A Systematic Approach to AI Content: A 100-Article-Per-Day Pipeline
The foundation for creating a content pipeline is high-quality source material. In this approach, videos are used as the information source, as they contain a dense and structured stream of data, perfectly suited for further processing by artificial intelligence.
Stage 1: Collecting and Preparing Source Video Materials
The foundation for creating a content pipeline is high-quality source material. In this approach, videos are used as the information source, as they contain a dense and structured stream of data, perfectly suited for further processing by artificial intelligence.
The process begins with the automated collection of relevant video files. A system is developed to find and download materials based on specified criteria, creating a database for subsequent work. The efficiency of the entire pipeline directly depends on the quality and relevance of the data collected at this stage.
Stage 2: Batch Processing in the “Content Zavod” System
| Process | Description |
|---|---|
| Transcription | Converting the audio track from the video into text format. |
| Structuring | Analyzing the text, identifying main topics, subtopics, and logical blocks. |
| Generation | Creating a coherent article text based on the structured data. |
After collection, the videos are sent to the core of the system, conventionally named “Content Zavod.” Here, they undergo batch processing. This means the system processes files not one by one, but in large groups, which significantly increases productivity.
Inside “Content Zavod,” videos go through several transformation stages. First, the audio track is extracted and then transcribed into text. After that, the AI analyzes the resulting text, structures it, identifies key points, and generates a draft of the article based on them.
Stage 3: Scaling with Parallel Workers
To achieve the stated productivity of 100 articles per day, sequential processing is not enough. The key element for scaling is the use of parallel workers. A worker is an independent process that performs the full processing cycle for a single video.
By running dozens of such workers simultaneously, the system can process a large number of videos in parallel rather than one after another. This allows for an exponential increase in content production speed without sacrificing quality on any individual element.
- High productivity: the ability to work on dozens of articles simultaneously.
- Scalability: easy to increase the number of workers to boost performance.
- Fault tolerance: the failure of one worker does not stop the entire system.
Stage 4: Automated Publication and Distribution
Creating the text is only part of the job. For the pipeline to be fully autonomous, the publication process must also be automated. Completed articles that have passed quality control are automatically sent to content management systems (CMS), such as websites or blogs.
The system doesn't just upload the text; it also performs a series of related tasks. It can independently select relevant tags, determine the category, add metadata for SEO, and even schedule publication for a specific time. This frees humans from routine operations and ensures a steady stream of new content.
Stage 5: Multi-Level Quality Control
High production speed should not come at the expense of quality. Therefore, a mandatory control stage is included in the systematic approach. It consists of two key levels: automated and manual.
Automated systems check each article for uniqueness, grammatical and stylistic errors, and compliance with formal requirements (e.g., text volume or structure). After the automated check, the material is sent for a final proofread by a human editor. The editor spends only a few minutes to ensure the logic and adequacy of the generated text, making minimal edits if necessary. This hybrid approach allows for maintaining a high standard of quality during mass production.
