Leverage Your Regulatory & Compliance Documents with Intelligent Processing

This table compares Retrieval-Augmented Generation (RAG) and Long Context (LC) Large Language Models (LLMs) across seven key factors:

1. Best Use Cases

• RAG is well-suited for dialogue-based question answering, fragmented or multi-source information retrieval, and real-time or domain-specific knowledge. It's effective in environments where information is distributed across various sources and needs to be pulled on demand.

• LC LLMs excel in Wikipedia-style factual Q&A, structured and self-contained documents like legal texts or books, and tasks requiring comprehensive document understanding. They are ideal for processing entire documents without retrieval steps.

2. Performance on QA Benchmarks

• RAG performs better on dialogue-based and general knowledge questions by dynamically retrieving relevant content during inference.

• LC LLMs outperform RAG on structured, knowledge-dense tasks such as answering factual questions from Wikipedia, thanks to their ability to interpret full documents at once.

3. Handling of Long Documents

• RAG only retrieves the most relevant chunks from long documents. This can be efficient but may result in incomplete context or loss of nuance depending on how content is chunked or retrieved.

• LC LLMs can read and reason over entire documents, enabling deeper understanding. However, very long inputs (even within the 128K token range) can trigger a “lost-in-the-middle” effect, where key content in the center is deprioritized.

4. Memory and Cost Efficiency

• RAG is more efficient in terms of memory usage and token consumption because it loads only small, relevant segments of text.

• LC LLMs, by contrast, have higher memory and compute requirements since the entire document is loaded into the model’s context window.

5. Relevance to Query

• In RAG, relevance depends on the retrieval strategy. The chunking method and how retrieved sections are ranked significantly impact output quality.

• In LC LLMs, the entire context is always available, which ensures better coverage — but very long contexts may dilute focus.

6. Accuracy and Completeness

• RAG is sensitive to the quality of the retrieved chunks. If important context is missed during retrieval, the model may provide incomplete or incorrect answers.

• LC LLMs tend to provide more consistent and complete answers, as they do not rely on retrieval mechanisms that could fail.

7. Challenges

• RAG systems must balance chunk size, retrieval depth, and ranking accuracy. Too many or poorly chosen chunks can reduce overall performance.

• LC LLMs face issues with forgetfulness when handling ultra-long inputs and often suffer from high latency and cost due to their resource-heavy nature.

This table compares five different chunking methods used in the context of processing and retrieving text data for use in large language models (LLMs). Each method is evaluated based on how it works, its advantages, and its limitations.

1. Fixed-Length Chunking

• How it works:
  Splits text into equally sized chunks based on token count (e.g., 512 or 1024 tokens).

• Pros:

  • Simple and fast

  • Works well with structured data like FAQs or database logs

  • Compatible with most LLM architectures

• Cons:

  • May split sentences awkwardly, losing meaning

  • Difficult to ensure retrieval of full thoughts or concepts

2. Semantic Segmentation

• How it works:
  Splits text using natural document boundaries such as headings, sections, or paragraphs.

• Pros:

  • Better at preserving contextual meaning

  • Maintains the logical structure of documents

  • Reduces the likelihood of retrieval errors

• Cons:

  • More computationally intensive

  • Requires natural language processing techniques for accurate segmentation

3. Clause & Obligation Detection

• How it works:
  Uses NLP to detect legal obligations, conditional statements, and compliance requirements in text.

• Pros:

  • Especially useful for legal and compliance documents

  • Ensures obligations are retrieved with full contextual clarity

• Cons:

  • Very domain-specific

  • Requires models trained on legal or regulatory corpora

4. Embedding-Based Chunking

• How it works:
  Groups semantically similar sentences based on meaning, not position, using embeddings (e.g., BERT).

• Pros:

  • Enhances retrieval relevance

  • Allows for dynamic chunking across diverse topics

• Cons:

  • Computationally expensive

  • Requires use of embedding models

5. Sliding Window Chunking

• How it works:
  Creates overlapping chunks to preserve context across boundaries, often used to maintain continuity in LLM input.

• Pros:

  • Reduces loss of context at chunk boundaries

  • Helps prevent hallucinations by maintaining input continuity

• Cons:

  • Introduces redundancy

  • Increases token usage and processing cost for LLMs

This table provides an overview of four common retrieval methods used in natural language processing and search applications. Each method is described alongside its advantages and disadvantages.

1. Cosine Similarity
Description: Measures the angular similarity between vector representations of text.
Pros: Fast and computationally efficient. Well-suited for semantic retrieval when embeddings are well-formed.
Cons: Depends heavily on high-quality embeddings. Performs poorly on out-of-vocabulary words or rare terms.

2. BM25 (Sparse Retrieval)
Description: A ranking function that scores documents based on exact word matches using term frequency and inverse document frequency.
Pros: Good for keyword-based searches. Simple and widely supported in search engines.
Cons: Ignores semantic meaning. Struggles with rephrased or synonym-based queries.

3. Dense Passage Retrieval (DPR)
Description: Uses deep learning-based embeddings to retrieve semantically relevant passages, often trained on QA data.
Pros: Captures context and semantics better than BM25. Handles synonyms and paraphrases well.
Cons: Computationally expensive. Requires large, labeled datasets for training.

4. Faiss-based Nearest Neighbour Search
Description: Applies Approximate Nearest Neighbour (ANN) search over dense embeddings using libraries like Faiss for large-scale retrieval.
Pros: Extremely fast at large scale. Reduces search time significantly.
Cons: Needs pre-indexing and GPU support. Lower accuracy compared to exact similarity.

What is the penalty for violating KYC?
RAG (Retrieval-Augmented Generation): Retrieves exact penalties from legal text.
Graph DB (Knowledge Graph): Finds structured penalties linked to specific obligations.
Long Context LLM: Reads the full document and summarizes key penalties.

Who must report suspicious transactions?
RAG (Retrieval-Augmented Generation): Lists obligated entities based on retrieved text.
Graph DB (Knowledge Graph): Connects reporting obligations to legal references and agencies.
Long Context LLM: Reads obligations and explains their significance.

Real estate money laundering risks?
RAG (Retrieval-Augmented Generation): Retrieves known risk factors from AML guides.
Graph DB (Knowledge Graph): Links risks to penalties, real-world cases, and regulatory bodies.
Long Context LLM: Generates a detailed summary of red flags.

^{[1] Laban, P., Fabbri, A. R., Xiong, C., & Wu, C. S. (2024). Summary of a haystack: A challenge to long-context LLMs and RAG systems. arXiv preprint arXiv:2407.01370.}

^{[2] Li, X., Cao, Y., Ma, Y., & Sun, A. (2024). Long Context vs. RAG for LLMs: An Evaluation and Revisits. arXiv preprint arXiv:2501.01880.}

^{[3] Hui, Y., Lu, Y., & Zhang, H. (2024). UDA: A benchmark suite for retrieval augmented generation in real-world document analysis. arXiv preprint arXiv:2406.15187.}

^{[4] Mandvikar, S. (2023). Augmenting intelligent document processing (IDP) workflows with contemporary large language models (LLMs). International Journal of Computer Trends and Technology, 71(10), 80-91.}