Building Robust, Enterprise-Grade RAG Applications

Creating a robust, enterprise-grade Retrieval-Augmented Generation (RAG) application is significantly harder than generating a quick demo!

RAG has often been described as the killer use case for Generative AI, especially for Large Language Models (LLM) & Large Multi-Modal Models (LMM). While it’s fairly easy to put together a quick RAG demo, developing a production-ready, scalable RAG system is complex.

Let’s dive deeper into what goes into creating a robust RAG system.

🔼 RAG System Architecture: Key Phases

A robust RAG system consists of two primary layers:

  1. Ingestion Phase (Processing and indexing the knowledge base)
  2. Inference Phase (Real-time query processing, retrieval, and generation)

Each of these phases has several tunable parameters (knobs) that can be optimized, much like hyperparameter tuning in machine learning.

✅ Ingestion Phase: Processing & Indexing the Knowledge Base

The ingestion phase processes a document corpus for knowledge augmentation and indexes it for fast retrieval. This is typically a batch or non-real-time process.

🔹 Key Optimization Knobs in the Ingestion Phase

  • 📌 Data Cleansing: Ensure documents don’t have conflicting or outdated information. Multiple versions, incomplete text, or poorly formatted tables can confuse the LLM.
    Remember: Garbage in = Garbage out!
  • 📌 Chunking Strategy:
    • Should documents be chunked?
    • If yes, chunk size and overlap need to be optimized.
    • Should chunks be stored directly or summarized before indexing?
    • Techniques like Small-to-Big Chunks can be explored.
      (This topic alone deserves its own post!)
  • 📌 Metadata Storage: What extra metadata should be stored with each chunk?
  • 📌 Embedding Model Selection: Choosing the right embedding model for vectorizing chunks.
  • 📌 Vector Dimensions & Indexing Strategy:
    • Should you use 128 or 1536 dimensions for embeddings?
    • Which indexing algorithm to use?
      • HNSW, IVF, or other vector search methods?
    • How many indexes should be maintained?

✅ Inference Phase: Real-time Query Processing

The inference pipeline is composed of three sub-phases:

  1. Query Processing (Refining the user query)
  2. Retrieval (Fetching relevant knowledge)
  3. Generation (Producing the final AI-generated response)

📌 Query Processing Phase

Here, the user submits a query, and the system optimizes it for better retrieval.

  • 🔍 Query Rewriting: Compress, clarify, or reword the query using NLP techniques.
  • 🔍 Query Segmentation: Break down complex queries into subqueries for better accuracy.
  • 🔍 HyDE (Hypothetical Document Embedding): Generate a hypothetical answer, embed both the query and answer, and process retrieval based on similarity.

📌 Retrieval Phase

This is where the system retrieves relevant information from the indexed knowledge base before passing it to the LLM.

  • 🔍 Search Techniques:
    • Semantic Search (cosine similarity)
    • Keyword Search (BM25)
    • Hybrid Search + Result Fusion (Best of both worlds!)
  • 🔍 Complex Retrieval Strategies: Techniques like structured retrieval from LlamaIndex.
  • 🔍 Reranking of Retrieved Chunks:
    • Should another model be used to rank the retrieved chunks before passing them to the LLM?

📌 Generation Phase

Here, the LLM generates the final answer after augmentation with retrieved knowledge.

  • 🤖 Model Selection:
    • Which LLM should be used? Open-source or vendor-specific models?
  • 🤖 Base Model vs. Fine-tuned Model:
    • Should the model be fine-tuned for domain-specific use cases?
  • 🤖 Prompt Engineering:
    • Exploring different prompting techniques for better control over outputs.

🔗 Further Reading

RAG is a powerful architecture, but optimizing each phase is critical for building enterprise-grade applications. What are your thoughts on improving RAG systems? Let’s discuss! 🚀