The Real Reason Why RAG Fails

Why Do We Only Get "Somewhat Correct" Answers?

Many companies have implemented RAG (Retrieval-Augmented Generation), but we often hear that they're not achieving the expected accuracy. The reasons why RAG systems return "somewhat correct but not accurate" answers can be mainly attributed to the following three factors:

1. Inadequate Embedding Models

General-purpose embedding models cannot properly understand industry-specific terminology and context. For example, "derivative" in finance and "derivative" in chemistry have completely different meanings, but they're the same word in English.

2. Lack of Index Strategy

Simple vector search alone cannot retrieve appropriate documents for complex queries. A hybrid search combining keyword search and semantic search is necessary.

3. Insufficient Grounding

Simply passing retrieved information to the LLM causes context loss or important information to be buried. Proper pre-processing and post-processing are essential.

Solution: A Trinity Approach

Embedding Optimization

• •Domain-Specific Embeddings: Use embedding models trained on industry-specific terms and context
• •Fine-tuning: Additional training with your company's data
• •Multimodal Support: Embeddings that include not just text but also tables and figures

Index Redesign

• •Hybrid Search: Combination of keyword search like BM25 and vector search
• •Hierarchical Indexing: Document → Section → Paragraph hierarchy
• •Metadata Utilization: Filtering by creation date, author, category, etc.

Grounding Enhancement

• •Context Expansion: Add relevant background information to retrieved data
• •Summarization and Highlighting: Summarize and highlight important information
• •Confidence Scoring: Evaluate and weight the reliability of each information source

Implementation Example: Using LangChain and LlamaIndex

Here's a Python code example implementing these optimizations:

1from langchain.embeddings import HuggingFaceEmbeddings
2from langchain.vectorstores import FAISS
3from langchain.retrievers import EnsembleRetriever
4from langchain.retrievers import BM25Retriever
5
6# Domain-specific embedding model
7embeddings = HuggingFaceEmbeddings(
8    model_name="your-domain-specific-model",
9    model_kwargs={'device': 'cuda'}
10)
11
12# Hybrid search implementation
13vector_store = FAISS.from_documents(documents, embeddings)
14bm25_retriever = BM25Retriever.from_documents(documents)
15vector_retriever = vector_store.as_retriever()
16
17# Combine with ensemble retriever
18ensemble_retriever = EnsembleRetriever(
19    retrievers=[bm25_retriever, vector_retriever],
20    weights=[0.5, 0.5]
21)

INDX Practical Case Study

At INDX, we applied these optimization techniques in building a knowledge management system for a major manufacturing company A, achieving the following results:

• •Search Accuracy: 80% improvement over previous system
• •Response Time: Reduced from average 3 seconds to 0.5 seconds
• •User Satisfaction: Improved from 85% to 95%

Conclusion

Improving RAG accuracy is achieved not by improving a single element, but through the trinity optimization of embeddings, indexing, and grounding. At INDX, we evolve enterprise RAG systems from "somewhat correct" to "accurate" by optimizing these elements in an integrated manner.