Retrieval-Augmented Generation (RAG) is a robust architecture in artificial intelligence that enhances Large Language Models (LLMs) by integrating external information retrieval mechanisms. This approach is particularly beneficial when an AI system needs to provide responses grounded in specific datasets or documents, ensuring both generative flexibility and factual accuracy. However, implementing RAG in a production environment requires careful consideration of its strengths and limitations.
This blog delves into technical experience with RAG, focusing specifically on the two primary approaches: Vector-Based Information Retrieval and Chunking Techniques, evaluating their effectiveness and understanding the reasons behind their limitations.
Before we dive into detailed use cases, if you're interested in learning more about RAG or need a refresher on how it works, check out our previous post RAG Agents: the future of AI? A Deep Dive into Retrieval-Augmented Generation
Objective: High-Precision Information Extraction from PDFs
Our objective was to develop a system capable of parsing unstructured PDF documents and extracting exact information efficiently. These documents featured un-defined layouts, including tabular data, key-value pairs, and inconsistent formatting with each PDF having its own structure. The core challenge was to avoid generative errors by providing accurate responses without hallucination.
Methodologies Implemented and Observed Outcomes
1. Vector-Based Information Retrieval
Approach: We implemented embedding techniques to convert the textual content of PDFs into vector representations. These vectors were stored in a specialized database designed for semantic search. The retrieval process involved matching the query vector with document vectors to find the most relevant information.
Challenges Faced: While this approach provided good semantic matching, it struggled with scenarios requiring precise data retrieval. The method often returned results that were contextually similar but not specifically accurate to the query. For instance, when a question demanded an exact value or a specific field from a table, the retriever occasionally returned loosely associated data.
Outcome: The generative model, when fed with broad or partially relevant information, produced responses that lacked precision. This led to inaccuracies, especially in unstructured data scenarios where exact matches were critical.
What We Learned: Vector-based retrieval is highly effective for open-ended or semantic queries but requires fine-tuning and strict filtering to handle unstructured document extraction tasks accurately.
2. Chunking Techniques
Approach: To improve the specificity of retrieval, we tested different chunking strategies. These included dividing the document into smaller sections such as sentences, paragraphs, sliding window chunks and LLM-based chunks. The goal was to create more granular pieces of information for the retriever to search through.
Challenges Faced: Finer chunking improved the chances of retrieving relevant data, but it also introduced complexity in maintaining the broader document context.
For example, when a query needed an answer from a specific section of a document, the retrieval model sometimes isolated useful information but failed to preserve the necessary context.
Outcome: Smaller chunks led to better retrieval specificity, but the generative model occasionally struggled to reconstruct the full context of the document. This fragmentation resulted in incomplete or overly narrow responses that did not fully address the query.
What We Learned: Chunking is a powerful technique for enhancing retrieval accuracy, but there is a need to balance chunk size with the ability to maintain meaningful context. Over-chunking can lead to loss of contextual integrity, while under-chunking might reduce retrieval precision.
Conclusion
Our focused analysis on Vector-Based Information Retrieval and Granular Chunking Techniques highlighted key insights into their performance within a RAG framework. While both approaches offer significant advantages, they also present specific challenges when applied to extracting exact information from unstructured PDFs. RAG is particularly useful when the AI system needs to answer open-ended questions or when the dataset is too large to fine-tune directly into a model. However, for scenarios requiring precise information extraction, combining RAG with other methodologies or opting for a different approach altogether might yield better results. The decision to use RAG should be guided by the specific use case—whether the goal is to enhance contextual understanding, provide dynamic responses, or maintain accuracy with structured data.
For open-ended questions or dynamic data, RAG offers a significant advantage by combining generative capabilities with real-time information retrieval. However, when the objective is to extract exact values or structured information from well-defined documents, a hybrid approach that blends RAG with rule-based extraction methods or a shift towards a more deterministic model might be more effective.
Have you had similar experiences when it comes to increasing the data or knowledge base of a system? We would be happy to discuss this with you.