LLM RAG

Retrieval Augmented Generation

Summary

Retrieval Augmented Generation (RAG) is a method used in Natural Language Processing (NLP) that combines the benefits of pre-trained language models with the ability to use external documents or databases for reference during the generation process. This allows the model to pull in relevant information from a large corpus of documents, augmenting its ability to generate more accurate and contextually relevant responses. It's particularly useful in question-answering tasks, where the model needs to pull in specific information to answer a question accurately.

Benefits

Retrieval Augmented Generation (RAG) offers several benefits:

  1. Contextual Relevance: RAG can pull in relevant information from a large corpus of documents, which can help generate more accurate and contextually relevant responses.

  2. Improved Accuracy: By leveraging external knowledge, RAG can improve the accuracy of responses, particularly in question-answering tasks.

  3. Scalability: RAG models can scale to large document collections, making them useful for tasks that require access to vast amounts of information.

  4. Flexibility: RAG allows for the combination of different retrieval and generation mechanisms, providing flexibility in designing models.

  5. Efficiency: By using a two-step process of retrieval and generation, RAG can be more efficient than traditional methods that require processing the entire document collection for each query.

  6. Better Generalization: RAG models can generalize better to unseen data as they can retrieve relevant information from the external documents at inference time.

Quote

Next-Gen AI Applications Pilot More Advanced RAG

RAG is king today, but that’s not to say that the approach is not without its problems. Many implementations today still utilize naive embedding and retrieval techniques, including token count-based chunking of documents and inefficient indexing and ranking algorithms. As a result, these architectures often suffer from problems like:

  • Context fragmentation. In many academic benchmarks, the correct answer is in one place in documentation, but this is almost never the case in production codebases
  • Hallucinations. LLMs degrade in performance and accuracy in multi-step reasoning tasks
  • Entity rarity. “Sparse retrieval” (e.g., word-matching algorithms) sometimes work better than “dense retrieval” based on embeddings in one- or zero-shot scenarios
  • Inefficient retrieval. High latency and costs
    To address these problems, next-generation architectures are exploring more advanced RAG applications, folding in novel
    techniques like chain-of-thought reasoning, tree-of-thought reasoning, reflexion, and rules-based retrieval.

Source: https://menlovc.com/perspective/the-modern-ai-stack-design-principles-for-the-future-of-enterprise-ai-architectures/?utm_source=substack&utm_medium=email

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