3 Proven Strategies to Boost RAG Accuracy Beyond the Baseline

The Experiment

We tested raw data vs. cleansed data.

• Raw Data: Direct export from CRM/Salesforce.
• Cleansed Data: Removed HTML tags, standardized terminology (e.g., "FAQ" vs "F.A.Q."), and stripped headers/footers.

The Result:

• Search Accuracy improved by ~30%.
• In specific technical domains, accuracy jumped from 59% to 77%.

The Code: A Simple Cleaning Pipeline

Don't overcomplicate it. A simple Python pre-processor is often enough.

import re from bs4 import BeautifulSoup def clean_text_for_rag(text): # 1. Remove HTML tags text = BeautifulSoup(text, "html.parser").get_text() # 2. Remove noisy separators (e.g., "-------") text = re.sub(r'-{3,}', ' ', text) # 3. Standardize terminology (Domain Specific) text = text.replace("Help Desk", "Helpdesk") text = text.replace("F.A.Q.", "FAQ") # 4. Remove extra whitespace text = re.sub(r'\s+', ' ', text).strip() return text raw_data = "<div><h1>System Error</h1><br>-------<br>Please contact the Help Desk.</div>" print(clean_text_for_rag(raw_data)) # Output: "System Error Please contact the Helpdesk."

Optimization 2: The Chunking Battle

How you cut your text determines what the LLM sees. We compared three strategies:

1. Fixed-Size Chunking: Split text every 500 tokens. (The baseline).
2. Semantic Chunking: Split text based on meaning shifts (using embedding similarity).
3. Hierarchical Chunking: Retrieve small chunks for search, but feed the "Parent" chunk to the LLM for context.

The Surprise Failure: Semantic Chunking

We expected Semantic Chunking to win. **It lost. \ In a Q&A dataset, the "Question" and the "Answer" often have different semantic meanings. Semantic chunking would sometimes split the Question into Chunk A and the Answer into Chunk B.

• Result: The system found the Question but lost the Answer. Accuracy dropped by 10-18% compared to Fixed Chunking.

The Winner: Hierarchical Chunking

Hierarchical chunking solved the context problem. By indexing smaller child chunks (for precise search) but retrieving the larger parent chunk (for context), we achieved the highest accuracy, particularly for long technical documents.

• Business Domain Accuracy: 94.4% (vs 88.9% for Fixed).

Optimization 3: Embedding Model Selection

Not all vectors are created equal. We compared Amazon Titan Text v2 against Cohere Embed (Multilingual).

The Findings

1. Short Q&A (Science/Technical):

• Cohere Embed outperformed Titan. It is highly optimized for short, semantic matching and multilingual nuances.
• Accuracy: 77.3% (Cohere) vs 54.5% (Titan).

1. Long Documents (Business/Manuals):

• Titan Text v2 won. It supports a larger token window (up to 8k), allowing it to capture the full context of long policies or manuals.
• Accuracy: 94.4% (Titan) vs 88% (Cohere).

Developer Takeaway: Do not default to OpenAI text-embedding-3. If your data is short/FAQ-style, look for models optimized for dense retrieval (like Cohere). If your data is long-form documentation, look for models with large context windows (like Titan).

The Final Verdict: How to Build It

Based on our production deployment which reduced support ticket escalation by 75%, here is the blueprint for a high-accuracy RAG system:

1. Know Your Data Type

• Is it Q&A / Support Logs?
• Use Fixed-Size Chunking. (Don't let Semantic chunking split your Q from your A).
• Use an embedding model optimized for short text (e.g., Cohere).
• Is it Manuals / Long Docs?
• Use Hierarchical Chunking.
• Use an embedding model with a large context window (e.g., Titan v2).

2. Clean Aggressively

Garbage in, Garbage out. A simple RegEx script to strip HTML and standardize terms is the highest ROI activity you can do.

3. Don't Trust Smart Defaults

Semantic Chunking sounds advanced, but for structured data like FAQs, it can actively harm performance. Test your chunking strategy against a ground-truth dataset before deploying.

RAG is not magic. It is an engineering problem. Treat your text like data, optimize your retrieval path, and the "Magic" will follow.

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