This article was originally published on IBM Developer by Shabna MT,
Rakesh Polepeddi,
Gourab Sarkar
Retrieval-augmented generation (RAG) is an AI framework for improving the quality of LLM-generated responses by grounding the model on external sources of knowledge to supplement the LLM's internal representation of information.
Retrieval augmented generation works in two steps:
Retrieval step: When presented with a question or prompt, the RAG process retrieves a set of relevant documents or passages from a large corpus of documents.
Generation step: The relevant passages that are retrieved are fed into a large language model along with the original query to generate a response.
For example, if the prompt was, “Can I provision a floating IP if I don't have a public gateway?” then the retrieval step would find documentation related to floating IPs and public gateways, and send them to the model along with the query. That way, the model can focus on understanding the question better and create a more relevant response.
In enterprise environments, RAG systems typically rely on external knowledge sources like product search engines or vector databases. When using vector databases, the process can be further split into the following tasks:
- Content segmentation. Breaking down large text documents into smaller, manageable chunks.
- Vectorization. Transforming these segments into numerical representations (vectors) suitable for machine learning algorithms.
- Vector database indexing. Storing these vectors in a specialized database optimized for similarity search.
- Retrieval and prompting. When generating responses, the system retrieves the most relevant segments from the vector database and uses them to construct prompts for the language model.
In this article, we explore various optimization techniques and discover practical strategies for taking your RAG implementations to the next level in terms of performance and impact.
Advanced RAG introduces a range of optimizations, both before and after the retrieval process, to enhance accuracy, efficiency, and relevance. Pre-retrieval optimizations involve techniques such as data preprocessing to reduce noise, efficient chunking strategies to maintain context, and advanced search capabilities like denseor hybrid retrieval. Post-retrieval optimizations involve techniques, such as re-ranking the retrieval results, enhancing contextual relevance, and using and crafting effective prompts. Let's consider the transformations to make data AI-ready for efficient vectorization and retrieval.
Data preprocessing for AI-ready data
To prepare data for efficient use with AI, specifically for efficient vectorization and retrieval, there's no single best method. The ideal approach depends on the data type and file format. Often, combining techniques yields the optimal outcome. Therefore, selecting the right tools is crucial, considering the data's nature, the AI application's use case, and the required retrieval methods.
Let's consider the following file formats: PDF (.pdf), Microsoft Word (.doc and .docx), Microsoft PowerPoint (.ppt and .pptx), Markdown (.md), and plain text (.txt).
The top four challenges for the above-mentioned file types include:
- Inconsistent formatting. Files might have inconsistent line breaks, spaces, and tabs, which need to be normalized.
- Unstructured data. Files might contain unstructured data, requiring additional steps to extract meaningful information.
- Non-standardized content. Documents might have tables and different section layouts that need to be processed correctly.
- Document noise. Document noise refers to any irrelevant or extraneous information in a document, such as extra spaces, special characters, headers, footers, or formatting issues, that can hinder data processing and analysis.
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