EP 544: AI Magic - Convert Outdated Content into Engagement Gold
74
Companies
54
Key Quotes
3
Topics
🎯 Summary
Podcast Summary: EP 544: AI Magic - Convert Outdated Content into Engagement Gold
This episode of The Everyday AI Show, hosted by Jordan Moulson, focuses on a powerful, yet under-discussed, application of generative AI: transforming and modernizing outdated internal or external content (like presentations and documents) into highly engaging, interactive assets. The host argues that many knowledge workers are using AI inefficiently, often performing manual steps even when using AI tools. This episode introduces a “live demo” workflow, part of a new weekly segment called “Put AI to Work Wednesdays,” showcasing how to leverage recent advancements, particularly within the Google ecosystem, to automate this repurposing process.
The core narrative arc follows the host as he attempts to update a nearly two-year-old presentation on “Small Language Models” into a modern, factually current, and interactive asset. The process involves chaining together several powerful AI capabilities to achieve what used to require significant manual document juggling and research.
Key Discussion Points & Workflow:
- Identifying the Problem: Knowledge workers waste hundreds of hours annually updating old documents. Simply using AI somewhere in the process doesn’t guarantee efficiency; many manual steps remain.
- The Goal: Convert an old, static PDF presentation into a new, interactive piece of content that is factually current, using AI to fill knowledge gaps.
- Step 1: Deep Research (Google Gemini): The host initiates a Deep Research task using Google Gemini (powered by Gemini 2.5 Pro). He highlights that this feature is significantly improved (post-April updates) and acts agentically, planning research, executing searches across multiple sources, reflecting on findings, and iteratively deepening the research until satisfied. The goal here is to generate a comprehensive, up-to-date research document (PDF) on the topic.
- Step 2: Content Transformation (Google AI Studio): The host moves to Google AI Studio, noting that while technically for developers, its UX is becoming increasingly beginner-friendly and it remains free (though API key usage might be the future).
- Chaining Inputs: Within AI Studio, the host uploads two key documents: the outdated presentation PDF and the new research PDF generated by Gemini Deep Research.
- AI Magic - Multi-Step Prompting: The core instruction set given to Gemini 2.5 Pro in AI Studio involves four critical actions:
- Transcription: Use computer vision (OCR) to verbatim transcribe the old presentation PDF, even if it’s composed of image screenshots.
- Gap Analysis: Analyze the old transcription against the new research document to identify missing or outdated information.
- Targeted Extension: Conduct additional targeted web research (grounded via Google Search toggle) specifically focusing on the most recent data (e.g., May/June 2025) to fill the identified gaps.
- Final Output: Update the presentation outline in its entirety based on the transcription, the initial research, and the new, targeted web findings, effectively creating a brand-new, current presentation outline.
- The Implication: This process turns static documents into dynamic software-like outputs, embedding live AI capabilities into the resulting content structure, making the final product interactive and far more engaging than a traditional slide deck.
Summary Analysis:
| Category | Insight |
|---|---|
| 1. Focus Area | Practical application of advanced LLMs (specifically Google Gemini 2.5 Pro) for content lifecycle management, document modernization, and interactive asset creation using Google AI Studio. |
| 2. Key Technical Insights | 1. Agentic Deep Research: Gemini’s research capability iteratively plans, executes, reflects, and deepens searches without extensive manual prompting. 2. Multimodal Input Processing: The ability of the model within AI Studio to accurately transcribe text from image-heavy PDFs using computer vision/OCR. 3. Chained Task Execution: Successfully instructing a single model instance to perform sequential, complex tasks: transcribe, analyze gaps, research externally, and synthesize a final updated document. |
| 3. Business/Investment Angle | 1. Productivity Leap: Significant time savings for knowledge workers involved in compliance updates, handbook revisions, or content refreshes. 2. Content Value Maximization: Outdated content is not discarded but resurrected and enhanced, maximizing ROI on past creation efforts. 3. Ecosystem Lock-in: The demonstration heavily favors the Google stack (Gemini, AI Studio), suggesting strong momentum for their developer and enterprise tools. |
| 4. Notable Companies/People | Jordan Moulson (Host, Everyday AI); Google Gemini (specifically 2.5 Pro and its Deep Research feature); Google AI Studio (the platform used for chaining inputs and outputs). |
| 5. Future Implications | The industry is moving toward AI-native content creation, where documents are not static files but dynamic interfaces capable of self-updating and interacting with the user or external data sources. The barrier to creating sophisticated, AI-enhanced applications from simple documents is rapidly dropping. |
| 6. Target Audience | Knowledge Workers, Content Strategists, Marketing Professionals, and AI Practitioners who need actionable, real-world workflows to improve efficiency and modernize existing corporate assets. |
🏢 Companies Mentioned
Canva
✅
ai_application
Microsoft
✅
big_tech
Grok
✅
ai_application
customgpt.ai
✅
ai_application
Google Docs
✅
big_tech
Cloud Next
✅
big_tech
Google I/O
✅
big_tech
LM Arena
✅
ai_research
Remember I
✅
unknown
Jordan Wilson
✅
unknown
Your Pocket
✅
unknown
Powerful Models Are Moving From
✅
unknown
How Efficient
✅
unknown
Specialized AI
✅
unknown
The Year
✅
unknown
💬 Key Insights
"Summarize Slide. Click this to get a concise AI-generated summary of the key points on the current slide. And then it says Deeper Dive. This button uses the slide's headline to ask the Gemini API for more detailed information."
"It essentially created an interactive presentation so it says Small Language Models in 2025: The Year of On-Device, Agentic, and Specialized AI."
"even this version of Gemini 2.5 Pro preview, the 605 version, it behaves much differently than the version that was released a month ago, the 506, and it's different. You're difficult, right? May 6th versus June 5th, it behaves differently."
"This PDF is a bunch of images, right? So it's literally going to use computer vision, go through and grab all of this text. A lot of this information... it's going to use computer vision to grab all this as well."
"it is actually first thinking about my very simple research prompt I gave it. And then it started to do a round of research. So it looks like it went to about 10 websites. And then after going to those 10 websites, it actually started to first reflect and think of the information that it found on those 10 websites first."
"So Google Gemini 2.5 is obviously a hybrid model. So when it needs to think and go very slowly and plan like a smart researcher would, it will do that. When it needs to just be fast, it'll do that as well."
📊 Topics
#artificialintelligence
126
#generativeai
11
#startup
1