Retrieval Augmented Generation

Analysis of the best-practices in leveraging Retrieval-Augmented Generation (RAG) for Large Language Models to build generative AI applications.

Context/Background:

Large Language Models (LLMs) like ChatGPT, despite their impressive capabilities, face limitations due to reliance on static training data. This leads to challenges such as:

  • Subpar Performance in Specialized Areas: Difficulty in handling domain-specific tasks due to the general nature of their training data.
  • Inability to Stay Updated: Struggle to incorporate new information and knowledge that emerges after their training is complete.
  • Hallucinations: Tendency to generate plausible but factually incorrect or misleading responses.

RAG emerges as a solution to these problems by integrating information retrieval techniques with LLMs, allowing them to dynamically access and utilize external knowledge sources during the generation process.

Problems/Challenges Solved by RAG:

  • Static Knowledge Limitation: RAG enables LLMs to overcome the limitations of fixed training data by providing access to real-time, updated information.
  • Hallucination Reduction: By grounding generation in retrieved factual data, RAG mitigates the issue of generating incorrect or misleading responses.
  • Domain Specialization Improvement: RAG facilitates better performance in specialized domains by dynamically incorporating relevant domain-specific knowledge.

Forces, Considerations, and Trade-offs:

  • Retrieval Efficiency: Balancing the comprehensiveness and accuracy of retrieval with the computational cost and speed of the process.
  • Information Quality: Ensuring the reliability and relevance of retrieved information, addressing issues like misinformation and bias.
  • Generation Creativity: Striking a balance between faithfulness to retrieved information and the ability to generate creative and insightful text that goes beyond the retrieved data.
  • System Complexity: Managing the growing complexity of RAG systems as they evolve to include multi-hop retrieval and iterative refinement processes.

Solutions and Approaches:

The RAG framework can be broken down into four key phases, each with distinct approaches:

  • Pre-retrieval:
    • Indexing: Organizing external data for efficient retrieval using methods like FAISS, HNSW, or kNN-LMs.
    • Query Manipulation: Refining user queries to better align with indexed data through reformulation, expansion, or normalization.
    • Data Modification: Enhancing the quality and relevance of data through techniques like entity linking and data enrichment.
  • Retrieval:
    • Search & Ranking: Identifying and prioritizing relevant documents using methods like BM25 or dense retrieval models like DPR.
  • Post-retrieval:
    • Re-ranking: Refining the initial document ranking based on additional criteria, often employing cross-attention models or knowledge distillation.
    • Filtering: Removing irrelevant or low-quality documents from the retrieved set using thresholding or self-reflection mechanisms.
  • Generation:
    • Enhancing: Integrating retrieved information with the original query and potentially adding further details or context to create a coherent response.
    • Customization (Optional): Adapting the generated text to specific user needs or preferences using frameworks like PKG or SURGE.

Solution Details:

  1. Data Preparation: Prepare and organize external data sources for indexing and efficient retrieval.
  2. Query Processing: Analyze and potentially modify the user query to improve retrieval accuracy.
  3. Initial Retrieval: Use a retrieval model to identify an initial set of potentially relevant documents.
  4. Re-ranking: Refine the initial document ranking using more sophisticated models and criteria.
  5. Filtering: Remove irrelevant or low-quality documents from the retrieved set.
  6. Information Extraction: Extract key information and insights from the remaining relevant documents.
  7. Information Fusion: Combine the extracted information with the original query and potentially add further context or details.
  8. Content Generation: Use an LLM to generate text based on the fused information and query.
  9. Customization (Optional): Tailor the generated text to specific user preferences or requirements as needed.
  10. Evaluation: Assess the quality and relevance of the generated text using appropriate metrics and human evaluation.

Resulting Consequences:

  • Improved LLM Accuracy and Reliability: RAG empowers LLMs with access to real-time information, reducing hallucinations and enhancing the factual accuracy of generated content.
  • Enhanced Domain Knowledge: RAG allows LLMs to perform better in specialized areas by incorporating relevant domain-specific knowledge on demand.
  • Dynamic Knowledge Updates: By leveraging the ever-growing pool of external information, LLMs can stay up-to-date without the need for retraining.