Welcome to the deep dive. Today. We're really digging into something fascinating the art, maybe the science of AI product development. Right, We've looked at some great material from an expert in the field, focusing on how you actually build AI that delivers real business value. And our mission for you listening is really to cuf through that complexity, give you the tools to approach AI projects with let's say, ambition and clarity.
Yeah, and what's so interesting is that AI, Well, it feels a bit like science fiction sometimes, doesn't it.
It really does.
But the actual building blocks, they're becoming more and more accessible, almost plug and play in some cases that there's a catch. There's always a catch despite the promise, there's still a lot of complexity, a lot of uncertainty.
So what we want to do today is show you how to maybe sidestep the common pitfalls.
Exactly learn on the job, but in a structured way. We're drawing directly from the experiences laid out in the material we reviewed.
Okay, so let's untack this first big question, why even bother with an AI development project?
Right? The fundamental why.
The sources we looked at make a pretty bold claim here. They say if your business offers digital products or services, well, AI isn't just an option. It can enhance or even totally transform what you do.
It's a big statement, but think about the scope refuining marketing, automating customer support.
Adding smart search features, building completely new disruptive products.
Maybe exactly. The potential is huge.
But and this is a big butt. It feels like we hear about AI projects going sideways pretty often. Why is that?
That's the crucial follow up question, isn't it? And the sources definitely highlight some critical mistakes, like what Well, a really common one is simply using AI for the sake of AI, you know, building a cool tech solution that doesn't actually solve a real problem.
Right the solution looking for a.
Precisely the material gives this example of financial analytics dashboard already crowded with info. Okay, and the team decides haylo out of chatbot. Why is because you know, jenai hype is everywhere?
Ah okay? And how did that turn out? Because this is where it gets.
Interesting, right, it gets very interesting. The sources point out that this team ended up creating more errors than actual insights. Ouch. Yeah, the chatbot just couldn't handle what users threw at it. Requests were too complex, sometimes way off topic. People even tried asking for investment advice it wasn't supposed to.
Give, so that really highlights some core problems.
Then, Absolutely, things like misaligned data the user needs just weren't connected to the data the model was trained on.
Makes sense, and a lack.
Of guidance in the UI. You know, a simple chat box invites basically anything, but the model couldn't cope.
And user expectations probably didn't help.
No, definitely not overblown user expectations fueled by you know, maybe flashy marketing images that promised more than the AI could deliver.
So connecting this back, the big takeaway is you absolutely need a clear opportunity, a real problem to.
Solve you got it, have to justifies the investment. The sources propose this mental model for AI systems three core parts, which are data intelligence that includes the AI models themselves, and the user experience.
Okay, data intelligence UX.
And all of that is constrained by and this is crucial AI governance requirements. Can't forget those guardrails.
Right, governance is key. So using that model, how do we actually find those good opportunities those high impact areas.
It's really about spotting where AI can significantly improve things for the user. Let's take a music streaming app. Okay, users often get stuck in a rut, right, yeah, struggle to find new music.
Yeah, it happens to me all the time.
So AI powered recommendations there that could be a total game changer. Boost engagement, help discovery.
Makes perfect sense. And the sources mentioned several types of benefits AI can bring beyond just you know, shiny new features.
Oh yeah, definitely. It's not just about innovation. First up is.
Automation, okay, reducing manual work.
Cutting the cost of manual processors. Yeah, But the sources emphasize this AI cost equation. It's not just building and running the AI.
What else is in there?
You have to factor in the cost often overlooked of finding and fixing the mistakes the AI makesh okay, and the risk of mistakes you don't catch that could be significant.
That's a really important point about hidden costs. What's next after automation.
Then there's improvement and augmentation. This is where AI supports human creativity, doesn't replace it.
Interesting like a copilot kind of Yeah, Yeah, The example given is Miro, the whiteboarding software.
They use AI for brainstorming, diagramming, summarizing ideas. It plays to both human strengths and AI strengths. Nice mortalization, adapting the product to what each user needs, like that music app learning your taste over.
Time, tailoring playlists to my mood. I like that.
Exactly, But you need to be realistic. Good personalization it needs.
A lot of data, and bad personal is you can.
Really push users away. Privacy concerns too.
Obviously true, Okay, any others.
One more convenience, just making things easier, reducing friction. Think of an AI search that anticipates what you're looking for.
Get see the answer faster. Yeah, I can see the value there.
Absolutely.
So. The sources also talk about two different ways to actually launch these AI products, two strategic approaches.
That's right. It really depends on the situation. The stakes. First is the careful approach, think ready, aim.
Fire, so lots of planning upfront.
Loads of it SORO research validation before you commit to significant development. This is for when the cost of failure is really high, like healthcare. Perfect example, health tech company building a diagnostic feature, you'd need to rigorously check, user impact, feasibility, business value regulations, everything you can't afford to get it wrong.
Makes sense.
And the other approach, that's the fast approach, ready fire aim.
So build something quickly and see what happens.
Pretty much, get a prototype out there fast, get real world feedback almost immediately, like.
The music recommendation feature we talked about.
Exactly like that. Yeah, speed is key, Failing fast is okay if the initial costs are low and maybe the market's crowded. Yeah, you learn by doing.
And that fits well with how AI models are developed right, iteratively perfectly.
It lets you reduce that uncertainty step by step based on how real users interact with it.
Okay, great, so we know YAI, how to find opportunities, different ways to launch. Let's get into the actual building blocks. The sources call it the AI solution space.
That's the term they use yet data, intelligence and user experience.
Let's start with data, the fuel for the AI.
Absolutely described as the fuel. It's the raw material for training, for fine tuning, for evaluating everything.
And it comes in different forms the.
Forms textual data, needing NLP, visual data, auditory data, sensorimotor data for robotics, self driving cars, even computer code itself like for get Up Copilot.
Wow. Okay, broad range, very.
Broad but the absolute crucial point, and the sources hammer this home. Data quality directly drives output.
Quality, garbage and garbage out.
Essentially, that's the classic saying, and it's never been truer than with AI. The quality of your data dictates the value you can provide. Period, got it?
And how does the AI actually learn from this data? The sources mention different types of learning, right.
They distinguish between unsupervised learning and supervised learning.
Okay, what's the difference.
Unsupervised learning is about exploring the data, finding hidden patterns structures like clustering users into segments based on their behavior without knowing the segments.
Beforehand, so letting the data speak for itself in a way.
Yes, it helps uncover insights you might not expect. Then supervised learning is different. You train the model with labeled data.
So you tell it what the right answer is, for example, exactly.
You give it examples with known outcomes, so it learns to classify new unseen data, think predicting customer churn or segmenting users based on predefined criteria.
Okay, unsupervised for exploring supervised for predicting or classifying based on known labels makes sense.
And underlying all this data use, of course, are ethics, user privacy, data minimization, consent. These are non.
Negotiable absolutely so data is the fuel. What about the intelligence part of that model?
Right, the AI itself. The sources break this down into three main types. First, predictive AI. Sometime it's called analytical AI.
What does that do?
It focuses on well defined tasks analyzing data to make predictions or solve pretty clear.
Problems like sentiment analysis.
Perfect example, taking unstructured text like product reviews and turning it into a structured output like a numeric sentiment score.
But humans still need to figure out why people feel that way exactly.
The AI gives you the what, but humans often need to interpret the why and decide what to do about it.
Okay. Type number two.
Generative AI or GENAI. This is what everyone's.
Talking about, creating new content.
Precisely text, images, video code, even things like chemical structures for drug discovery. It's reshaping whole industries.
How does it work? Fundamentally?
It essentially combines existing information in new, often unexpected ways. It's learned patterns from vast data sets and can generate novel outputs based on prompts. It can even do surprisingly well on things like you know, standardized tests.
Impressive and the third.
Type AGENTIC AI, This is maybe the next frontier. This is where AI doesn't just predict or generate.
It acts acts how so it changes.
The state of the world. These agents combine language models with external tools think APIs, databases, functions.
So they can interact with other systems.
Yes, and they can reason, plan and learn from those interactions. The example given is like a product management agent doing what imagine it updating a project roadmap, automatically analyzing transcripts from sales, calls for insights, even drafting communications to stakeholders, all autonomously.
Wow. That's that's a significant step up.
It really is. It implies a much higher degree of autonomy.
Okay, so we have data and these three types of intelligence predictive, generative AGENTIC. What about the third piece, user experience? How is UX different for AI?
That's a great question, because it is different traditional software. It's deterministic, you click a button, the same thing happens every time, right. Predictable AI not so much. It's inherently uncertain. It can make mistakes, it can hallucinate, make things up.
So the UI has to account for that uncertainty exactly.
It needs to be designed with unpredictability and potential failure baked in. How do you do that well? The sources suggests things like showing confidence scores, like next to AI generated text it might say high confidence or medium confidence.
So the user knows how much to trust it precisely.
It helps calibray trust. Another idea is footprints, ways to trace the AI's steps. How did he get from the prompt to this result? That builds transparence.
That transparency seems key. The source has had a good example, didn't they from a sustainability reporting app.
Oh yeah, that was a great one. When it is generating a draft report instead of just a generic spinner, Yeah, it shows a progress window explaining what the AI is doing, analyzing financial data, checking regulatory guidelines.
Et cetera, keeping the user informed.
Exactly, and then the draft report itself. It uses those confidence scores color coded green for high confidence, yellow for HM.
Maybe double check this bit that really empowers the user, doesn't it. It shows them where their expertise is needed.
It absolutely does. It's about collaboration, not just automation.
And there was this idea of rethinking friction. Sometimes making things slightly harder is good.
Yeah, fascinating concept. Instead of making everything seamless. Sometimes you introduce intentional disruptions like what like maybe the AI flags its own potential errors, calls it a self critique, or it poses challenge questions back to the user, prompting deeper thought.
Why would you do that?
It can active the user, make them more engaged, and fight against that tendency to just blindly trust the AI, what they call automation bias.
Interesting, So deliberately slowing things down sometimes to improve the outcome.
Exactly counterintuitive but potentially very effective.
Okay, so you've got your data, your intelligence type, your UX designed for uncertainty. Now how do you make the AI model itself smarter, more specific to your needs?
Right? Customization? The sources detail three main ways to customize language models kind of increasing in technical complexity.
What's the starting point?
Prompt engineering? This is your entry point, the most accessible way. It's all about crafting effective instructions.
It's telling the model what you want more clearly.
Pretty much, you guide the model without having to actually change the model itself. Simple tweaks in the prompt specifying the tone, the format, the desired output.
Style like write in an authoritative, professional.
Tone exactly or use a friendly, approachable voice. Small changes can make a huge difference to the quality.
And there are different techniques within prompt engineering.
Oh yes, there's basic zero shot prompting just input output, then FEU shot where you give it a couple of examples to learn from by.
Analogy, learning by example, right, and.
Then more advanced stuff like guiding its reasoning step by step, asking it to think out loud, or having it generate multiple answers and then kind of vote on.
The best one, asking it to critique itself.
Even yeah, that reflection technique, asking the LM to review and improve its own output. Lots you can do just with prompts.
But prompts have limits. I assume they.
Definitely do, especially when you need the AI to use specific, up to date or proprietary information. That's where the second technique comes in. Retrieval augmented generation.
Or RAG ray. Okay, what does that do?
This is really powerful. Our RAGE lets the AI dynamically retrieve relevant information from external sources.
Like your company's internal database or recent articles.
Exactly, internal databases, articles, meeting notes, whatever you connect it to. It pulls that relevant info in at the time of the request and weaves it into the LM's response.
Ah, so it's not just relying on its initial training data.
Precisely. This is crucial for factual accuracy, especially with rapidly changing information, and for tailoring responses using your own private data.
Like that content generation app example, pulling in client case studies.
Perfect example, And importantly, RA significantly reduces those hallucinations.
Because it's grounding its answers in real retrieved information.
You got it. It has specific texts to base its answer on, rather than just making things up based on its general training.
Okay, so prompt engineering first, then RA for external knowledge. What if that's still not enough, then.
You move to the third most involved technique, fine tuning.
Fine tuning the model itself.
Yes, this is for when the base LM just fundamentally lacks intrinsic understanding of your domain and brand voice.
Like the example the content app struggling with really niche B to B sauce topics exactly.
Prompting and roged might help, but maybe the core model just doesn't get the nuance, the specific terminology, the required style.
So fine tuning trains it on that specific stuff.
Right. You take the base model and further train it on your own proprietary data, your past successful content. Your style guides your specific.
Jargon, so it really internalizes your specific domain and voice.
Deeply internalize it. It moves beyond just retrieving facts. It learns the style, the nuance, the perspective. There's also instruction fine tuning, which is interesting.
How does that work?
The model adapts more dynamically based on direct user feedback and edits. If a user corrects an output, the model learns from that specific instruction for future tasks, makes it more responsive.
Okay, three levels prompting our rag fine tuning a clear progression in complexity and capability.
That's a good way to put it.
Now, we've talked a lot about the tech, but building these products, it's not just code and models, is it. It's about people?
Oh? Absolutely, central. The sources really emphasize this. AI teams are almost by definition diverse and interdisciplinary.
You need lots of different skills.
You do software engineers, data scientists, UX designers, crucially, domain experts who actually understand the area the AI is working in.
And the product manager what's their role in this mix?
The PM becomes almost an educator and a translator. They have to bridge these different worldviews, different technical languages, different.
Goals, balancing the tech possibilities with the user needs and the business.
Goals, and reconciling priorities between all these different stakeholders. It's a complex coordination role, especially.
As we said, with all the uncertainty involved, how do you manage that the potential for failure.
This brings us back squarely to AI governance. The sources spend a lot of time on this, and rightly so it's paramount.
What does governance cover specifically in AI?
Several critical areas. First, security, protecting.
The whole system against what kind of threats, things.
Like data poisoning at attackers feeding bad examples to mess up the training data, data exiltration and leakage sensitive information getting out maybe through prompts or model outputs insecure output handling. Imagine the AI generating harmful code like a delete database query when it should have done a select and even model theft attackers trying to steal or replicate your valuable AI model.
So security is multifaceted.
What else under governance privacy by design, building privacy and from the start, not bolting it on later.
How do you do that?
Proactive risk assessments, making privacy the default setting, ensuring end to end security for data, and being transparent with users about how their data is used.
Transparency again seems vital it is.
Then there's mitigating bias. This is huge. AI models can easily pick up and even amplify existing societal biases present in their training data.
So how do you fight that?
Strategies include things like data audits, carefully examining your training data for imbalances, maybe using tools like fair learn and algorithmic bias mitigation techniques, trying to make the model itself fairer, and explaining its decisions.
Explaining decisions That sounds like the next part exactly.
Transparency and accountability, providing explainability, understanding how the AI reached a decision, and interpretability making the outputs understandable and actionable.
For humans and keeping humans involved.
Absolutely, establishing human in the loop or human on the loop processes, especially for high risk decisions, ensuring a human can always intervene, review, or override the AI.
So governance is really about building trust and safety around the AI.
That's a perfect summary. It's the essential foundation.
Okay. So with all that complexity, the tech, the teams, the governance, how do you effectively communicate the value of what you're building, especially to stakeholders who might not be AI experts.
Great question, The sources stress being concrete. Articulate the value in tangible terms.
So instead of just saying it boosts sufficiency.
Quantify it for efficiency and productivity. Show the time saved. The example they used was brilliant. Our platform automates data aggregation, saving your team eighteen point three to three hours.
Per week, okay, specific and.
Then translate that into cost savings. That's equivalent to nearly forty seven, six and fifty eight dollars annually. Numbers make the impact real.
That definitely makes it easier to understand the ROI. What about communicating well the inevitable failures?
Ah? Yes, communicating about failure. This is critical for managing expectations and building trust.
So don't hide the mistakes.
Absolutely not be realistic, the sources say. Instead of pretending engineers can eliminate all errors, you need to be upfront about the types of mistakes.
The AI will make like what kinds of mistakes.
They list common ones, false positives, predicting something that doesn't happen, like a sales spike, false negatives, missing something important like the impact of a flash sale, right, ambiguity or misinterpretation, just misunderstanding the input, bias in the outputs due to biased data and of course hallucinations the AI making things up.
So you name the potential problems.
You name them, you're transparent about maybe how often they occur, and crucially, you explain how you're working to continuously improve the system.
So it reframes mistakes not as disasters, but as part of the ongoing learning process exactly.
It builds trust, manages expectations, and can even enlist users in helping to improve the AI collaboratively.
That's a really insightful way to handle it. Okay, we've covered a lot of ground here. Looking back over this deep dive, what really stands out to you?
For me, I think it's that successful AI product development is this constant interplay, this dance, as you called it, between ambition and clarity. It's about having big ideas but also having a systematic way to tackle all the inherent uncertainty whether that's through really good data practices, careful model selection, designing that user experience thoughtfully for.
Uncertain robust govern in someplace is exactly.
It confirms the journey isn't really about achieving perfection right out of the gate. It's about that continuous improvement, that iterative learning, getting better step by step.
I agree. My main takeaway is maybe connecting that back to trust. By understanding all these nuances we discussed avoiding the AI for AI's sake trap, knowing when to use RAG versus fine tuning, being transparent about failures, You're not just building a product.
You're building trust with your users, with your stakeholders. That feels fundamental, It really does.
Which leaves us and you listening with maybe a final thought to ponder.
Yeah, I question, maybe in what part of your own work or even your life, could you apply this kind of systematic deep dive approach to understanding and maybe leveraging AI.
And if you did that deep dive, what unexpected insights, what little nuggets might you uncover along the way