This post is my submission for DEV Education Track: Build Apps with Google AI Studio.
What I Built
I built a comprehensive web application called "Useless Facts" that demonstrates the power of Google AI Studio through intelligent content generation and topic extraction. The app uses Google Gemini 2.0 Flash Lite to generate quirky, useless facts from real-time news articles and extract meaningful topics using Named Entity Recognition (NER).
Key AI Prompts Used:
- Fact Generation: A sophisticated chain-of-thought prompt that instructs the AI to identify tangential topics and create surprising facts
- Topic Extraction: NER prompts that extract entities like organizations, people, locations, and scientific terms from news articles
Google AI Studio Features Utilized:
- Gemini 2.0 Flash Lite for both text generation and embeddings
- Structured Output with Zod schemas for reliable JSON responses
- Text Embeddings for semantic search and topic matching (Turso, SQLite with embedding support)
- Automated Data Pipeline with daily RSS feed processing and AI-powered topic extraction
Demo
Live Application: useless-app-nu.vercel.app
Key Features Showcasing Google AI Studio:
1. Real-Time Fact Generation
- Users can generate AI-powered facts from current news articles
- Facts are generated using a sophisticated prompt that finds tangential, surprising information
- Example: From a tech article about a new quantum chip, the AI might generate: "The silicon used in computer chips must be 99.9999999% pure, a standard known as 'nine-nines' purity."
2. Intelligent Topic Extraction
- Uses NER to extract meaningful topics from news articles
- Categorizes entities into types: TECH, ORG, PERSON, LOCATION, CONCEPT, EVENT
- Implements TF-IDF scoring to rank topic relevance
3. Smart Topic Selection
- Users can choose from trending topics before generating facts
- Visual interface with color-coded topic badges
- Multi-select functionality for personalized fact generation
4. Automated News Processing Pipeline
- Daily RSS Ingestion: Automated cron job fetches from 10+ trusted sources (BBC, Science Daily, NASA, TechCrunch, Atlas Obscura, etc.)
- AI-Powered Topic Extraction: Each article is processed through Gemini NER to extract meaningful entities
- TF-IDF Scoring: Topics are ranked by relevance and frequency across all articles
- Real-Time Updates: New articles and topics become available immediately for fact generation
My Experience
Working with Google AI Studio was incredibly rewarding and revealed several key insights about building production-ready AI applications:
What I Learned
1. Prompt Engineering is an Art and Science
The most surprising discovery was how much the quality of AI output depends on prompt structure. My initial prompts were too simple, but implementing a chain-of-thought approach with step-by-step reasoning dramatically improved fact quality. The structured approach of "identify main subject → brainstorm tangential topics → select best fact" made the AI much more reliable.
2. Structured Output is Game-Changing
Using Zod schemas with generateObject() instead of raw text generation eliminated parsing errors and made the application much more robust. The AI consistently returns properly formatted JSON, which is crucial for production applications.
3. Embeddings Enable Smart Search
Using Gemini's text-embedding-004 model for semantic search allowed me to build intelligent topic matching. The app can now find articles related to user-selected topics even when exact text matches aren't available.
4. Automated Data Processing is Game-Changing
Building a complete RSS ingestion pipeline with AI-powered topic extraction was surprisingly straightforward with Google AI Studio. The combination of automated cron jobs, structured data processing, and real-time AI analysis created a self-sustaining content generation system that requires minimal maintenance.
What Was Surprising
1. The Power of Confidence Filtering
I was surprised by how important confidence scoring became. Filtering NER results to only include entities with >30% confidence dramatically improved topic quality while reducing noise.
2. Context Length Matters
Initially, I was sending full articles to the AI, but limiting content to 1500 characters actually improved both performance and fact quality. The AI works better with focused, relevant content.
3. Error Handling is Critical
A recent AWS global outage caused a major service disruption. This experience underscored the importance of robust error handling, reliable fallback mechanisms, and retry logic to ensure application resilience in production environments.
4. The RSS Pipeline Creates Endless Content
The automated news ingestion system processes 50+ RSS feeds daily, creating a constantly updating database of articles and topics. This means the app never runs out of fresh content for fact generation, and users always have access to the latest trending topics.
Top comments (2)
This is really cool work! I love how you combined real-time news, AI fact generation, and smart topic extraction into one seamless app. How did you decide on the confidence threshold for filtering NER results?
I think there's something wrong with the NER topic extraction and TF-IDF statistical weight score for each topic, "AI" topic is the top1, TF-IDF supposed to give lower weight for topic "AI". or maybe I sort it wrong when I put all the articles and topics together.