GenAI for Journalism
Generating Journalistic Content with AI / Case
Our client, focusing on providing sports content for media outlets, betting platforms, and news agencies, aimed to change how they generate tennis articles.
With a target audience keen on receiving real-time and personalized sports news, the company struggled with manual content creation processes that limited their ability to meet the demand for instant, customized articles across languages and formats. Recognizing the potential of AI-driven content generation, they partnered with AAI Labs to develop a LLM-based system designed specifically to streamline and automate tennis article generation.
Objectives
Automating the content generation for real-time tennis match updates and analyses.
Customizing content for various formats (singles, doubles, women’s, men’s, and age groups).
Ensuring data accuracy and content quality by basing articles on live sports statistics.
Scalability to expand coverage to similar sports, such as badminton or table tennis, in the future.
Challenges
Integration of live sports data for real-time and post-match updates.
Adaptability of content generation to different match formats and client specifications.
Ensuring uniqueness across generated articles to avoid plagiarism risks.
Minimizing disruptions to existing workflows while adopting AI-driven technology.
Solution
1. Automated article generation
Leveraging an LLM, the system generates tennis articles by analyzing real-time match data, player statistics, and match outcomes. This functionality supports various content types, including detailed match analyses, summaries, and event previews.
2. Flexible API integration
A RESTful JSON API built with Python and Flask enabled seamless integration into the client’s content management system. The API allows bulk and single article generation based on filters like date, tournament, player, and language, significantly simplifying content requests.
3. Multilingual support
Integrated with DeepL, the system provides accurate translations. This feature ensures a broader reach for audiences across different regions and supports the client's expansion goals.
4. User-centric interface
The model was integrated into a user-friendly interface that allows the client to customize the output format and style. This design empowers the operations team to adjust articles without requiring in-depth technical knowledge.
5. Future scalability
While initially focused on tennis, the prototype is scalable to other racket sports like badminton and padel. This feature aligns with the client’s long-term vision of diversifying sports content without substantial additional costs.
Implementation
Various LLM models, including newest GPT, LLaMA, and Falcon versions, were initially tested to identify the one best suited for real-time, sports-specific article generation. Following model selection, historical and real-time data were collected from APIs to train the model, equipping it to process large volumes of information and generate accurate, insightful content. The development process also incorporated regular client feedback loops, with monthly remote sessions conducted to present progress, test the prototype, and implement adjustments based on client input, ensuring the solution remained aligned with their evolving requirements.
Results
70% reduction in content production time: The system's automated nature enabled the operations team to produce articles instantly post-match, drastically reducing turnaround time.
Enhanced quality and consistency: By relying on structured, statistical data, the model generated articles with high accuracy, maintaining consistency across large volumes of content.
Cost savings: Automating content reduced the need for manual labor, allowing the company to scale its content offerings without proportional increases in staffing costs.
Increased reader engagement: With quicker article delivery and tailored content, user engagement rates improved, leading to higher page views and longer session durations on the client’s platforms.