filed is called object based writing.
Speaker 3: Oh wow, okay, and what that means is and what
Speaker 3: that means is that once let's say the r FP
Speaker 3: aries and we have gone in and tried to analyze it,
Speaker 3: and we create something called the flight plan. And why
Speaker 3: are we calling it flight plan because we are the
Speaker 3: copilot for proposal writers and in the aviation industry, obviously
Speaker 3: there's a very important concept called flight plan that is
Speaker 3: used for communication. So what we do is we try
Speaker 3: to deeply using AI models, try to deeply understand the RFP,
Speaker 3: understand the evaluation criteria, understand the instructions, and we manifest
Speaker 3: that into a UI pattern called the flight plan. So
Speaker 3: you go in and then you intera to the flight plan,
Speaker 3: and this is where you go and say that I
Speaker 3: want you to use this architecture. And let's just take
Speaker 3: a concrete example that our listeners can follow along. Let's
Speaker 3: say the RFP is about a records management solution, right,
Speaker 3: and you may have done ten different types of records
Speaker 3: management solution, maybe use SharePoint for that, maybe used some
Speaker 3: purpose built records management solution things for that.
Speaker 1: By the way, I'm sorry, yep, yep.
Speaker 3: So if you had to do that. So once the
Speaker 3: flight plan comes in, what we do is we say, hey,
Speaker 3: we think that you have used these are three or
Speaker 3: four architectural patterns to go after this kind of work.
Speaker 3: Here's a suggestion, but please you need to update that
Speaker 3: with maybe, like you said written Carl, SharePoint is not
Speaker 3: the right solution. You want to do something else, so
Speaker 3: replace that, And in that flight plan you tell us
Speaker 3: that you know, I'm wanting to undertake this task using
Speaker 3: this architectural pattern. And then you also in the flight
Speaker 3: plan you get to tell people do you really understand
Speaker 3: the pain points? Do you really understand the motivation of
Speaker 3: the buyer? Why did they issue the RFP? And do
Speaker 3: you why do you why do you think you're differentiated
Speaker 3: than other competitors? If all your differentiation is or we
Speaker 3: have all the certifications in place, well, every other competitor
Speaker 3: is going to say that. So we force you to
Speaker 3: think critically about the response and at a higher level.
Speaker 1: So is it kind of like an outline that you
Speaker 1: can interact with? It seems like it is. You you
Speaker 1: have a model for a conversation with the AI. Yes,
Speaker 1: so yes to this, no to this? What's about that?
Speaker 3: Yes? And you know, so you're setting think of this
Speaker 3: as that you're setting the vision for the response that
Speaker 3: you want to generate. Right, you're talking about your competitors,
Speaker 3: you're talking about your win themes, you're talking about your
Speaker 3: pain points, you're talking about your solutioning strategy, and on
Speaker 3: and and on. There are lots of data structures that
Speaker 3: we capture, and then once we have that information, then
Speaker 3: we try to in a thoughtful manner, try to infuse
Speaker 3: what you gave us as a direction. Right, if your
Speaker 3: capture team, if your salespeople have done a good job
Speaker 3: and they weren't their salary, they probably on the golf
Speaker 3: course realized that the key motivation for this RFP was
Speaker 3: the pain point this organization is going through is the
Speaker 3: last three upgrades were not there, and that key point
Speaker 3: AI does not know about or would never know about.
Speaker 3: And this will be your capture team. And then you
Speaker 3: take that information, you have all of the data structures,
Speaker 3: you have your knowledge repository, and I'll talk more about it.
Speaker 3: We do a lot of work, and I very much
Speaker 3: agree with what you were saying Card earlier about sets comment.
Speaker 3: We do a lot of collation, classification, tagging of content
Speaker 3: and cleaning of content. So now have you have your content,
Speaker 3: We have your vision for how you want to respond,
Speaker 3: and then we start drafting your content in a thoughtful manner.
Speaker 3: And that drafting is done using best practices. And this
Speaker 3: is where we are really fortunate to have Shipley. I
Speaker 3: talked about this last time. Shipley is a fifty year
Speaker 3: old company which is which taught people how to write
Speaker 3: persuasive proposals. That's what they do. They teach classes, they
Speaker 3: generate content. So now we have this information, we use
Speaker 3: their best practices. And one example would be when you're
Speaker 3: responding to an our RFP, be sure about customer centricity.
Speaker 3: We talk about customer's problem, but speak to those problems
Speaker 3: maybe in your terms without sort of taking ownership of
Speaker 3: the problem. And as if you're teaching them right about
Speaker 3: active voice, passive voice, and then very important those are
Speaker 3: all very important. And then you start with the problem understanding.
Speaker 3: Then you say where you have done this kind of work,
Speaker 3: you do you have really good past performance about this.
Speaker 3: So they are best practices of writing that come in.
Speaker 3: And this is why it takes us minutes and hours
Speaker 3: to generate the draft, right because you know, I was
Speaker 3: joking with you last year when I was the show
Speaker 3: that if you went to Chad Gypt and said, CHADGEPT,
Speaker 3: this is a very important question. I'm going to ask you,
Speaker 3: please think about it for five minutes before you respond.
Speaker 3: Chad GPT does not know what to do with the
Speaker 3: four minutes and fifty nine seconds, right, because it just
Speaker 3: gave you the answer right away. Right in our case,
Speaker 3: that's how it works. In our case, what we are
Speaker 3: doing is, Oh, you have asked us to draft this information.
Speaker 3: Let us try to Oh you told us this here, okay,
Speaker 3: and your knowledge repository saying this and one other thing
Speaker 3: that the comment that you read just a moment to
Speaker 3: good Richard that if you tell the language model that
Speaker 3: please take these ten things that I'm telling you and
Speaker 3: infuse them and generate some content. Language model, maybe listen
Speaker 3: to the first or second requirement and maybe the last
Speaker 3: requirement and then forget everything in the middle.
Speaker 2: Right.
Speaker 3: So this is where the thoughtful copilot comes in. We
Speaker 3: go step by step manner to generate the content.
Speaker 2: I love it, but I appreciate that this is I
Speaker 2: got to imagine when you've an experienced RFP person, you're
Speaker 2: probably comparing the new RFP to past ones you've done.
Speaker 2: It's got to be a certain amount of cut and
Speaker 2: paste there at least referencing material, which means you tend
Speaker 2: to try and solve the same problem over and over again,
Speaker 2: rather than this customer centric like do you understand this
Speaker 2: particular problem this time, which may or may not be
Speaker 2: explicitly stated, and then make sure you're tuning the material
Speaker 2: to that, even if it is from past material. This
Speaker 2: is where the language model actually is super advantageous, because
Speaker 2: you can train it on the past data and it
Speaker 2: will generate original texts yep, presumably with the impetus of
Speaker 2: the new customer problem attached to it. This is stuff
Speaker 2: that's hard for people to do, but I think the
Speaker 2: software be pretty good at it.
Speaker 3: That's right. This is a very important point I in
Speaker 3: my previous role we talked about. I was there for
Speaker 3: a long time and I didn't write proposals, but I
Speaker 3: often served as a technical smme for a certain section. Right, Hey,
Speaker 3: this is the section we need to write about. How
Speaker 3: should we write about and what would happen there is?
Speaker 3: I would tend to talk about the projects that I
Speaker 3: focused on, you know, even if they were not the
Speaker 3: best fit. Right, because organizational memory influences how you respond
Speaker 3: versus This is where the superpower of the language model
Speaker 3: is that you have one hundred different past performance to
Speaker 3: look for, and maybe the the ones I picked because
Speaker 3: of my bias because that's what I've worked on and
Speaker 3: I'm very proud of those, Maybe those are not the
Speaker 3: best ones. Maybe you should put something else that has
Speaker 3: better engagement to the problem at hand.
Speaker 2: Right, So.
Speaker 1: I can see the advantage of this because it's very specific.
Speaker 1: But then again, you have to feed it your data.
Speaker 1: What about the stuff that's built into office You know
Speaker 1: that that knows like all of your documents and your
Speaker 1: spreadsheets and your your files and your PDFs and all
Speaker 1: that stuff. Is it too broad? Is that knowledge too
Speaker 1: broad to feed an RFP into and say come out
Speaker 1: with something meaningful.
Speaker 3: Yes, that's a that's a great question. We we don't
Speaker 3: get that question now much much much more, But previously
Speaker 3: we used to get this question, why should I use
Speaker 3: your copilot? We have the M through sixty five co pilot, right,
Speaker 3: And the answer to that question is, if I can
Speaker 3: even take a step back, you have general purpose Shared
Speaker 3: chat GPT, claude.
Speaker 1: What have you?
Speaker 3: Right, and think of them as outside your firewall. Great tools.
Speaker 3: I love them. I'm sure all of you have two
Speaker 3: or three or more subscriptions because they are great at
Speaker 3: synthesizing information, summarizing information. Just you're trying to write something
Speaker 3: about a topic, it's great to collaborate with generate some content.
Speaker 3: Of course, you have to think you ultimately you own
Speaker 3: the content, and there can be inaccuracies, of course. So
Speaker 3: that's outside the firewall tools. And I say that with
Speaker 3: some hesitation, because only two weeks ago or three weeks ago,
Speaker 3: chat gpt announced that, hey, in addition to outside stuff,
Speaker 3: you can also point to a Google drive or something
Speaker 3: like that and we'll start ingesting your content. So let's
Speaker 3: just keep that aside for a moment. Let's just talk
Speaker 3: about Claude and chat GPT. Great tools, but outside the
Speaker 3: firewall tools. And then you have the office productivity tools
Speaker 3: like and three sixty five Copilot an equivalent that are
Speaker 3: built into, built into your enterprise. And they're great, right.
Speaker 3: They know who you talk to a lot on email,
Speaker 3: they know who the subject matter experts are. They have
Speaker 3: a full access to your one edrive, what have you.
Speaker 3: So they're great. But again they are general purpose, and
Speaker 3: two important differences right. You can use them to write
Speaker 3: an email to a customer whose irate. You can use
Speaker 3: it to plan a party, a going away party for
Speaker 3: your co worker. They do well, they will get all
Speaker 3: these things right. You don't have to worry about copying
Speaker 3: and pasting information because they have access to all the data.
Speaker 3: There is security built in. They only get to information
Speaker 3: that you have access to. All all great things with
Speaker 3: those productivity tools. But when we talk about a domain
Speaker 3: specific coal pilot like ours, there are more things that
Speaker 3: need to be done. So I talked about having very
Speaker 3: specific documents. We go to a customer and say, can
Speaker 3: you please give us your past RFP response documents, and
Speaker 3: maybe they have two thousands of those documents. We look
Speaker 3: at those documents and we break them up into logical boundaries,
Speaker 3: We classify them, we auto tag them, things like that.
Speaker 3: We are very opinionated about what we are trying to
Speaker 3: decipher from your proposal library content.
Speaker 1: It's a specific data set rather than everything in your
Speaker 1: enterprise exactly. You might not you know, you might not
Speaker 1: want that email to your wife about the new dog.
Speaker 1: In your response to the you.
Speaker 3: Don't want that and no, Caul, that's absolutely right. And
Speaker 3: there's one other sort of technical issue. Right, the general
Speaker 3: purpose tools don't know exactly how to chunk these documents,
Speaker 3: and often they chunk it at some page boundary or
Speaker 3: some fixed word boundary, and we cannot do that. We
Speaker 3: chunk them based on the logical sections the structure of
Speaker 3: an RFP document, so that when we provide that as
Speaker 3: a context to the language model, we provide the most
Speaker 3: precise context. And it's all about context, right. Language models
Speaker 3: can get confused easily, so we provide So that's one
Speaker 3: big difference, right, all of the upfront work. If you
Speaker 3: ask me where where we've done our most R and D.
Speaker 3: We are fifty people strong now, so you're growing quite
Speaker 3: a bit. So fifty to fifty two or fifty three
Speaker 3: people and many data scientists awesome, awesome team of engineers, mlops, DevOps, whatnot,
Speaker 3: all running on measure and of course data science engineers.
Speaker 3: So one part of research is ingestion. How can we
Speaker 3: be opinionated optimized ingestion? That's one part. And then also
Speaker 3: how do you glean some metadata from these documents? That's
Speaker 3: also important, right, because semantic search can break down if
Speaker 3: you're trying to find very factual information. So how do
Speaker 3: you extract the metadata things like that. That's one part
Speaker 3: of R and D. The other part of R and
Speaker 3: D is we talked about the flight plan. We talked
Speaker 3: about how you get so much data from the user
Speaker 3: about win themes and pain points and solutioning and all
Speaker 3: of that. Right, how do you effectively generate prompts dynamically
Speaker 3: so that you can generate quality content that adheres to
Speaker 3: proposal writing best practices. So those two things together is
Speaker 3: why there is a domain specific copilot, and people try
Speaker 3: to do that. Initially, Hey, let me just upload the
Speaker 3: RFP to the to the M three sixty five copilot
Speaker 3: and say can you generate a response? And then in
Speaker 3: some cases the response is limited. We are generating a
Speaker 3: response which can be one hundred and sixty hundred and
Speaker 3: fifty pages long. So, first of all, you can't do
Speaker 3: that with a general purpose copilot. And secondly, how do
Speaker 3: you without having to write all the prompts yourself? In
Speaker 3: our system, the users never have to write a prompt
Speaker 3: because the prompts are changing. The best practices for prompts
Speaker 3: before the reasoning models came along, and the best practices
Speaker 3: for prompting after using models are very different and you
Speaker 3: can't expect your end users to keep learning new prompting techniques,
Speaker 3: so we dynamically generate the prompts based on what is
Speaker 3: in your knowledge repository. Right, So those are the two things.
Speaker 3: Sorry for the long answer, but those are the two
Speaker 3: things that are different.
Speaker 1: No, that's okay. Yeah, that's really great. And this seems
Speaker 1: like a good place to take a break, so we'll
Speaker 1: be right back after these very important messages. Stay tuned.
Speaker 1: Did you know you can lift and shift your dot
Speaker 1: net framework apps to virtual machines in the cloud. Use
Speaker 1: the elastic beanstock service to easily migrate to Amazon EC
Speaker 1: two with little or no changes. Find out more aws
Speaker 1: dot Amazon dot com, slash elastic Beanstock, and we're back.
Speaker 1: It's dot net Rox. I'm Carl Franklin, that's Richie Campbell hey,
Speaker 1: and that's vishwas Lele our friend. And just as a reminder,
Speaker 1: if you don't want to hear these ads before, during,
Speaker 1: and after the show, you can become a patron a
Speaker 1: Patreon for five dollars a month. Go to Patreon dot
Speaker 1: dot netroocks dot com. We'll give you a feed that
Speaker 1: has no ads. You still have to hear me apologizing
Speaker 1: for them. Okay, well, you know the thing, as you
Speaker 1: were talking, it just kind of occurs to me that
Speaker 1: what you're doing is you're creating a company around a
Speaker 1: specific domain, right that where you know how to ingest
Speaker 1: the documents, you know how to query, and you fine
Speaker 1: tuned it. It's not like somebody is going to come
Speaker 1: to you and say maybe they have, but as anybody
Speaker 1: come to you and said, hey, you did such a
Speaker 1: great job for this domain, can you make me a
Speaker 1: company around this domain? And that's essentially what you've done
Speaker 1: with just the starting with the idea of their domain.
Speaker 1: And I imagine that's what a lot of our listeners
Speaker 1: are trying to do with their own businesses and their
Speaker 1: own domains, isn't it.
Speaker 3: So that's an excellent question, Carl, That's an excellent question.
Speaker 3: Let me my thinking has evolved over this question. I
Speaker 3: think you had asked a similar question last year when
Speaker 3: we talked, and my thinking has evolved. So, Number one,
Speaker 3: I think the patterns that we have figured out in
Speaker 3: terms of breaking down these documents in a domain specific manner,
Speaker 3: generating these prompts dynamically, those patterns generally are applicable to
Speaker 3: any business problem. So that's right. But what I've also
Speaker 3: come to realize is in order to get true value
Speaker 3: from jen AAI for any business process, any complex and
Speaker 3: proposal writing is a complex business process. There are hundreds
Speaker 3: and thousands of business processes. You really have to do
Speaker 3: a lot of domain specific work in order to provide
Speaker 3: value to the customers. It's not like you could take
Speaker 3: these patterns. Well, you have to Let's say you went
Speaker 3: to the financial sector, you went to the insurance sector,
Speaker 3: You'd have to do a lot of domain analysis to
Speaker 3: figure out what are the key entities, what should be
Speaker 3: the metadata. You'd have to do a lot of work
Speaker 3: to figure out the best practices, think about the prompt
Speaker 3: generation engine. So it's just not a matter of taking
Speaker 3: these patterns. Maybe the patterns supply at a higher level,
Speaker 3: but it is the work that has gone into realizing
Speaker 3: these patterns is where we have spent the last year in.
Speaker 1: Right, because it seems like there's a lot of people
Speaker 1: that are using RAG and it's kind of like what
Speaker 1: you're doing here, But I don't think there from what
Speaker 1: I understand, look at it, like as Richard likes to say,
Speaker 1: it's in a squirt bottle. You know, we just want
Speaker 1: to spray some RAG on our documents and be able
Speaker 1: to query them and get what we want out of them.
Speaker 1: And what you're talking about here is a lot more involved,
Speaker 1: Like it's not just a RAG tool that you're using.
Speaker 1: It's everything from the original document, how to break them down,
Speaker 1: how to chunk them, how to put them in there,
Speaker 1: the things to look for, the way to respond like
Speaker 1: it's U And so I imagine you're we don't really
Speaker 1: look in terms of accuracy because you're not saying you
Speaker 1: know how many widgets are in BIN forty five right now,
Speaker 1: You're not. You're really genuine generating a text response which
Speaker 1: you ultimately have control over.
Speaker 3: But accuracy is very important part for us. By the way, Carl,
Speaker 3: responsible they is an important tenet for us, and it
Speaker 3: manifests for us in many ways. And I'm really glad
Speaker 3: that we tried to adhere or architect to these tenets
Speaker 3: from the very beginning. And let me give you a
Speaker 3: couple of examples. Right, so, every time we generate a
Speaker 3: piece of text for you, the responsible AI tenant says
Speaker 3: that you should try to be transparent with the user.
Speaker 3: So you have to tell them exactly which document, which
Speaker 3: paragraph did you source the content from. That's one. And
Speaker 3: then ultimately we said, it's a shared responsibility model. It's
Speaker 3: about the complementarity between the AI models and the human beings.
Speaker 3: So how do you help the proposal writers get you
Speaker 3: an accurate response sooner? So we generate something called the
Speaker 3: hallucination report every time we generate a piece of text.
Speaker 3: It and what the haalucination report is that every time
Speaker 3: we write out some text, we try to extract any
Speaker 3: assertions that you've made in that text or the AI
Speaker 3: has made on your behalf, based on your knowledge repository.
Speaker 3: And we said, you know, you're saying that you've done
Speaker 3: these five thousand no cloud migration, but we really can't
Speaker 3: find anything like this in our knowledge repository, right, which is.
Speaker 2: A great thing for it to say when it can't
Speaker 2: find something, rather than make something up.
Speaker 3: Yeah, that's right. And we know that the sales team
Speaker 3: tends to sometimes take a creative license with some things.
Speaker 3: Maybe they combine to projects. Right, So it's not saying
Speaker 3: this is wrong, it is saying that it will it
Speaker 3: will serve you well to take this statement. We can't
Speaker 3: find an explicit reference to the statement in or repository.
Speaker 3: Can you come back and sort of take a look,
Speaker 3: and maybe you can approve that risk and say, no,
Speaker 3: we are taking these two projects in and we're saying
Speaker 3: that overall we have done these kinds of migrations for
Speaker 3: this aggregated agency, and that's how we are making this assertion.
Speaker 3: That's fine, you can approve that risk.
Speaker 1: Yeah, it may be an accurate inference or it may
Speaker 1: be completely wacky.
Speaker 2: But at least if you have the references, you can
Speaker 2: go evacuate it yourself if you want, Yeah.
Speaker 3: You can. You can go back and read. And the
Speaker 3: third thing.
Speaker 2: That we also do and argue the interpretation.
Speaker 3: Third thing that we also do is something called the
Speaker 3: completion report. Right, So the RFP document was forty to
Speaker 3: fifty pages long. It had instructions, it had evaluation criteria,
Speaker 3: it had many many requirements, and it's a very important
Speaker 3: aspect of responding that you're compliant. You're answering all the
Speaker 3: questions that you were asked to do.
Speaker 2: Yeah, you didn't miss any you didn't miss any.
Speaker 3: So we again try to generate a compliance matrix for
Speaker 3: you and say, look, these are the things that were
Speaker 3: asked in the RFP. But again, this is a very
Speaker 3: important part you have to go and validate that yourself,
Speaker 3: because language models can be inaccurate at times. So it's
Speaker 3: a very much an assistive technology, and we are not
Speaker 3: quite getting it to autonomous anytime soon. That press up
Speaker 3: button proposal comes out that you're ready to submit.
Speaker 2: But it also occurs to me that again this is
Speaker 2: something it's good at that it could lead you to
Speaker 2: cerve responses or lack of response to a requirement, the
Speaker 2: same way that a generative AI image recognized er may
Speaker 2: point a radiologist to a particular spot on a picture
Speaker 2: and say this looks anomalous yep, and encourage them to
Speaker 2: look in the right corners. Given all the guidance you're
Speaker 2: providing here, which I really appreciate, right that the flight
Speaker 2: plan approached the prompt generation to make sure it's accurate
Speaker 2: and focus on the right material with the references. Does
Speaker 2: the language model actually matter? Like you talked about open
Speaker 2: AI last year, Now there's been many more models released.
Speaker 2: I mean cloud was always around, but deep seeks appeared
Speaker 2: like does any of those make a difference given this
Speaker 2: strict set of guidance.
Speaker 3: That's a great question, So let me answer that using
Speaker 3: two important points. Right turns out that these rf peace
Speaker 3: can also have sensitive information, and you need to have
Speaker 3: things like CMMC two standards where you are explicitly telling
Speaker 3: the government that this information is not just public RFP.
Speaker 3: So think about you're going after some RFP that talks
Speaker 3: about some kind of things weapon system and even the
Speaker 3: RFPs can have what is known as a controlled unclassified information.
Speaker 3: So in order to support and show that you're handling
Speaker 3: that information correctly, you need to show to the auditors
Speaker 3: where your packets have traversed in that system. And then
Speaker 3: ultimately where did you send this data and did let's
Speaker 3: say you sent it to the external system like language model.
Speaker 3: Did that language model itself have a security classification and
Speaker 3: it is giving you guarantees about not logging that data
Speaker 3: beyond the metadata and things like that.
Speaker 2: Right, yeah, and the sovereignty where did that they actually go?
Speaker 3: Yes?
Speaker 2: Now, I mean this makes a strong case for running
Speaker 2: as a local model.
Speaker 3: Well, that's true, we use but you know the same time,
Speaker 3: these RFPs are very complex, so we want the best
Speaker 3: comprehension that is available.
Speaker 2: You want a trillion parameters, you need want trills.
Speaker 3: I need a trillion parameters, right, So what we do
Speaker 3: is platforms like asure opening. I give you that guarantee
Speaker 3: and you can actually show to people how your network,
Speaker 3: how do your packets from all the way from incoming
Speaker 3: RFP to how the response generation traversed a certain network
Speaker 3: path that the auditors can say, yep, we certify, we're
Speaker 3: okay with that, that these packets are fine. So that's one.
Speaker 3: So it's not just a matter of hey, let me
Speaker 3: just go send this data packet to somewhere else unless
Speaker 3: they have a classification. So that's one one important consideration
Speaker 3: for us.
Speaker 2: So just narrow is your choices of models based on
Speaker 2: whether or not they're going to clear see see two.
Speaker 3: Yes, that's one. And secondly, Richard, the other thing that
Speaker 3: we've realized is that these models have a personality. So
Speaker 3: just because they speak English doesn't mean you can just swap.
Speaker 2: Now anthropomorphic of you Vishwa's goodness, Yeah, you've pushed.
Speaker 1: Richard's answer promorphic button there.
Speaker 3: Yes, so these models have a personality against say saying that,
Speaker 3: And so I find this interesting when people say, hey,
Speaker 3: these are models are all English pays, so you can
Speaker 3: just switch one and see which one works. When you
Speaker 3: are generating hundreds and hundreds of pages of prompts. As
Speaker 3: we do, it is really important for us to understand
Speaker 3: the personality of the model, and we can change the model,
Speaker 3: and in fact, we change the models all the time.
Speaker 3: When we went from four to four turbo and so on,
Speaker 3: and now we're using oh one and three. Every time
Speaker 3: we change a model, we have to do extensive testing
Speaker 3: to make sure our prompting layer works well. But at
Speaker 3: least these models are a certain family. Part of these models,
Speaker 3: just switching to us different models.
Speaker 2: You've all described you're only talking about open ai models
Speaker 2: there so far.
Speaker 3: Yes, we're talking about OpenAI models, so that's the other part.
Speaker 3: So those two important considerations have gone in into deciding
Speaker 3: which models we go against. We're thankful to open ai
Speaker 3: and Azure open Ai that their models have generally kept up.
Speaker 3: It's not like they're not they're the ones who came
Speaker 3: out with the reasoning models. Of course, reasoning models are
Speaker 3: available on other platforms as well now, but they've kept
Speaker 3: up with the advancements that are happening. So we're pretty
Speaker 3: happy with where we are. But we constantly run tests
Speaker 3: to see how other models would do.
Speaker 2: Now and you mentioned already the difference between using a
Speaker 2: model that doesn't have a reasoning feature, and I'm doing
Speaker 2: air quotes yep, because I read the Anthropic paper where
Speaker 2: they talked about the fact that the whole reasoning behavior
Speaker 2: is a post back or behavior that the result to
Speaker 2: the prompt generated and then analyzed after the fact by
Speaker 2: the tool to generate the quote unquote reasoning. Right.
Speaker 3: So the idea here is that I'm trying to generate
Speaker 3: a response to a certain section. Sure, and either I
Speaker 3: can just in a non reasoning model, I would just say, hey,
Speaker 3: generate an answer, and we'll just go through a certain
Speaker 3: chain and just start generating a response immediately. But the
Speaker 3: reasoning model, it just tries to develop a plan and say, Okay,
Speaker 3: you're asking to generate this response. I could use this
Speaker 3: example versus this example. Let's try with this example, go
Speaker 3: halfway through and realize that there's not enough meat in
Speaker 3: this Let's just go back and switch the example. That's
Speaker 3: what reasoning is all about, right.
Speaker 2: Yeah, that's what it implies. But what the Anthropic paper
Speaker 2: says is that they're actually doing that after the fact.
Speaker 2: They already have the statement they want to say, and
Speaker 2: then they take their statement apart and fill in the
Speaker 2: pieces to show how they quote reasoned it.
Speaker 1: Yes, more like justification than reasoning.
Speaker 2: You showed me the equation. I knew the answer, and
Speaker 2: now I write out quote my work even though I've
Speaker 2: already got the answer. I'm just trying to fill it
Speaker 2: in to make you feel better because you asked me
Speaker 2: to show your work.
Speaker 1: Yeah.
Speaker 3: No, that's the anthropic paper, no question about it. But
Speaker 3: we are adding on top of that reasoning layer. We say, hey,
Speaker 3: which past performance do you want to use? How should
Speaker 3: you be writing about it? Things like that.
Speaker 2: Yeah, and I'm not going to say anything bad about
Speaker 2: the fact that it goes and poll's references because you
Speaker 2: need those. That's all the quality stuff for there.
Speaker 1: Yep, that's fishs. Last time we talked, we've got on
Speaker 1: the subject of how you think jen Ai and AI
Speaker 1: in general is going to affect the lives of average
Speaker 1: developers like us. Has your thoughts on that changed in
Speaker 1: the last year.
Speaker 3: My thoughts have not changed. I think developers who will
Speaker 3: greatly benefit from tools like that. It is not a panacea.
Speaker 3: You can't just say hey, I'm going to give you
Speaker 3: these things and go code me something. I think it
Speaker 3: puts developers who have been thoughtful, who have worked on
Speaker 3: the craft who understand the edge case as well, who
Speaker 3: are able to critically look at the code that the
Speaker 3: lllms are generating. They're in a very good position because
Speaker 3: they can really get that acceleration at the same time
Speaker 3: they're critically analyzing what's being generated for them. I think
Speaker 3: it's a real challenge for people who are beginning because
Speaker 3: it can very superficially tell you that it is working,
Speaker 3: but it may not actually be working exactly the way
Speaker 3: you want it to, or.
Speaker 1: The AI might give you a solution which is more complex,
Speaker 1: and you're building and rolling your own thing when there's
Speaker 1: already something available that does it, and it's not going
Speaker 1: to tell you that. And I've had this experience and
Speaker 1: you go back and you say, hey, can I just
Speaker 1: use X? And it says, oh, yes, absolutely.
Speaker 3: Yes, Yeah.
Speaker 1: Well why didn't you tell me that before? Are you idiot? Right?
Speaker 2: Yeah?
Speaker 1: So to your point, somebody who doesn't know about you know,
Speaker 1: tool X or tool why isn't going to find out
Speaker 1: that it gave you the answer that you wanted, which
Speaker 1: is how do I build this? It didn't say you
Speaker 1: didn't say how do I solve this problem? And if
Speaker 1: you did that, you might have gotten a different answer.
Speaker 3: Well, that's that's very important. It's also important to do
Speaker 3: it incrementally and iteratively so that, yeah, you don't just
Speaker 3: get two hundred lines of code, you have no idea
Speaker 3: and you run a test and you think it's fine.
Speaker 3: But if you do it incrementally, all of the basics
Speaker 3: of getting this right. Can I refactor this, can I
Speaker 3: get can can this piece be optimized? Using this? Yeah,
Speaker 3: it's accelerating that immensely, and so.
Speaker 2: You know, it's funny you're describing exactly what you say
Speaker 2: p wind does for RFP that experienced RFP builder respond.
Speaker 2: Builders can use this tool to accelerate the behavior. But
Speaker 2: it is an interaction the tools pulling resources from past
Speaker 2: work and suggesting options that then you could iterate on incrementally,
Speaker 2: piece by piece, so that you know, when we talked
Speaker 2: a year ago this was about making RFPs faster. Now
Speaker 2: you're really talking about higher quality and better tuned to
Speaker 2: the demands of the new RFP.
Speaker 3: Definitely, definitely, and the quality. We are reaching a point
Speaker 3: to where people say I can spot judge it PT
Speaker 3: generated text from a mile right, people see that I
Speaker 3: don't know if there's.
Speaker 2: Well AI slop is now in the vernacular for a reason.
Speaker 2: There's a lot of bad generative AI slop out there.
Speaker 1: I can definitely tell when images are AI created because
Speaker 1: they're so slick, you know, and people have made.
Speaker 2: A half he even fingers.
Speaker 1: Yeah, people have made a I don't know, hobby or
Speaker 1: maybe a career out of generating. You know, the most peaceful,
Speaker 1: sublime picturesque spot with a house by a brook and
Speaker 1: you know this and that, and they just post it
Speaker 1: on Facebook and say a beautiful place right with no contacts,
Speaker 1: with no you know, people are like, wow, where is that? Well,
Speaker 1: it's amazing, right, Yeah, it just doesn't exist. And I
Speaker 1: could spot those pictures a mile away.
Speaker 2: Let me tell you, as a conference organizer, I know
Speaker 2: when you use chat GPT to write your abstract, abstract
Speaker 2: at your abstract because they are all the same.
Speaker 3: Yeah.
Speaker 2: I got a thousand abstracts in and four hundred of
Speaker 2: them start with in a world. Yeah right, like come on.
Speaker 3: Yeah and b. Because we're so focused on the quality
Speaker 3: of writing, we explicitly look for these things where language
Speaker 3: models are trying to quote unquote write the perfect text
Speaker 3: because you know, you've given them ten things to write
Speaker 3: and they find the best way possible to pack that
Speaker 3: information and sort of present it. The problem is it
Speaker 3: is very onerous on a reader when you're packing it
Speaker 3: so much information right, and then overuse of cliches, making
Speaker 3: statements without sort of providing background information. These are all
Speaker 3: common sense of LLLM generated writing, and we take explicit
Speaker 3: steps in our engine to either correct this mistake or
Speaker 3: constantly remind the model to slow it down to make
Speaker 3: it readable, to improve the quality. These are all things
Speaker 3: that are works in progress. A lot more work to
Speaker 3: be done there, but those are the kinds of things
Speaker 3: that we're thinking about. Goes back to all of these
Speaker 3: things are needed to have a real impact on solving
Speaker 3: a business problem. When you think about this, it's not
Speaker 3: just a matter of taking this and saying, Okay, I'm
Speaker 3: going to go start generating financial reports tomorrow. There'll be
Speaker 3: a lot of work that needed to go in.
Speaker 1: As a programmer, you kind of think of yourself as
Speaker 1: a senior programmer slash manager, somebody who can write code
Speaker 1: but chooses to offload the boring stuff to a junior programmer.
Speaker 1: And it's all about the prompts, you know, It's all
Speaker 1: about what you ask it to do. And I've had
Speaker 1: the experience of chatchypt anyway where I said, you know,
Speaker 1: I'm trying to do this, and it might be about programming,
Speaker 1: it might not, and it will say, oh, well there's
Speaker 1: currently no way to do this and blah blah blah,
Speaker 1: this is not possible, and or maybe it gives you
Speaker 1: some suggestions that are just stupid, like is it turned on?
Speaker 1: You know, that kind of stuff, And then you can say, well,
Speaker 1: is any can you check the internet to see if
Speaker 1: anyone else is having this problem? And that is the
Speaker 1: most powerful prompt you know. If you're not satisfied with
Speaker 1: the answer it gives you just say, hey, goog google
Speaker 1: it or whatever you know, and it will come back
Speaker 1: with sometimes with yes, there's a somebody's having this product.
Speaker 1: It seems like this is a common problem and this
Speaker 1: is what this people, these people did to overcome it, right, absolutely,
Speaker 1: So it's it's all about the prompts and it's all
Speaker 1: about thinking in that sort of manager senior programmer to
Speaker 1: junior programmer mindset.
Speaker 2: Yep. But back to you. You know the p win tool.
Speaker 2: You're mostly writing prompts with the tool to then run
Speaker 2: them to get results for the operator, that's right, that's right,
Speaker 2: And because there is a repetitive piece to this of
Speaker 2: setting the restrictions and making sure the context is correct.
Speaker 2: Like I got to imagine there's a couple of big
Speaker 2: paragraphs at the top of every prompt just to make
Speaker 2: sure it stays in the constraint, and then specifics for
Speaker 2: the section you're in in the RFP.
Speaker 3: YEP, there's some systems prompts that go in and every
Speaker 3: time to sort of keep.
Speaker 2: And it occurred to me, you know, thinking about this
Speaker 2: problem space and thinking about in our reactions to AI
Speaker 2: SLOP and you know, detechnics and so forth. It's like,
Speaker 2: if you think this pattern matching software is awesome, try
Speaker 2: a human because humans are really good at finding patterns, especially,
Speaker 2: and that's what they see. There's certain patterns to what
Speaker 2: a lot of low quality l M tools generate that
Speaker 2: you immediately perceived. And it's now starting to shift our
Speaker 2: minds where it's now repulsive. Yeah, and we're in a
Speaker 2: funny place, but it's there's some good advice in here
Speaker 2: visual so I really appreciate it. Like a year later,
Speaker 2: you're speaking very differently about the product you're making and
Speaker 2: the tool and the way to use these tools like
Speaker 2: it shows that it's the evolution that's going on here.
Speaker 3: Yeah, I know, not Richard and call that's that's certainly true.
Speaker 3: Learned a lot in this process of awesome technology. There's
Speaker 3: no question about it. I think we'll be it's the
Speaker 3: next super cycle, as Gartner Le likes to call. They said,
Speaker 3: you know, every tech super cyclist twenty years. The last
Speaker 3: one started in two thousand, but digital and the Internet
Speaker 3: and we are at the We're at the intersection of
Speaker 3: the previous one and launching into the gen AI.
Speaker 2: Yeah. Yeah, this has been I felt like this is
Speaker 2: a fundamental shift in U acts.
Speaker 3: It's a fundamental shift.
Speaker 1: The way we interact with our equipment is about to change.
Speaker 1: But I think this generation isn't going to be twenty years.
Speaker 1: It's probably gonna be more like five or six or ten.
Speaker 3: True's that's that's so.
Speaker 1: Much has happened in the last.
Speaker 2: A lot happened in the first few years of the
Speaker 2: twenty first century as Internet came out of the dot
Speaker 2: com boom, and you know, we shifted to mobile.
Speaker 1: It's not like we've been going slow so far, no,
Speaker 1: but it does seem to be accelerating.
Speaker 3: I think, Yeah, I know, it seems to be accelerating.
Speaker 3: But also I would say that there is some platauing,
Speaker 3: which you may see counterintuitive when I say that plat
Speaker 3: doing because GBT five was not released. They were going
Speaker 3: to release GPT five. Right, Just a bigger and bigger
Speaker 3: model with more data is not leading to better results.
Speaker 2: Yeah, the last few years we've pretty much consumed the
Speaker 2: whole Internet into tokenization. There isn't more Internet to grab,
Speaker 2: so that that's not an exponential function, Like we're kind
Speaker 2: of addicted to the exponential function, but it only existed
Speaker 2: because a small group of people worked extremely hard to
Speaker 2: increase the density of silicon substrates. That's the only thing
Speaker 2: they you know, most everything else doesn't work that way,
Speaker 2: and this doesn't because there's only so much data. And
Speaker 2: it's very clear that bringing in more data generated by
Speaker 2: AI for this is like taking a photocopy of a photocopy.
Speaker 2: It's degenerative, it's not useful data.
Speaker 1: Well, I can't wait to talk to you next year
Speaker 1: to see what's new and see what else you've learned. Yeah,
Speaker 1: it's fantastic. Thank you, Vishwaz. It's always very talking to you.
Speaker 3: Always a pleasure to be on this show. Thank you
Speaker 3: for inviting me again.
Speaker 1: Well, thank you and thanks for listening, and we'll talk
Speaker 1: to you next time on dot net rocks. Dot net
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