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TL;DR

For businesses wanting to turn raw data into usable insights with Generative AI, three key points are essential:

1. RAG Isn’t Plug-and-Play

   RAG (Retrieval-Augmented Generation) can help manage unstructured data, but it’s not a complete solution by itself. It requires custom technical setup and fine-tuning, especially in complex cases like multi-format data and domain-specific queries. Business leaders should understand RAG basics to ask the right questions and avoid overreliance on a single approach.

2. Use Real Data for Testing

   Testing RAG applications with real user questions is crucial. Artificially generated questions often don’t capture the nuances of real queries, which can lead to issues in production. Gathering real question-document pairs ensures that retrieval is effective and that generated answers are relevant.

3. Focus on User-Centric Problem Solving

   Break down user needs into smaller tasks to deliver quick, meaningful results. For example, automating repetitive questions in a customer service center can free up time and increase productivity. This user-focused approach helps maintain client interest and trust, paving the way for gradual improvements that address actual pain points.

"Is all the hype about transforming raw data into a ready-to-use knowledge database just fake? How can a business actually leverage that?" asked a friend of mine, who’s overseeing the optimization of his company’s customer service center and yes, he starts to be disappointed.

When he asked, I felt a bit overwhelmed—there were so many key points I wanted him to keep in mind before talking with his engineering team. So, I wrote it all down in an email, which eventually turned into this post.

To all business stakeholders, here are three essential points to keep in mind if you want to take full advantage of one of Generative AI’s most promising applications: using your data to answer questions effectively.

RAG alone isn’t enough—pay attention to the technical details

To address your problem, the engineering team plans to build a Retrieval-Augmented Generation (RAG) pipeline.

Over the past two years, RAG has gained significant traction in tech circles. The concept, hinted at by its full name, leverages retrieval methods to enhance the capabilities of language models. Without diving into technical details—there are numerous resources available on that—I’ll note that RAG is a natural starting point for handling large volumes of unstructured data and quickly finding specific answers.

To make this work, the engineering team will structure the data in a way that enables the application, powered by ...