Since the past few months, I have been trying to understand how artificial intelligence will reshape the field of knowledge management. Among the many emerging opportunities, one that stands out is Retrieval Augmented Generation (RAG)—an approach to seamlessly integrate enterprise data into LLMs such as GPT, Claude, Gemini to name a few.
Retrieval Augmented Generation (RAG) is rapidly gaining traction as a practical and economical solution for organizations looking to bridge the gap between their proprietary knowledge and the pre-trained LLMs. My intent was to understand what RAG is, how it works, and what knowledge managers can do to prepare enterprise data for this transformative technology. I have been reading about RAG, talking to experts, attending workshops to learn more about this technique. This draft encapsulates my understanding and offers reflections, viewed primarily through my KM lens.
What is Retrieval Augmented Generation (RAG)?
Large Language Models (LLMs) are pre-trained on extensive datasets, but the specifics of this training data often remain unclear. As adoption of these models grows, the conversation has shifted towards how organizations can integrate their own data into these models to extract similar benefits of generative AI tailored to their unique needs.
Two main approaches were identified: Fine-tuning and RAG.
- Fine-tuning requires retraining the LLM with additional data, but it is resource-intensive, costly, and often unnecessary unless the use case demands custom behavior. Example Google’s Medpalm is a fine-tuned model trained on extensive medical datasets, images and textbooks. Mostly such models are meant for a specific industry/sector.
- Retrieval-Augmented Generation (RAG) dynamically connects LLMs to enterprise data by retrieving specific chunks of content relevant to a query from a (vector) database. This allows the LLM to process the data in real time and generate accurate responses. By leveraging this approach, organizations can reduce costs and enhance flexibility. RAG empowers LLMs to answer questions using external data sources, even if that data was not part of the model’s original training. For enterprises focused on integrating their content rather than creating a specialized model, RAG-enhanced GenAI applications provide a practical and scalable solution.
It’s no surprise that Retrieval Augmented Generation (RAG) has gained significant popularity in recent times. RAG has several use cases, such as improving intelligent search and retrieval, content summarization, data tagging and classification etc. A great example is chatbots used for customer support, where RAG helps retrieve the right information at the right time. Additionally, within data repositories, much of the content remains “dark,” waiting to be discovered. With RAG, these overlooked pieces of content have a much higher chance of being retrieved and put to use, which is a significant advantage. The technique also helps you track the source of the response. This is called grounding, and this grounded approach reduces hallucinations by providing verifiable sources and fosters trust in the AI’s outputs.
How Does Retrieval Augmented Generation (RAG) Work?
Let’s think of RAG as a layer in the architecture of a language model. This layer introduces a retrieval mechanism on top of the standard model, enabling it to fetch relevant information from external data sources (such as a vector database) in real-time. This enhances the model’s ability to generate accurate responses based on the most up-to-date or specific data available.
In this system, the vector database is where your enterprise data is stored as vectors. Each piece of content is broken into many smaller chunks, vectorized, and stored in the database. During this process, metadata and classifications are also added to each chunk to preserve context and ensure that the model can understand the content in a meaningful way.
When a user submits a query, the LLM:
- Searches for the most relevant chunks in the vector database
- Retrieves these chunks and ranks them based on relevance
- Examines and re-ranks the top results to ensure the most accurate and contextually appropriate information is prioritized
- Generates a response by synthesizing the retrieved information and combining it with its own language capabilities
This method significantly improves the model’s performance by ensuring it uses relevant, real-time data, making the answers more accurate and contextually relevant.
A Knowledge Manager’s Perspective
As Knowledge Managers, we all recognize the importance of good metadata in maintaining a healthy knowledge repository. The search experience is greatly improved when we apply quality metadata and regularly review content tags. Organized content makes it easier for machines to process and extract relevant information, which is crucial for systems like RAG.
I believe that incorporating a pre-processing phase before data ingestion can significantly boost the effectiveness of RAG-based systems. As Knowledge Managers, we play a pivotal role in preparing and organizing data. Clean, structured, and well-labelled data ensures that machines can process it more efficiently, ultimately leading to higher-quality outputs.
Drawing on my learnings, I would like to share some thoughts on how we can optimize data for RAG-based systems. While I am not an AI/RAG expert by any means, I believe these ideas could trigger some food for thought. I’d love to hear any feedback or suggestions from others in the community.
1. Focus on Quality Content
The adage “garbage in, garbage out” resonates more strongly today than ever before. AI systems rely on accurate, relevant, and well-structured data to deliver meaningful insights. As Knowledge Managers, our focus should be on curating high-quality content that not only adds value but also drives organizational efficiency.
A key aspect of this process is identifying What good content looks like your organization. Engaging with business leaders can be instrumental in shaping this definition. Their strategic insights can guide us in determining the types of content to prioritize, enabling us to curate resources that align with organizational goals and enhance decision-making.
2. Implementing a Pre-processing Phase
Before integrating data into the RAG pipeline, it might be a good idea to enhance its quality through a thorough pre-processing phase. This can be achieved by:
– Adding meaningful metadata: By reviewing and adding unique titles, descriptions, keywords, and business tags to enrich the content and make it searchable.
– Balancing metadata precision and breadth: Using both broad terms based on the content/idea for wider retrieval and specific tags to target precise results effectively.
– Reviewing machine-generated metadata: Ensuring accuracy and relevance by validating automated metadata for contextual appropriateness. I am not sure if this is possible but would help.
The bottom line is clear: well-crafted metadata significantly boosts retrieval accuracy and relevance. A proactive investment in refining metadata and keywords should pay dividends by improving the overall performance of RAG-based systems.
3. Tagging content as Best-in-Class to get better outputs.
Differentiating high-value content from general information can significantly enhance the quality of results generated by RAG systems. By implementing a mechanism that prioritizes authoritative or critical content when processing a query, the output can be made more relevant and impactful. However, at this point, this is a thought, and we will need to check with the experts, if this is possible. I would love to know.
To achieve this, we can collaborate with business leaders to define clear criteria for what constitutes “best-in-class” content. These criteria might include factors like alignment with strategic priorities, proven performance metrics, or endorsement by subject-matter experts. Once defined, tagging content accordingly will ensure that the RAG system prioritizes high-value information, leading to more accurate and meaningful outputs.
4. Categorize Content for Relevance
Not all data holds equal importance. Example, when I read a piece of content, I would implement various reading strategies- skip, skim, extensive reading to extract the meaning out of the piece. So, the effort required stands divided and that saves me time and effort. Talking about RAG, I wonder if the system can adopt a similar strategy as well. While chunking, can it categorize content into tiers (e.g., most relevant, somewhat relevant). Accordingly, it adds the required tags, retrieves the relevant chunk and processes the output. This kind of a segregation can help the system focus on high-impact data while treating less critical information as supplementary. This approach might lower the latency rate, improve accuracy and save time and cost as well. Would love to know what the RAG experts have to say on this. Is an approach like this even possible?
In summary, while RAG (Retrieval-Augmented Generation) holds immense promise, it is an evolving technique. Significant efforts are underway to enhance retrieval quality, address challenges like managing unstructured data, ensure consistent tagging, and maintain robust data security. As knowledge managers, this presents an exciting opportunity to deepen our understanding of RAG, given that the foundation of any Gen AI tool lies in how effectively data is managed. With our expertise in data governance, search and handling both tacit and explicit knowledge, we are uniquely positioned to contribute to the successful integration of RAG into enterprises. With thoughtful planning and implementation, RAG has the potential to revolutionize how organizations access, share, and utilize knowledge, paving the way for a future where AI becomes a true partner in decision-making.
There is so much to explore, and this is just a starting point. Would love to hear your thoughts on this topic. We can all learn together from our experiences, and I look forward to hearing from you.
About the Author:
Angshumala Sarmah is a seasoned Knowledge Management professional with over 18 years of experience in Knowledge Management. She specializes in fostering knowledge-sharing cultures, utilizing data-driven metrics, and leading cross-functional teams to achieve impactful results. Her expertise includes content strategy, data governance, and process automation, with a focus on M365 tools, Power Automate, and Generative AI. Recently, she developed a Gen AI-powered app that streamlines case study and credential drafting, successfully rolled out across the firm she works with.
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