Project Info
Client
Digital News Channel
Service
AI & RAG Application Development
Industry
Media & Journalism
Stack
Python, Embeddings, Vector DB, REST APIs, LLM
Challenge
The primary challenge was enabling users to retrieve accurate and contextual information from a continuously growing set of published news articles. Traditional keyword-based search was insufficient for understanding intent, context, and semantic meaning. Additionally, ensuring reliable accuracy scoring and maintaining fast response times across large datasets posed significant technical bottlenecks.
Our Solution
We implemented a Retrieval-Augmented Generation (RAG) based chatbot architecture using vector embeddings. Articles are processed through a Python-based API to generate embeddings, stored in a vector database, and queried dynamically to provide context-aware answers along with confidence scores indicating response accuracy.
Semantic search over thousands of articles- Context-aware answers using embeddings
- Python-based embedding ingestion pipelines
- Accuracy scoring with confidence levels
- Fast vector-based retrieval
- Scalable ingestion for new articles
- REST-based chatbot integration
Blockers & Bottlenecks
Handling large volumes of article embeddings efficiently
Ensuring relevant context retrieval for user queries
Managing response latency during peak usage
Maintaining accuracy and confidence scoring consistency
Solutions & Impact
Optimized embedding storage using vector indexing strategies
Fine-tuned retrieval logic for improved relevance
Asynchronous APIs for faster chatbot responses
Confidence scoring based on similarity thresholds
Conclusion
This RAG-based chatbot successfully transformed how users interact with news content by enabling intelligent, context-aware exploration of articles. The solution delivers high accuracy, scalability, and performance—making it a powerful tool for modern digital journalism platforms.
Intelligent Retrieval
Enabled semantic search across large article datasets, delivering context-rich and accurate responses.
Scalable AI Architecture
Designed to handle continuous article ingestion without impacting chatbot performance.
User Trust & Engagement
Confidence scores improved transparency and increased user trust in AI-generated responses.