Zohra : 0:01

Hello folks, Welcome to another season of Inside TechCom with Zohra Mutabana. In Season 6, we unpack how generative AI works and what it means for your TechCom workflow, From core concepts to practical use. We're going to go under the hood. It's time to adapt, create and thrive with AI. Let's dive in. Adapt, create and thrive with AI. Let's dive in. Hello listeners, Welcome to another episode of Inside Techcom with Zohra Mutabana. Today I have Kartika Raman, and I ran into her at the Convex conference this past April in San Jose. I got to attend her talk, which really got me thinking, hey, I should have her on my show and thankfully, Kartika agreed. So, hey, Kartika, welcome to my show. Thank you, Thank you. It was great meeting you. Absolutely, it was a pleasure, and I'm glad that you're here so that we can dive deeper into what you presented. But before we dive into that, Kartika, tell us a little about yourself, what you do, where you are from all of that good stuff.

Karthika Raman: 1:06

Right, right, yeah. So yeah, my name is Kartika Raman, like you said, and I am currently a lead technical writer at Salesforce. I am part of the AI content experience team, which is primarily responsible for documenting Salesforce AI and agent tech technologies, so that's kind of my primary focus. Oh gosh, I've been in the industry for a couple decades now. I started my career as a database administrator, it admin, that type of thing, and eventually moved into writing. It's a long story, but you know I worked in multiple companies, but now I'm currently at Salesforce.

Zohra : 1:46

Perfect. I've never had an opportunity to speak to somebody who's been a database administrator slash IT admin and moved into writing. Yeah, and I know you said it's a long story, but maybe you can give us a little gist of what brought you to writing.

Karthika Raman: 2:01

So, yeah, as it was primarily working on SQL databases, that's where I was focused on. And it so happened that one of the people that I worked with also was at Microsoft and they were actually also, you know, a database administrator by doing writing. So I was very curious I'm like you're a database administrator and now you're writing for SQL, like what is that like? And it was like, yeah, just come and talk to my team, you know, and maybe you can. I also said maybe I can learn some inside stories. That's how it started and somehow I got recruited for would you be interested in writing and a little bit about myself there.

Karthika Raman: 2:38

I always like to challenge myself to kind of look into spaces I've never been in before. So it was like, oh, I told them, but I have not really written. I mean, I've written some internal for our company employees on how to use our apps or things like that, but I've never written anything really formally and nothing's as big as for the entire world. So it was like I don't know if I can. They were like we actually need people who have domain expertise, who understand the technology, to come and explain. And you know, don't worry about that, just come and try it out. That's kind of how it started, yeah.

Zohra : 3:15

That's such a beautiful story, and what I liked about your experience was you said I go looking in spaces where I've never been before, and that kind of resonates with me because, as a technical writer, I'm literally documenting something that has not been documented or talked about. Exactly Right, so you're in the perfect realm in the perfect industry.

Karthika Raman: 3:36

Yes, absolutely yeah. It gives you that opportunity or quenches my thirst for wanting to learn new things and go into spaces. I'm not comfortable in that type of thing, for, yeah, technical writers have to have that kind of mindset where you are thinking about hey, this is new thing, so I need to learn. I need to learn, I need to learn not just for myself, but to impart that knowledge to others. So that is kind of really what I think cemented my career in technical writing. I really like learning technologies, but I think you even learn deeper when you actually try to teach it to somebody else. You know, yeah, it becomes really more solidified, let's say.

Zohra : 4:17

Absolutely, because you're teaching yourself as you're also trying to impart that knowledge. Quote, unquote, right, right. So it does take a lot from you when you're trying to do that, and that's what we enjoy. Perfect, all right. So, audience, like I mentioned, we met at the Convicts Conference this past April and your talk was around how technical writers sort of need to fit into AI and move forward with it. How do we own that in simple terms? But you also talked about prompt engineers and ontologists and how these new roles are emerging and what the field of technical communication is going to look like. Can you share a little bit about that talk? Let's take from there so that our audience has some background to it, because that's where we are starting from. That's the starting point, right?

Karthika Raman: 5:03

Yeah, sure, yeah, my talk was about you know how to think about our roles evolving in this technical AI era. That's evolving as well. Alongside. Things are moving at a fast pace we hear a lot about is my job going to go If tech writing is no longer required? What am I going to do after? So those were the type of questions I keep hearing in my own circles, within my company, outside, and it's not just for technical writers. I think almost every profession is asking that question Engineers, product managers, all of us are asking that question. It's like how do we evolve? In some ways, it's not unlike other technologies that have come before that have challenged us as to like is my job going to go away? But I think the pace at which this is happening is kind of, is what is a little bit unprecedented, I would say so it's like you need to think fast. Anyway, all that said, I think fundamentally, the shift for me, or the factors influencing this evolution, is like before LLMs came into the picture.

Karthika Raman: 6:13

The AI systems were built using rule-based things that humans gave to the machines, meaning they were very deterministic. The machines, meaning they were very deterministic, meaning you know, if you provided a certain input, you always got the same output. So if you gave the same input for the same input, you always got the same output. That was good. But these systems were challenged with nuances, ambiguity and working with unstructured data like region images and all of that stuff. So with the advent of LLMs, which are trained on large amounts of data, they can handle all of this stuff, but they are probabilistic and non-deterministic, meaning the responses can vary. They have great capabilities coming and more potential, but there's this dual nature where you can get a lot of capability but you're not always sure what you're going to get. And while the previous systems were things like rules based on classic programming, languages and decision trees and if-then-else type of things, now the instructions are being provided in natural language, so it's also open to interpretation as you talk through. As humans also, we try to interpret, but we have the capability of going back and forth and asking clarifying questions. We bring in the context that we have experience with and things like that.

Karthika Raman: 7:45

When you think of businesses and enterprises, they need responses to be auditable, repeatable and trustworthy. They want the AI to behave in a trustworthy manner. So how do you get these systems, which are probabilistic and non-deterministic in nature closer to what you want. So there are various ways to do this, and I think when you talk about prompt engineering or writing, which are essentially writing instructions in natural language, that is, to help the AI system steer in a way that they give you the desired outputs.

Karthika Raman: 8:24

Things like ontology, taxonomy, metadata all of these are the additional grounding or the context that AI needs in order to be more specific, in order to be more relevant and move towards accuracy in a better way. So that's kind of how I think about the roles and because right now we're communicating with the AI or the machines in natural language, that's where the technical writer's abilities and core skills come from. We have always written, or our goal has been to write clearly, concisely, to help someone, someone, a human user, perform a task or, you know, complete their job successfully. Now we're taking those and applying to the AI, either through prompt instructions or by providing terminology, taxonomy and those, the organizational structure, the business context, all of those pieces. I think that's kind of what, how I think about our roles and how we should think about where our skill set should kind of go, move forward.

Zohra : 9:36

I know that was a long answer. No, that's great Because I think you kind of really ground this discussion where you're coming from and where you'd see the roles, the new roles, evolving technical writers. I think that's a great background for, and context for us to take this conversation forward. Now we talked about LLMs and I'm sure most of our audience would know, but just for our context, it is the large language models and we are trying to talk in the context of generative AI. I just want to have some scope around that, am I right?

Karthika Raman: 10:08

Yeah, absolutely, yeah, yeah, large language models. Essentially, these models are trained on large amounts of data, data sets, data, and they use what we call deep neural networks. Just think of it as layers and layers of learning, of self-learning that it does, to kind of build its quote unquote knowledge, yeah, knowledge, quote unquote knowledge, yes, yeah, right, yeah.

Zohra : 10:34

So all these roles that you're thinking about in your experience is there at Salesforce? Are these roles already in existence or, as you're identifying them, there are these roles being created?

Karthika Raman: 10:48

That's a great question, and one thing I do want to kind of call out is, when I say these, when I especially I know in April we had I presented specific roles right, but I think of them as more like organization principles or things that you might do and skills that you might need, because I don't know that, because it's evolving fast, I don't know that we have people with specific roles. I mean, those are coming there, we are starting to bring those in, we're experimenting, but in some cases, your role might be still called technical writer or content developer or something. But you might be doing something specific Like, for example, you might be working closely with designers and engineers to design the interaction experience between the AI systems and humans. So those would be the content designers. It could be prompt engineering if you're developing designers. It could be prompt engineering if you're developing agents or AI features that require engineers to provide prompt instructions as well as classic programming languages. It's a collaborative work that you might do as part of your technical writing. So we are beginning to kind of do these roles and things like that.

Karthika Raman: 12:01

Information architecture is another place where we might be doing things like taxonomy, organization of our content and things like that. So we are moving towards those, but we haven't set in stone, I would say, what those rules are going to be called, and I think that's a good thing, because we don't know yet what we call. But I do also see outside of Salesforce. Sometimes people advertise for content engineers or knowledge engineers and things like that. They're all pretty much along the same lines that you're trying to do. Yeah, go ahead.

Zohra : 12:35

No, please don't apologize. I think you gave a fantastic response to my question that, since this field is evolving. When I say field, since AI is evolving and alongside with that, our roles are evolving, when I say field, since AI is evolving and alongside with that, our roles are evolving, we don't know where we're going to land. But what I want to come back to is the skills that you emphasized, right. That is where you need to focus on. It. Doesn't matter what your title is called, but you have those skills. The many skills that you talked about, the organizational skills, the taxonomy, informational architecture, surface those tap into that skill. That's what I'm taking away from your insight.

Karthika Raman: 13:13

Yes, absolutely yeah. If you don't have experience, start building those. And the other thing that I would emphasize regardless of the role that you're thinking, whether it's technical writer or other roles, even developing knowledge around AI, ml, those type of things the basics of it really depends on how deep you want to go and what you're trying to do, but you know, you should have some basic understanding of these things and I think, more than anything, key thing is to use them, test them, evaluate them for yourself. A lot of that learning comes from doing as well. So engage with the AI to see how it's working, understand its capabilities and its weaknesses, so you know how to work with them and then you can help others understand how to work with them. That's really some of the things that I would say. Regardless of the role, regardless of where your passion lies, these are some basic things that you should be thinking about.

Zohra : 14:12

Great reminder for all of us that we are on this journey together and we are all at different starting points and tapping into what that means. And getting I interviewed another guest. That means and getting I interviewed another guest and you will be listening to him as well. He talks about riding the crest, so that you're kind of moving with the wave. He uses the surfing analogy and I may not be describing it the best way, but this is exactly how we need to think about as we move alongside with this technology. You we kind of have to align with it. I do want to ask this Kartika, you mentioned you have a background. You were a database administrator, as a DBA. I'm 100% sure and this is my assumption, but please correct me that you were far more technical than many technical writers are, or many of them.

Karthika Raman: 15:03

Yeah, I know I've met a lot of technical writers. No, I don't know that. I would say far more. Yes, I did have a technical background.

Zohra : 15:10

I've met amazing technical writers who started without being technical and became non-technical and then became technical over time. So again, that's another journey. But your starting point was as a technical person quote, quote, unquote you had a better understanding of the database ecosystem. How did that lend to your journey as a writer? So we are slightly pivoting here, because that again brings me back into AI. Did that kind of help your journey as you've pivoted to writing and then now you, you know, specializing in this new realm?

Karthika Raman: 15:46

Right. Yeah, that's such a great question. I don't know that I've sat down and thought about exactly how that has. It was a long time ago, to be very honest. So that's there as well. But I think one thing it definitely brought me in because I know I've heard this from others as well, not just my own feeling it gave me the real world experience.

Karthika Raman: 16:06

Sometimes, when we are coming from traditional writing backgrounds and you don't have the exposure to the technology and the customers who use technology, sometimes you have to go and learn and also or interact with customers to kind of really understand what that is. But coming from a technical world, some of the challenges that you just think, so the customer empathy, the customer perspective was much easier for me to translate right in the beginning as my in my role as a technical writer. But I think as I move into AI technology, I think some of the things you know, like I I don't know anything about data science, or at least when I got into this I did not have some data science background. Yes, I had DBA background, but that kind of the ability to kind of dig through, problem solve, all of those things come as a DBA. So when you go into that mostly when you're doing database administration.

Karthika Raman: 17:03

Yes, there's some basic tasks that you do, but most often somebody will come to you with a problem and say how do I solve this problem? You learn to go deeper, you dig deeper. That was one of the key skills and I'm thinking and reflecting and building as I'm talking to you Is it that ability to go deeper. I think that is what kind of led me into kind of oh, I want to go deeper into this technology. How is it doing this stuff? Why is it so different different times and how is it evolving? I think that's kind of also the problem solving and having to go deeper and deeper also has kind of, I think, served me well, I would say.

Zohra : 17:41

Kartika, thanks for taking that question. I know this was not part of our list of questions and I like to sometimes get philosophical with our discussion, the reason being, again, we all come with a background. It's an invitation to my audience to think about okay, where did you start this journey? What were the things that you did to land where you are today? You've acquired skills along the way. Tap into that you mentioned, go deeper. It allows you problem solving. You have to, over time, develop these skills. You're testing, you are challenging what you're documenting because your empathy, you're trying to work, you're approaching with empathetic mindset, since you're trying to address the needs of an audience, and these are skills that we all have at some level. So that's why I kind of I loved how you brought this whole thing together about. You got to sometimes pause and think about okay, what are the skills that I need to apply? Because that is where you're thinking out of the box, in my personal opinion, yeah very right.

Karthika Raman: 18:48

Yeah, exactly, I think you're spot on.

Zohra : 18:51

Thank you for taking that question. We talked about ontology and I want to I know it's it's again not looking at it as a role but as a skill set. I've been reading a little bit about it, but the first time I really heard about it was, honestly, at your talk. I want you to just give us a very brief definition of what that is and what that role entails, because when I heard about it, I'm like what is that? And I went looking for information and then I'm like, oh, I think I can do a little bit of this.

Karthika Raman: 19:25

Again, just to expand what we do, yeah, for sure. So ontology is like I don't know, like it's a formal representation, high level. We can think of it as a formal representation of the concepts in a domain. So it is things like entities what are the things that exist in that domain, what kind of things are they? What are their properties? So if you think of retail domain, for example, hey, there's a customer entity, there's a product entity and a customer has an address, product has a sale price. So those are the properties or like the things that qualify what those things are. And then the relationship a customer buys a product and things like that. So I'm very, very high, you know, very high level.

Karthika Raman: 20:10

This is not by any means a technical talk on what ontology is, but think of that as kind of understanding the domain in the large X in a formal way. So that's really what ontology is. The formal way and I think traditionally it's like comes from things like library science. I think they have the formal teaching on ontology as well. But I think as technical writers we also have this way of organizing the structure, the concepts, building the relationships between the things.

Zohra : 20:43

This is not a technical discussion about what ontology is, but, again, our understanding of what that means or how we can translate what we understand about ontology to our profession. That's my goal and I think you did a great job of giving us some specifics on how to think about ontology within our domain and the content we work with as we move on. I would like to know have technical writers talked to you about you know what are their concerns? What do you hear from them? I'm sure you've had a chance, especially at Convex. And since your talk was around how our roles are evolving, can you share some? And since your talk was around how our roles are evolving, can you share some?

Karthika Raman: 21:23

Sure, I think the top question was is technical writer role going away? I think that was one of the top questions and I think we've touched a little bit on along our conversation here. It depends on how you are defining your technical writer role. If it's just core writing content, it's going to change at the very least. So I think we all think of, like, can I not write with AI? I've been doing that for such a long time. Do I need AI to actually start writing now? I don't.

Karthika Raman: 21:55

So what are we talking about? It's not about can you write or can you not write with AI. It's about, kind of, how do you build these knowledge systems, the information systems that AIs are going to consume for them and with them and with them I mean the AI. So you know how well can you leverage AI to build what you need and how well can you build it for the AI, which, in the future, might develop content or provide content for our customers on the fly, or build user interfaces on the fly or take action on behalf. So now, content and knowledge systems not only need to inform, but they also need to enable AIs to do all of those things. So it goes beyond just what we're traditionally writing for, so I'd like to think of us as developing knowledge and information, not merely documenting our features, for example.

Zohra : 22:57

Again, trying to expand how we think about our field and what we do, that bird's eye view of what we do, getting strategic about our mindset. That's what I'm taking away from this conversation. You're not just writing, it's not a very siloed role, but there are these different touch points that impact what you do.

Karthika Raman: 23:17

Exactly, and to some extent we're already doing a lot of those. I don't know that, especially in big companies. My experience is I've never been just writing. I've done a lot of other things, like we work with our engineers, we work with our marketing folks, we work with our right now with AI. We even have a new partner to work with at Salesforce. They're I forget what their exact term is, but they're mostly the team that works to make sure AI is behaving responsibility and humanely, so we partner with them as well. So writer role, I think has to go beyond, you know, just co-writing, and now we're kind of shifting more towards that broader view and the broader impact that we bring within the company.

Zohra : 24:04

Yeah, I want to revisit this question and we may have touched upon it, but for many writers who are coming from a traditional writing background, and especially if they're not working with a large company and they are a little behind on where their company might be with the whole AI experimentation, do you have any suggestions on how they can approach taking their journey forward?

Karthika Raman: 24:33

Yeah, that's a great question on where somebody should start. The first thing I would say is, regardless of where you are in this journey and your expertise, continuously testing, evaluating, using experimenting is number one. Even if you you know you have the basics done, you still need to do that Because, like we spoke about we know you kind of the technology is evolving, so you need to keep in touch with what's going on. So, experimentation number one try to use it. There are a lot of chat, GPT, and there are free tools as well, so you can start there. You don't even have to do the thing, and there are a lot of free learning resources as well.

Karthika Raman: 25:12

Familiarize yourself with the basic concepts. What's an AI? What's a machine learning model? How are models trained? What is that life cycle like? What are the issues, what about this bias and what about the ethics issues? Keep yourself informed. There's a lot you can learn out there right now, even if your company is not actively using AI or requiring you to use AI and try it out for yourself. Of course, you have to still be very careful on not leaking information in the sense of like putting information out there to these LLMs about your proprietary company information, but anything that's public or your personal you can still kind of use that. I would just be cautious on using these example.

Zohra : 25:59

Awesome Again, kind of taking that learning mindset, that growth mindset here. I would say expand on your AI literacy.

Karthika Raman: 26:08

Yeah, two key things. You summarized it perfectly. I tend to kind of talk around in a story manner and I really love how you kind of summarize at the end and kind of make it crisp.

Zohra : 26:18

Love it. Oh, thank you, I learn from my guests. It's all about the growth mindset, I would say, because in my previous episode I was looking for the word what is it? And my guest said it is AI literacy. Zora.

Zohra : 26:31

I love storytelling because storytelling is what I think makes the content relatable for us and you've done such a fantastic job of sharing the philosophy, the mindset, the takeaways with examples. All that is really coming together. And again, some of these questions might sound repetitive, but I do believe that we are all with large companies. We have more exposure to these AI tools. We are kind of trying to get a little more technical here, so let's assume that, okay, great, Now we've started. Our AI literacy is a few notches higher now. But as we start looking at data itself and I'm not asking for anything proprietary, but just at a high level I would like you to share, when we are starting to look at content itself, what in your world is happening that can translate to what, how other writers are looking at? Ok, now I've learned about AI, I've got all these skills. What can I do really with data? Right, I mean yeah.

Karthika Raman: 27:31

Yeah, that's, that was a clarification. I was going to ask yeah. So yeah, you know how do I apply?

Zohra : 27:37

Yes, how do I apply? There you go. You summarized it better than I did.

Karthika Raman: 27:41

No, that's fine, how do I apply? There you go. You summarized it better than I did. No, that's fine. How do I apply? And one of the, I think, very early questions that we were talking about is like this whole non-deterministic little bit of an opaque system that AI present. One of the things is like the knowledge that you build in understanding how the models are built, how they're trained, how are they actually generating these results or content and why is it that varied? That knowledge is important as you go deeper, because if you're going to explain this to somebody else for example, the traditional things that you might have written documentation on how these features work, but now you're also going to need to be documenting on explaining, setting expectations, on communicating risks. For example, if your company is developing models, let's say, for use by your customers, you might need to document how that model performs, what is its intended use and what is it, what are its limitations. So, in the experience that I'm building, there are a few things that are coming that we need to kind of partly think about is one of that is explaining product in a little deeper way than we have before. So that is one of the things that's coming. The second thing is our content is now one of the venues of building confidence in our customers to deploy or use AI agents.

Karthika Raman: 29:18

A lot of the questions are on is my data, meaning the customer's data? How is it used? Are you using it for training the LLMs? What's happening in some industries like finance and healthcare? They're high stakes and they are worried about sensitive data leakage. So how is that being handled? How is that being protected?

Karthika Raman: 29:40

So we're thinking not just in usage, but we're thinking much broadly about the ecosystem in which AI is being deployed and what are all the implications that come with it. So, really, kind of and using that knowledge that you have built and you're building over time, and kind of use that to help people navigate this ecosystem is that's how I would think of, like that's how you start applying to it. Again, I'm thinking probably big companies, but this will start, you know, happening in all kinds of ways, even in smaller areas. Even if you're writing a book, let's say, you're leveraging AI, you know you're to fact check your AI, make sure it hasn't made up stuff, or you're transparently letting your users know, like did it help you create the images for your book, for example? So I don't know, I'm not a fictional writer or I'm not a creative writer, so I probably don't know all the implications that you would have in that space. But I'm just saying, like, think of these things as concepts and then see how it applies to your work.

Zohra : 30:49

I'm just going to summarize all the amazing action items that I have taken away from this. One is about deeper explanation context, and that's something that I'm running into at my company. As we are writing topics, we think of every page as the first page, and I'm realizing that the context you talked about deeper product explanations that was your example and it's so true. We are writing now content that is for AI consumption and human consumption Exactly, yeah Right. And how do you give more context? Because, for a customer, they may have traveled or journeyed to that topic Exactly, yeah Right. And how do you give more context? Because, for a customer, they may have traveled or journeyed to that topic, yes, right, so they may have built that context or they may be coming with that knowledge already internalized, if they are advanced users. Exactly, but for you, an AI tool has no context. You have to provide that context to that AI and we are starting to think deeper. How are we going to organize our topics so that AI generate useful content, meaningful content?

Karthika Raman: 31:49

Yes, meaningful content, relevant content, and lean towards more accuracy than having to switch things up so when it tends to hallucinate if it doesn't have the knowledge or the context. So, yes, that is very important. So, well put, yes, we definitely, because we're writing to both human and AI systems, and AI systems don't come with the knowledge or the context that the human user has built over time or through their journey, because you know, like you said, we might have come to that topic based on something else or based on work we're doing, and for the AI, that doesn't exist, at least currently.

Zohra : 32:30

At least currently, exactly. And then, when we start thinking about context, we are starting to think about data accuracy and the implications of what that data can have if there is any sort of a leak or even hallucination. But our end goal is to make sure that our content is trustworthy and we are continuing to maintain that confidence in our content. In fact, that responsibility, the stakes are higher, I believe, now.

Karthika Raman: 32:59

Yes, exactly, yeah, because humans can be a little forgiving and you know, we can kind of put our own experience to that. But AI is going to interpret your content. Let me put it this way the content that you're responsible has to be accurate. Your content has to be accurate. In order for the AI to be accurate, you have to provide it the context, through various means, for it to apply. Accurate, you have to provide it the context, through various means, for it to apply that context and make the response or the action meaningful, like you said, and relevant. And the terms that you use, the terminology that you use, is how it will be interpreted by or be understood quote, unquote understood by the AI. So all of your content is now very much the data. The foundation, actually the foundation of our, or the core part of our work is now the foundation for the AI in order for it to provide a trustful, accurate and relevant response.

Zohra : 33:59

We've literally talked about everything that kind of starting from how, as a writer, you need to think about and then how you need to start applying those skills. Right. Are there any tools that you have to recommend that you may be using or you've encountered that can help you do these things?

Karthika Raman: 34:18

Yeah, we're also experimenting constantly. So I would say yeah, and we also have. I mean, I work at Salesforce we also have certain tools that are approved for us, especially if you're doing something that's not yet released to the public. So we have certain tools that we're using and when you have certain tools that you're authorized to use, you tend to use them more, even for non-proprietary information. So I don't want to recommend one tool over the other. I'm not trying not to be on the side of anything, but I can tell you the kind of tools that we use.

Karthika Raman: 34:55

I use Gemini quite a bit. I also like Cursor, because Cursor can really help you expand. It can even get you to use it. If you don't have any programming experience, you don't have to Like. For example, you can give it natural language instructions and ask it to build a script for you to run, maybe to run your content audits or something like that, and it will actually give you the code. You can ask it to explain the code to you. I mean, any of the LLMs will do that. But Cursor can both interpret the code you have. You can run it on the code, ask it to run it on the code and ask it to explain. It can generate scripts for you. It can help you run those scripts. It'll tell you and it'll give you a sample so it can get you really started on those pieces of understanding more deeper and how these things work. Okay, so, beyond Gemini I've used Gemini, I've used Quad, I've used ChatGPT Cursor is one of the things that, especially if you want to kind of use it to kind of help you get into a little bit more technical, it can be really helpful because it can interpret code for you.

Karthika Raman: 36:04

It can be used to also build sample code for you so you can give it natural language instructions. It'll also explain the code to you as well. I found that to be one of the useful tools even for things like running content audits. If you want to build a script to run content audit on your large documentation set, you don't have to be a programmer, you don't have to ask another programmer to help. You can ask it to build a script and explain it to you, give you examples on how to run it, et cetera. There are many, many tools out there and if you're doing graphics or things like that, there are various other tools.

Zohra : 36:36

Again, just experiment see what works for you. And again, the recommendation is not these specific tools. Make sure that you're using company approved technologies wherever you're working at. But for those who do not have any access to technology, these are just some ideas. We are not recommending or endorsing any of these products, just making that very very clear.

Karthika Raman: 36:56

Yes, absolutely.

Zohra : 36:57

I'm on board with you on that, kartika. This was just some ideas to get people thinking, hey, what can I do? And, of course, all these free tools that are out there. You use them if your company has no specific requirements, but if they do, then you have access to technology already. Then you're luckier, I would say, than many that do not have that access. Great discussion so far, kartika. I want to bring all this together. I think this is one question that has been on my mind. Teams what can companies do to future-proof their documentation? Teams, or rather, what can teams do to future-proof themselves for their documentation?

Karthika Raman: 37:35

Yeah, this is another thing I think we've kind of touched a little bit, but I would say, yes, the growth mindset that you called out the learning, the continuous learning mindset, is one thing. That don't be afraid to use these technologies or learn about them. As a technical writer, we always learn new technology, so keep that fresh in mind. Also, think about this is about knowledge, so it's more than writing for a siloed piece of documentation. Think more broadly. What is the context in which it's coming?

Karthika Raman: 38:09

The more you understand the context, the more you understand about organizing principles for your business use case and for the content, the more future-proofed you are to step into the roles that have evolved or even new roles that emerge. I think that's the way to think about it. Think that currently, as your role stands right now, is one thing is for sure, I think already being kind of in place. The way it is right now is not the way it's going to be tomorrow. So, with that kind of approach, is it, was it that you can do to make sure you're evolving alongside the AI is going to be key for future proofing yourself and be not afraid to try different things, and if you want to try different roles. That should be okay as well.

Zohra : 38:56

Yeah, one last question I promise We've talked about first. I think most of my questions have been around for writers who've been on this journey for some time.

Zohra : 39:06

I would be doing a disservice to my audience if they're starting this journey as a writer, as as beginners as new technical writers what would you recommend for somebody who is entering this field, who has some writing background, but it's mostly their educational qualifications that they might be bringing to the table? What would you recommend to them? I know, apart from the whole growth mindset and learning about the tools, is there anything that comes to your mind that you would be looking for if you were in a hiring position?

Karthika Raman: 39:35

Normally, I would even say, like, all the things that I just told you that are seasoned technical writer, also apply to the new writer. You do need to have those very same things, but you know, let's say, what would a hiring manager be looking for in the new writer? Yeah, their exposure to even if you haven't had a business opportunity to kind of use AI tools how have you used it? What are your thoughts on it Even the ability to kind of have an opinion, I think, is going to be key. Form an opinion of what you think you bring to the table. Form an opinion on how you would apply those knowledge to the work that you do. Even if it's just your personal opinion, that's okay.

Karthika Raman: 40:17

I think that is critical thinking. Don't outsource that to the AI. Talk about that as well. Like apply your critical thinking. Don't outsource that to the AI. Talk about that as well. Like apply your critical thinking skills. That is something that I treasure and I kind of selfishly want to keep to myself. I think it's okay if you give me the first draft, but I want to be able to critically look. You know that, showing that you're able to critically evaluate AI responses. Do your fact check. All of those skills are what you will kind of want to highlight in your, whether it's a job interview or the career path that you're thinking about. So those are hopefully some of the things that will help you.

Zohra : 40:54

Yeah, I think that's a great advice, because how do you translate this on your resume? Do you show that right? Yeah, if you have bare bones experience or you're just starting off on this journey maybe you've been in another profession then you may already have certain skill sets that you can leverage and talk about, but these are some really core ones that I think you brought to surface fantastic points. Kartika, this has been such a fantastic conversation. I know I said this was my last question, but I want to give you the floor in case you had any last minute thoughts or just want to wrap up this in your way.

Karthika Raman: 41:32

Right. Oh, thank you. I've enjoyed this conversation too. Let me say, yeah, this has been great talking to you.

Karthika Raman: 41:38

One thing I do want to say is like, yes, there is a lot of trepidation out there is if AI is going to impact my job. I think the reality is, yes, it is, but it's also going to open up opportunity. I think we had a report World Economic Forum report come out this January that talked about this dual nature. It said, hey, while AI is going to be probably one of the leading causes for workforce reduction but it's not just about workforce reduction it's expected to shift the way we work, it's supposed to shift our roles and also create new opportunities. So think of it that way.

Karthika Raman: 42:15

I think there was another article in New York Times that said something like I don't know if I'm exactly saying this correctly AIM might take your job, but it might give you 22 new ones, or something like that. So it's an interesting article. I know I would say, yes, go get yourself educated, not just in the AI, but what's happening in the world. That also kind of hopefully, will give you out of the box thinking and think about it and be excited, be cautious but be excited.

Zohra : 42:46

Be cautious but be excited. Great advice there. I do want to share this.

Zohra : 42:51

I get the Economist at home and there are articles about AI, of course, and one of the things that latest one that I read was how we shouldn't be outsourcing our skills. That latest one that I read was how we shouldn't be outsourcing our skills. That the very thing that you touched upon critical thinking, fact checking the skills, that you own them and apply them rather than outsourcing them to AI. So, on that note, kartika, thank you so much for being on my show. I really appreciate your insights, yeah thank you.

Karthika Raman: 43:16

You too, zora, it's been a pleasure, thank you, thank you.

Zohra : 43:20

Listen to Ins Inside Techcom on your favorite app and follow me on LinkedIn or visit me at wwwinsidetechcomshow. Catch you soon on another episode. Thank you for listening. Bye-bye.