We Built Our Own RAG Model that Improves Content Production by 200%

For the past two years, we have been using AI assistance, including AI agents, to streamline our content creation process. While these tools were helpful, none of them were customized to the degree we needed. Even with the use of AI, the process of creating articles and other written content remained complex and time-consuming.

Writing a detailed long-form article, which may seem like a simple task with AI, still took us between 3 to 4 hours. First, we had to create an outline, ensuring that we captured all the key points and had a clear structure.

This was followed by the research phase, which involved scouring the web for content gaps, gathering relevant data, and finding credible sources.

Once we had everything, we relied on AI assistance to write and curate the content. But even with these tools, the process was still slow and involved multiple stages of revisions.

Need for a More Efficient Process

While AI agents helped, they didn't fully meet our needs.

1. The content created with them often didn’t align with our desired format or tone.
2. Articles lacked the proper headings and subheadings, making them difficult to read.
3. Furthermore, the research phase still presented challenges, with AI sometimes producing inaccurate or irrelevant information—an issue we all know as “hallucination.”

Fixing these mistakes meant adding additional time to the overall process, and getting the output into the right format was a recurring struggle.

We realized that the content creation process could be much more efficient with the right tools and structure in place.

Building Our Own Retrieval-Augmented Generation (RAG) Model

To solve this, we decided to build our own Retrieval-Augmented Generation (RAG) model tailored specifically to our needs. The idea behind the RAG model was simple: augment traditional AI by pulling in relevant data from a custom-built repository.

This approach would ensure that the information used by the AI was both accurate and aligned with our business goals, reducing errors and minimizing hallucinations in the process.

Implementing the RAG Model for Streamlined Content Creation

We started by using our internal content repository, which primarily contains PDF files. These documents are packed with valuable information on topics relevant to our business, and the model leverages this knowledge base when generating content.

To further improve the efficiency of our workflow, we integrated Deepseek R1, which interacts with the repository and reads over 100,000 words worth of input files.

By incorporating these technologies, we were able to quickly retrieve information relevant to any query, reducing the time spent on manual research.

Speeding Up Content Production

The results were immediate and impressive.

What used to take hours to complete was now done in minutes. The RAG model generates the first draft in just 2 minutes.

Creating the first draft used to be the most time-consuming part of the entire process. We had to gather the right information, create an outline, and write the initial draft.

But now, with the RAG model in play, the model pulls in the relevant data, structures it according to our needs, and produces a complete first draft in no time. All that’s left is for us to make minor adjustments.

Self-Learning & Minimal Human Input - Maximum Results

But the improvements didn’t stop there. The second draft, which previously required extensive edits, now closely matches our expectations right from the start. In the past, the drafts often lacked proper organization, headings, and subheadings, and the tone wasn’t consistent with our brand voice.

On top of that, AI-generated research was sometimes off, requiring us to manually fix inaccuracies. With the RAG model, these issues were eliminated.

The model now pulls its information directly from our curated repository, ensuring the accuracy and relevance of the content. Furthermore, the model has learned to format the content according to our preferred structure, including headings and subheadings, which means we don’t have to remind it of our requirements every time.

Reduced Errors and Better Consistency with RAG Model

Thanks to the RAG model, we’ve drastically reduced errors in our content.

There’s now less than a 0.1% chance of hallucinations, meaning the information provided is almost always accurate.

Additionally, since the model works exclusively with our repository, it no longer requires the manual research we used to do. All the relevant data is already in place, and the model pulls it as needed, further improving efficiency.

We no longer need to remind the model of the content gaps or the formatting requirements because it already knows what we expect. As a result, the first draft now takes just 2 minutes, and the entire article—complete with all revisions—takes less than an hour to finish.

3X Content Production in Same Time

The shift in our content creation process has been transformational. What used to take several hours is now completed in less than an hour, from the first draft to the final version.

This newfound speed allows us to produce content at a much higher volume while maintaining the quality we’re known for. More importantly, we now have the ability to generate content quickly without sacrificing the accuracy and formatting that are crucial to our brand’s success.

Expanding the RAG Model's Capabilities

The RAG model has now become an essential part of our content creation strategy.

It helps us create a wide variety of content, including ads, press releases, thought leadership articles, case studies, white papers, emails, and newsletters.

It helps us create a wide variety of content, including ads, press releases, thought leadership articles, case studies, white papers, emails, and newsletters.

We’ve even started using it to generate tables, statistics, and bullet points to make our content more engaging and easier to read.

The model also helps us identify which topics have the most potential for driving traffic and generating leads by analyzing our current traffic and lead data.

This allows us to produce more targeted and effective content that aligns with our business objectives.

Future Improvements and Self-Learning Capabilities

Looking ahead, we plan to expand our repository to include up to 1 million words focused on specific topics.

This will further enhance the model’s contextual understanding and enable it to generate even more comprehensive content. Additionally, we are working on improving the tone of the output by providing better context and prompts to ensure that the content closely matches our brand voice.

One of the most exciting developments we’re working on is the introduction of a self-learning feature.

As we continue to use the RAG model, it will learn from the edits we make and adapt to improve the quality of future outputs.

Automating Content Creation + Publishing

The RAG model has improved our content creation process. What used to be a slow, manual, and error-prone workflow has now become a streamlined, efficient, and automated system.

The time saved is significant, and the accuracy and quality of our content have improved dramatically.

This saved time will be used in further monitoring and analysis of marketing activities - the true purpose of any AI-driven marketing team.

With this model in place, we’re able to scale our content production like never before, all while maintaining a high standard of quality. This improvement in AI-driven content creation is not only keeping us competitive in the industry but also allowing us to stay ahead of the curve in a rapidly evolving content marketing landscape.