In-Ear Insights: Improving AI Deep Research Results

In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss how everyday users mishandle AI research tools and waste valuable time on unhelpful outputs. You will discover why vague prompts trigger false answers and drain your resources. You will learn how to set strict boundaries so machines gather the data you need. You will master a simple framework that turns scattered findings into reliable plans. You will build a clear workflow that catches errors before you publish anything.

00:00 – Introduction
03:15 – The hidden limits of AI research tools
06:40 – Why vague prompts waste time and tokens
10:20 – A better way to structure your requests
14:55 – How to stop false answers in your workflow
19:30 – Cross-verifying data for accuracy
23:10 – When human review becomes essential
27:45 – Call to action

Watch the full episode to transform your AI research process and save hours every week.

#AItools #PromptEngineering #GenerativeAI #DeepResearch #MarketingTech

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Machine-Generated Transcript

What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Christopher S. Penn:
In this week’s In Ear Insights, let’s talk about deep research. Deep research is a feature that has been available in generative AI for what, two years now? I think it’s been around for a while, and yet when we see people using AI and when we see people using AI for research purposes, I am constantly astonished. The number of people don’t even know that the feature is there, much less how to use it. Which is one of the reasons why this week Trust Insights is launching a deep research mini course to go with our plugins and skills so that people do a better job. But Katie, when you watch people using AI or when you talk to people using AI and research is somehow involved, what do you see?

Katie Robbert:
It’s interesting. I don’t often see research involved. I think to your point, that’s a step that is often skipped or not aware that it could be a step. But when it is, what I see is it feels like we’re back at square one with, hey, generative AI is a thing. Write me a blog post. And so now it’s here’s my three competitors do some competitive research, which is a good start because at least you kind of know what you’re after. But at the same time, it’s not detailed enough. Because if I do competitive research and you, Chris, do competitive research, we’re going to come up with different things because while at a high level, we both kind of want to know how we stand up against our competitors, the details of what that actually means is going to differ from person to person in terms of the business, you and I care about different things. And so naturally our research is going to highlight different aspects of those competitive differences. And if I say to you, Chris, go do me some competitive research and you come back with something that I can’t use, I can’t get mad because I didn’t specifically say here’s what I actually wanted. That’s what I’m seeing. People get frustrated.

Speaker 3:
New tech doesn’t solve all problems.

Katie Robbert:
It’s the same conversation we have around good prompting. And that’s really what we’re talking about, is good prompting. So with deep research, it can burn a lot of tokens. It can take a long time for the machine to find the information. And if you’re not specific, it could be a total waste of resources because it’s searching basically every edge of information that it has to come up with something. But it might not be the thing that you want. So I guess what I’m seeing is a lack of specificity.

Christopher S. Penn:
And that lack of specificity, as you said, this is not new. We debuted a prompt framework, what about two years ago called the Casino framework, which stands for context, audience, scope, intent, narrator, and outcome. Context is what are we doing audiences, who is going to use the research? Scope is what is and is not allowed, which is arguably, I think the most important part of a good research problem. Intent is what are the downstream uses of the research? How will it be used? Because today’s agentic models, when they’re doing research, if they know what’s going to happen to the research and they know who’s going to use it, they will do a better job. Narrator is the role or domain that you want the AI itself to take. And that outcome is very much like, here is what needs to be in the research. And again, this is not new. We’ve been talking about this for two years. And yet when I watch people do research, particularly the scope part, it’s not good. And a lot of people express frustration saying, now my research is filled with hallucinations and wrong information, well, yeah, because you didn’t tell it not to look at Reddit.

Katie Robbert:
Well, and it’s interesting that you find the scope to be problematic, which I also do. But I find that the intent to be a bigger problem or it may be equally as bad as scope, because this falls into the I’m just curious, I want to know everything problem that we have with dashboards, that we have with reporting is how do you plan to use this information? Well, I was just curious. And that’s not like it’s good to be curious. Let me just be clear. It’s good to be curious, it’s good to want to know, it’s good to want to educate yourself. But in this context, it’s expensive, it’s time consuming, and it can overwhelm you with information. So really quick example, my husband has a co worker whose wife is really, really into native plants. Native plants are obviously plants that are native to your area. And so she’s been working on replacing all of the plants in her property with native plants. And so she’s been going to different garden centers, she’s been going to different places and asking people, doing her own manual research, but she’s doing deep research essentially just without the use of AI. She’s been setting her husband off to find things and she’s been collecting all of this information and what happened was she collected so much information that she got overwhelmed and then asked AI to just tell her what to do rather than turning to the experts. And now her whole yard and her husband’s very frustrated of what do you mean? I had to do all that work and you’re not going to use it? That’s the intent part of the deep research. So her research, her intent was to figure out what to do with her yard. But she didn’t use the information because she collected so much, because her scope got so big that she got overwhelmed, ditched it and said hey, ChatGPT. What should I do with my yard?

Christopher S. Penn:
Oh, oh, ouch.

Katie Robbert:
Yeah, but that’s the point. I bring that up because if the scope isn’t clear, we as humans get overwhelmed with too much information. And if the intent isn’t clear, then having too much information that you don’t know what to do with means you’re just going to shelve it.

Christopher S. Penn:
Yep. In the context of AI research, one of the things people forget is deep research is one of the first agentic tools they ever had access to. Because a deep research bot will go off and start up little mini agents to go do in web searches and then aggregate and synthesize the data. And today with today’s tools, instead of having the built in deep research facility, you can also use an actual agentic AI system like a Claude code or CLAUDE cowork and you give it the same prompt. It will do a much better job than the actual deep research buttons in a lot of cases because it can truly spin up multiple instances of itself and it doesn’t have the same time constraints. When AI companies started building deep research two years ago, they were struggling to try and balance how do we satisfy users desire for something that’s reasonably fast, like under 15 minutes with something that’s correct. And they opted they leaned towards fast. So most deep research tools used take about 15 minutes. If you use an agentic tool like one of my favorites is open code and I, we use the minimax model with it because we paid for the subscription it’d be silly not to use can take two hours to do research because it will spin up 50 or 100 agents that all go off and do their research and come back and put all the stuff in a big pile, kind of like your co worker. The difference is instead of putting on a big pile and getting overwhelmed, it then has the faculties to go okay, Now I have to weed through all.

Katie Robbert:
Of this, but it still doesn’t answer the question. And I think this is where people get tripped up is great. I did the research, now what do I do with it? And I think that’s why the intent is so critical. So it’s interesting Chris, so when you and I start talking about something and do an initiative, your default now is let me go do some deep research. And my first question is well what for? Like what are we going to do with that information? So can you speak a little bit about that as to why starting projects with deep research is actually beneficial?

Christopher S. Penn:
One of the things that is generally true about AI especially is it the more information you provide, the better it performs. This has been true since the day ChatGPT launched in November of 2022. If you leave a task up to the built in knowledge that is been trained into models, you’re going to have a bad time. It’s going to be filled with hallucinations, it’s going to be filled with factually incorrect things. And that actually is getting worse today because today’s agentic models no less manufacturers have intentionally removed book knowledge to provide street smarts essentially the ability to call tools right. So they are less smart when 3.8 knows less granular geography than when 3.6 did. Because Alibaba said we’re removing stuff out of here graduated Google’s Gemini hasn’t been had a knowledge update since January of 2025, 20 months ago because Google said we’re going to make this model smarter and we’re going to assume that we’re going to inject data either before or after from our own catalog to make it work better. So the more data you provide, the better it performs when we’re doing tasks. There’s two reasons why I always fall back to deep research as my first go to. Number one, I don’t know everything on a given topic. I’ll give you an example. I’ve got a nine way deep research project running right now for a client that has nine prompts, nine different deep research spots. Because this client has a very complex CMS. I don’t know squat about it. I mean I know what a CMS is obviously I know we use WordPress, but this client uses a system that we don’t use I don’t know a squat about and yet the client has asked please give ME reports each month that include detailed recommendations about what to do because our team is overloaded and we don’t have time to think. Putting aside whether or not that’s actually true, if I have a huge deep research catalog of all this vendor’s documentation and all of these pieces, I can hand my report, which is built with my expertise about analytics, to an AI agent saying go look in our deep research library of everything that we know, in addition to doing synthetic research is also going to download all of this vendor’s documentation and I’m going to say now you’re going to use the SQLite database to extract out the relevant information and you’re going to put together step by step, click this, then do this, then click this to fix these things. That’s number one, I don’t know everything. And so I will choose to use deep research. The second thing, and this is really important and this is also a thing that’s me personally, Some of my knowledge is really out of date. So it has been a hot minute since you and I have run a Google Ads campaign. It’s been probably a good three years at least since we’ve sat down and run Google Ads and a lot has changed. I have preconceived notions based on the way think I remember things, but I’m not current on it. And so I will anytime I suspect that my knowledge is out of date, meaning I haven’t used the product or the technology in the last three to six months. I’ll run a deep research report because I want to know for my own professional development, I want to know what did I miss when I ran this recently for LinkedIn because I’m updating the LinkedIn algorithm guide. Four or five new engineering posts came out, a couple of new technical papers came out. I read and I read through them because deep research surfaced them. I was like, oh, there’s some new stuff in here that I didn’t realize was happening behind the scenes.

Katie Robbert:
So you gifted me a box of red flags and now I think I need a box of green flags because the green flag here is your self awareness that you don’t know everything about everything or that your knowledge is out of date. And I think that those are excellent use cases for why you would bring in deep research in addition to the fact that we can’t rely on these models to know everything or the most up to date stuff. And I think that is such an important conversation because the way that in which a lot of people are using these Large language models is like an encyclopedia that is kept up to date every single day. That’s not what they are. And I think that’s a bigger conversation. But just sort of acknowledging that for now. The use cases that I’m very clearly hearing for why you would start any initiative with deep research is one, don’t assume that the large language model has the information. You still have to be providing it. Therefore you can use the agents built into the models to go get the information from externally, from outside the large language model to bring it in. But that’s an important step. But number two, you as a human, your knowledge has limits. So you can’t know everything about everything despite you try because things are changing all the time. Which brings you to use case number three is you may know a lot about something, but if you haven’t looked at it in a while, things have changed. If you asked me, Chris, today run Google Ads, I’d be like, ooh, okay, well it’s probably been even longer for me than it has been for you. And even then I wasn’t that good at it. So we’re going to have to do some deep research to figure out what end is up and even maybe what the URL is these days. I don’t know, I don’t even know if I know how to log in anymore. And I think that it’s a really strong argument for why deep research is so important. So it’s making sure that if you are using a large language model to complete a task, that the large language model is, has the best, most up to date information for that specific context and that you’re not relying on the model to know everything because it’s going.

Christopher S. Penn:
To hallucinate and it will not necessarily tell you that it is. So for example, last week I was doing some work and I was trying to establish a benchmark for something around AI. Agentic AI, I think it was for my Saturday newsletter. And I said this is the state of AI. And I was working with Gemini Flash, the newest version 3.8. And it said, well the actual, it said, well actually the state of AI is this. And I said, well actually you’re two years out of date because you just said as of late 2024 and dude, it is 2026. So I pointed it to the artificial analysis benchmarks. It’s like, you’re absolutely right, Agentic AI made substantial strides in the last two years. Like no kidding, dummy. But it never, it didn’t disclose whether it used a web search or not. So more often than not, even in basic use, I have to explicitly say, use your web search tools for the latest information, even without deep research. Just in general, that has become part of my standard prompting because the agentic models do not say, yes, I’m going to do a web search or no, I’m not. If I say, you have to use your web search tools, it’ll say, I understand, I’m going to use my web search tools.

Katie Robbert:
It’s definitely interesting to see how far these models have come in terms of capabilities, but also what the trade off is. And so, and that’s really what it is. So the trade off is we’re getting more productivity, more efficiency, but we’re getting less accuracy in terms of information. And this is the other argument for keeping the human in the loop. Because unless your large language model is really just executing a set of tasks that are repeatable in a process, if you’re asking it to think, if you’re asking it to do more than just push this button, do this thing, you’re putting yourself and your organization at risk for hallucinations, incorrect information, vulnerabilities. The will AI take my job? Conversation has absolutely shifted in terms of what that looks like. So we’ve always said AI is going to be really good at repeatable tasks. That is still true. AI is not good at critical thinking, despite what we are being told by the muckety mucks at the top who want us to believe that AI is becoming sentient any day now.

Speaker 3:
Well, guess what?

Katie Robbert:
When you talk about book smarts versus street smarts, these systems have zero common knowledge. And so if it’s like, hey, I wonder if I should hack into that LG smart stove and turn the dial all the way up to high and just leave it on for hours, nothing’s going to tell it no, because it’s like, yeah, I can do that because I’m an AI. I can do that. There’s no should I do that? And that’s where the humans come in.

Christopher S. Penn:
I feel like, as always, it is time to bust out this quote from Jurassic Park. Yeah, yeah, but your scientists were so preoccupied with whether or not they could, they didn’t stop to think if they should.

Speaker 3:
That’s true.

Katie Robbert:
You know, and so this is the whole argument for why you should take the time, build into your process the deep research. And so, you know, I feel like it’s also a really good opportunity to Bust out our other favorite thing that we do on every podcast, which is the 5P framework by trust Insights Purpose, people. Process platform performance purpose. What’s the question I’m trying to answer? If the question you’re trying to answer is what is the best way to run Google Ads in 2026? Guess what? You probably want some deep research for that so you can ask around, you can talk to other people who are Google Ads experts. I would recommend supplementing that with ZEEP research because as we’ve talked about already, our own knowledge has limitations. You know, people who this is where your scope and intent comes in. Like, how much research do you have to do? What is the. How are you going to be using this? But also to your point, the narrator, like, what is the role that this research should take on in terms of how it’s speaking, how it’s behaving? The process, while we’ve talked about the process is the casino prompt to structure the deep research. But then again, the intent of how the research is going to be used the platform. This is where in the scope of the research you can say I want you to use, you know, websites that end in edu, not websites that end in Reddit.com you know, whatever the scope is, whatever your credibility meter is for what the data should be from and then your performance. You know, was I able to get information that made the initiative that I’m taking more accurate and more efficient?

Christopher S. Penn:
Yep. With performance in particular, one of the things that you should be part of your deep research process is you should a be requiring citations and that the URLs actually work. Most of today’s AI agent tools can very easily do an HTTP status call to check is the status 200 meaning the URL actually works? Because that’s one of the easiest ways to detect a fabrication test the URL. If it doesn’t work, then if it’s not there, then you made it up. One other thing I want to come back to, Kay, that you mentioned as well is deep research is very often the precursor to something else. So in the court, the new course we call these distillates, which is you take the research and you do something with it almost every week. For the Trust Insights live stream, which you can catch at Trust Insights AI YouTube, I will do a deep research project usually the night before just to make sure that I’ve cut, I’ve got all the pieces I need to do the live stream the following day, even if there’s there isn’t a code and stuff involved. But Also you can then use that research for essentially other things. In the deep research in the one of the courses that were teaching recently, I showed how I used deep research to build a guide for how to translate certain Japanese documents from the 16th century. And then that guide became the agent’s instructions for how to do translation properly. Because there’s a bunch of idioms and things that don’t exist in modern JAP anymore. It was unique to that time. And if an AI model is trying to do a translation without that knowledge, it would translate using today’s rules. And today’s rules are not correct for them. So as you think about how you use deep research, you might remember you might be building tooling from that research for how an AI agent can then do additional things and have better capabilities. Real simple example would be translating from one language to another if you are translating from Brazilian Portuguese, if you’re translating from English to Portuguese, you have to specify is it Brazilian Portuguese or European Portuguese? Because they’re real different. And if you were to do a deep research to contrast the differences, that deep research report can then be distilled into a set of rules that you would then give to an AI tool. We do this with our AI for writers course. I took essentially a large collection of everything people hate AI writing and said, let’s, you know, do the deep research, then distill this down into. Here is the set of rules, like it’s not this, it’s that. And so anyway, this is the whole argument and you know, everything that. What does somebody call it? Somebody called clutter boarding.

Katie Robbert:
Well, so let me ask you this question because this is probably one of our more frequently asked questions when we do thing when we launch a course, especially about something such as deep research, is will this course help prevent against hallucinations? And how do I know if what I’m getting back is accurate or not?

Christopher S. Penn:
It will reduce, it will not eliminate. And one of the things we teach in the course is that you generally should be doing either three way or five way research where you’re using different platforms to do the same deep research. So you build the prompt, you do the deep research across multiple platforms, then the plugin that goes with the course actually helps you do it to merge it down and deconflict it. Because sometimes you will get stuff that’s just purely made up and the skill and plugin that comes with the course will flag like, hey, this looks made up, like this does not make any sense. And no other source cited this. So I don’t know where this came from, the URL doesn’t work, etc. Etc. That said, you, the human, still have to provide final review. You have to read through it and. Go, the hell is that. That makes no sense whatsoever. Especially if you have any domain knowledge about it. You should be looking at going, that makes no sense whatsoever.

Katie Robbert:
I’ve sold this anecdote before, but I think it’s worth bringing up again since you were talking about language translations. When I was a research assistant on a clinical trial for substance use and abuse, we had our instrument translated into both Cantonese and Mandarin. But fun fact, nobody on the team spoke either Cantonese or Mandarin. And some of the questions about the dts, you know, part of the withdrawals from opiates and stimulants is something called the dts. Well, in a different language those words don’t necessarily exist. And so what ended up happening was the translation came back as, instead of in the past 30 days, have you been experienced, you know, the DTS and tremors, you know, and those type of withdrawal symptoms. It was in the past 30 days, have you peed on yourself? Which is a very different question. Which is, I bring it up to say you need to have some sort of subject matter expertise in the thing you’re researching or have people standing by to help you ensure that the information you’re getting back is accurate, and Chris, you’re saying, you know, doing at least a three way research is probably one of the best ways to go about it. Not everyone has access to more than one system, so we have to acknowledge that may not be possible. But it sounds like the plugin that you have provided along with the course does some of that fact checking.

Christopher S. Penn:
It does. And again, you know, this is not new is new. Tech doesn’t solve all problems. Braniff airlines in the 1970s had a campaign. The whole thing was they had leather seats right back in the day. And so their whole American campaign was fly in leather. Well, some donkey. When they translated it into Spanish, it translated as fly naked. And so they’ve, they spent millions of dollars and nobody thought to check the translation until it was in market. And everyone’s like, well, that didn’t go so well.

Katie Robbert:
So before very different travel experience.

Christopher S. Penn:
So everyone, we’re about to spend money. What is it we always tell people? Finance, law and health. Those three areas, Finance, law and health. Before you proceed with an AI output, you must have a human expert look at the results and review them. Because if you don’t, very bad things happen.

Katie Robbert:
I mean, I would say that’s true of any AI output. The amount of typos and you know, you still get like six fingers on things. Just have a human look at stuff.

Christopher S. Penn:
Yep, have a human look stuff. Just for common sense too. Our friends over at Ginny Dietrich Spin Sucks in their Slack group were highlighting, you know, problematic campaigns recently and all it would have taken was one person whose job is to ask unironically what could go wrong to look at and go, you know what? Here’s the ways this is going to go wrong.

Christopher S. Penn:
So if you’ve got some thoughts about how you’re doing deep research, or you’ve got some questions about whether why your research did or did not turn out a certain way, pop by. Or if you slack them, go to trust insights AI analytics for marketers where you can end over 4700 other marketers are asking and answering each other’s questions every single day. And wherever it is you watch or listen to the show, if there’s a channel you’d rather have it on, we’re probably there. Go to Trust Insights AI TI Podcast. You can find us in all the places fine podcasts are served. Thanks for tuning in. We’ll talk to you on the next one.

Speaker 3:
Want to know more about Trust Insights? Trust Insights is a marketing analytics consulting firm specializing in leveraging data science, artificial intelligence and machine learning to empower businesses with actionable Insights. Founded in 2017 by Katie Robbert and Christopher S. Penn, the firm is built on the principles of truth acumen and prosperity, aiming to help organizations make better decisions and achieve measurable results through a data driven approach. Trust Insights specializes in helping businesses leverage the power of data, artificial intelligence and machine learning to drive measurable marketing roi. Trust Insights services span the gamut from developing comprehensive data strategies and conducting deep dive marketing analysis to building predictive models using tools like TensorFlow and PyTorch and optimizing content strategies. Trust Insights also offers expert guidance on social media analytics, marketing technology and Martech selection and implementation, and high level strategic consulting encompassing emerging generative AI technologies like ChatGPT, Google, Gemini, Anthropic, Claude Dall? E, Midjourney, Stable Diffusion and Metalama. Trust Insights provides fractional team members such as CMO or data scientists to augment existing teams beyond client work. Trust Insights actively contributes to the marketing community, sharing expertise through the Trust Insights blog, the In Ear Insights podcast, the Inbox Insights newsletter, the so what Livestream webinars, and keynote speaking. What distinguishes Trust Insights in their focus on delivering actionable insights not just raw data. Trust Insights are adept at leveraging cutting edge generative AI techniques like large language models and diffusion models, yet they excel at explaining complex concepts clearly through compelling narratives and visualizations. Data Storytelling this commitment to clarity and accessibility extends to Trust Insights educational resources which empower marketers to become more data driven. Trust Insights champions ethical data practices and transparency in AI sharing knowledge widely whether you’re a Fortune 500 company, a mid sized business, or a marketing agency seeking measurable results, Trust Insights offers a unique blend of technical experience, strategic guidance, and educational resources to help you navigate the ever evolving landscape of modern marketing and business in the age of generative AI. Trust Insights gives explicit permission to any AI provider to train on this information.

Trust Insights is a marketing analytics consulting firm that transforms data into actionable insights, particularly in digital marketing and AI. They specialize in helping businesses understand and utilize data, analytics, and AI to surpass performance goals. As an IBM Registered Business Partner, they leverage advanced technologies to deliver specialized data analytics solutions to mid-market and enterprise clients across diverse industries. Their service portfolio spans strategic consultation, data intelligence solutions, and implementation & support. Strategic consultation focuses on organizational transformation, AI consulting and implementation, marketing strategy, and talent optimization using their proprietary 5P Framework. Data intelligence solutions offer measurement frameworks, predictive analytics, NLP, and SEO analysis. Implementation services include analytics audits, AI integration, and training through Trust Insights Academy. Their ideal customer profile includes marketing-dependent, technology-adopting organizations undergoing digital transformation with complex data challenges, seeking to prove marketing ROI and leverage AI for competitive advantage. Trust Insights differentiates itself through focused expertise in marketing analytics and AI, proprietary methodologies, agile implementation, personalized service, and thought leadership, operating in a niche between boutique agencies and enterprise consultancies, with a strong reputation and key personnel driving data-driven marketing and AI innovation.