AI Research for Podcasters and Journalists: Why the First Step Isn’t Asking AI
Stop Letting AI Guess: The Right Way to Research Guests and Sources
I keep seeing people throw complicated questions into AI models in the simplest way possible and expect serious, definitive answers. The absurd part is that AI often responds with absolute confidence — until you push for details, sources, or clarification. Then the same model that spoke like an authority says “sorry,” “you’re right,” or admits the information was wrong.
That’s why feeding the right information and asking detailed, nuanced questions is more important than ever. Before AI, you would browse Google, jump from one source to another, and spend hours piecing things together. Now, it feels easier — just type the question and get the answer. But that ease is dangerous if you don’t control what the AI sees.
I’m not saying it’s bad. I’m saying use it the right way.
Stage 1: Build the dataset yourself
If you want accurate results, you can’t start by asking AI to “tell you everything about” someone. You have no control over what sources it uses or what it leaves out. The first step is to collect the relevant data yourself.
You want the full public footprint:
Interviews, podcasts, and panel appearances.
Articles they’ve written or been quoted in.
Videos, transcripts, and any long-form content where they’ve spoken.
With tools like Google Search Scraper, you can run targeted queries to gather exactly this. Combine their name with context keywords, event names, or site-specific searches. Then, run YouTube Transcript Scraper for video content and Article Scraper for text.
When you’ve done this, you’re holding a verified dataset — complete, relevant, and under your control.
Stage 2: Ask AI with precision
Once you’ve built your dataset, bring it into AI for the analysis stage. Use AI Prompt Automation to:
Summarize appearances.
Identify recurring topics and talking points.
Flag changes in stance or contradictions.
Note topics they consistently avoid.
Because you control the input, you can trust the output.
Why this matters now more than ever
Skipping the data collection stage is handing over control of your research to a black box. You won’t know what was missed or why certain details are wrong. This is where most people fail — they think the output is the truth, not realizing the AI might have never “seen” half of what matters.
By curating the sources yourself first, you’re combining AI’s speed with your own judgment. That’s where the real advantage comes from.
If you want the benefits without setting it up, our Concierge Service can handle the entire process for you. You give us the name, we deliver the dataset and full analysis. No missing pieces.
Garbage In, Garbage Out: Quick Checklist for AI Research
Before running AI analysis, ask yourself:
Do I have a complete dataset? Did I collect content from all major formats — video, audio, text?
Is it relevant? Does every piece of data relate directly to the person or topic?
Is it recent? Have I included the latest appearances and updates?
Is it verified? Do I know the sources are authentic and not opinion dressed as fact?
Am I asking the right question? Is my prompt specific, clear, and based on solid data?
Follow this, and AI becomes a force multiplier — not a confident liar.