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If there is one area where AI clearly demonstrates its value, it's knowledge management. Every organization, regardless of size, is inundated with vast amounts of documentation and meeting notes. These documents are often poorly organized, making it nearly impossible for any individual to read, digest, and stay on top of everything. However, with the power of large language models (LLMs), this problem is finally finding a solution. LLMs can read a variety of data and retrieve answers, revolutionizing how we manage knowledge.
This potential has sparked discussions about whether search engines like Google could be disrupted by LLMs, given that these models can provide hyper-personalized answers. We are already witnessing this shift, with many users turning to platforms like ChatGPT or Perplexity for their day-to-day questions. Moreover, specialized platforms focusing on corporate knowledge management are emerging. However, despite the growing enthusiasm, there remains a significant gap between what the world perceives AI is capable of today and its actual capabilities.
Over the past few months, I've explored building various AI-based tools for business use cases, discovering what works and what doesn't. Today, I'll share some of these insights on how to create a robust application that is both reliable and accurate.
For those unfamiliar, there are two common methods for giving large language models your private knowledge: fine-tuning or training your own model and retrieval-augmented generation (RAG).
This method involves embedding knowledge directly into the model's weights. While it allows for precise knowledge with fast inference, fine-tuning is complex and requires careful preparation of training data. This method is less common due to the specialized knowledge required.
The more widely used approach is to keep the model unchanged and insert knowledge into the prompt, a process some refer to as "in-context learning." In RAG, instead of directly answering user questions, the model retrieves relevant knowledge and documents from a private database, incorporating this information into the prompt to provide context.
While RAG might seem simple and easy to implement, creating a production-ready RAG application for business use cases is highly complex. Several challenges can arise:
Real-world data is often not just simple text; it can include images, diagrams, charts, and tables. Normal data parsers might extract incomplete or messy data, making it difficult for LLMs to process.
Even if you create a database from company knowledge, retrieving relevant information based on user questions can be complicated. Different types of data require different retrieval methods, and sometimes, the information retrieved might be insufficient or irrelevant.
Simple questions might require answers from multiple data sources, and complex queries might involve unstructured and structured data. Therefore, simple RAG implementations often fall short in handling real-world knowledge management use cases.
Thankfully, there are several tactics to mitigate these risks:
Real-world data is often messy, especially in formats like PDFs or PowerPoint files. Traditional parsers, like PyPDF, might extract data incorrectly. However, newer parsers like LlamaParser, developed by LlamaIndex, offer higher accuracy in extracting data and converting it into an LLM-friendly format. This is crucial for ensuring the AI can process and understand the data correctly.
When building a vector database, it's essential to break down documents into small chunks. However, finding the optimal chunk size is key. If it is too large, the model might lose context; if it is too small, it might miss critical information. Experimenting with different chunk sizes and evaluating the results can help determine the best size for different types of documents.
Reranking involves using a secondary model to ensure the most relevant chunks of data are presented to the model first, improving both accuracy and efficiency. Hybrid search, combining vector and keyword searches, can also provide more accurate results, especially in cases like e-commerce, where exact matches are critical.
This approach leverages agents' dynamic and reasoning abilities to optimize the RAG pipeline. For example, query translation can be used to modify user questions into more retrieval-friendly formats. Agents can also perform metadata filtering and routing to ensure only relevant data is searched, enhancing the accuracy of the results.
Creating a robust agentic RAG pipeline involves several steps:
First, retrieve the most relevant documents. Then, use the LLM to evaluate whether the documents are relevant to the question asked.
If the documents are relevant, generate an answer using the LLM.
If the documents are not relevant, perform a web search to find additional information.
After generating an answer, check if the answer is grounded in the retrieved documents. If not, the system can either regenerate the answer or perform additional searches.
Using tools like LangGraph and Llama3, you can define the workflow, setting up nodes and edges that determine the flow of information and the checks performed at each stage.
As you can see, building a reliable and accurate RAG pipeline involves balancing various factors, from data parsing and chunk sizing to reranking and hybrid search techniques. While these processes can slow down the response time, they significantly improve the accuracy and relevance of the answers provided by the AI. I encourage you to explore these methods in your projects and share your experiences. As AI continues to evolve, the ability to effectively manage and retrieve knowledge will become increasingly critical.
