RAG for Relevancy of AI Generated Responses

 

Why RAG?

Imagine you have a huge library full of books, but instead of reading every book to find the answer to your question, you have a super-smart librarian who quickly finds the most relevant pages and summarizes them for you.

That’s how RAG (Retrieval-Augmented Generation) works in AI. Instead of just relying on what it already knows (which might be outdated or incomplete), a RAG system first retrieves fresh and relevant information from a database or the internet and then generates a response using that information.

This makes RAG much smarter and more accurate because it pulls in the latest, most relevant facts before answering—just like a good researcher!

RAG Techniques

Retrieval-Augmented Generation (RAG) relies on a combination of retrieval and generation techniques to improve the accuracy and relevance of AI-generated responses. 

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Here are some common RAG techniques:

1. Retrieval Techniques

These methods help fetch relevant documents or information before generating a response.

• Dense Retrieval (e.g., FAISS, DPR) – Uses deep learning-based embeddings to find the most relevant documents efficiently.
• Sparse Retrieval (e.g., BM25, TF-IDF) – Uses traditional keyword-based search to rank documents by relevance.
• Hybrid Retrieval – Combines dense and sparse retrieval methods to improve accuracy.
• Hierarchical Retrieval – First retrieves broad categories of information, then drills down into specific details.
• Re-ranking Models (e.g., Cross-Encoders, Rank-BERT) – After retrieving initial results, these models refine the ranking for better precision.

2. Augmentation Techniques

These techniques improve the way retrieved information is used in response generation.

• Chunking – Breaks documents into smaller passages to improve retrieval relevance.
• Context Expansion – Adds additional metadata or summaries to make retrieval more precise.
• Memory-Augmented Retrieval – Keeps track of past queries and retrieved data for better long-term context.

3. Generation Techniques

Once the right documents are retrieved, these methods help generate better responses.

• Fusion-in-Decoder (FiD) – Feeds retrieved documents directly into a language model to improve response quality.
• Retrieval-Augmented Fine-Tuning – The model is fine-tuned on retrieved data to improve accuracy.
• Contrastive Learning – Helps the model distinguish between useful and irrelevant retrieved data.
• Chain-of-Thought (CoT) Prompting – Encourages step-by-step reasoning to improve complex responses.

4. Post-Processing Techniques

These techniques refine the final output.

• Answer Verification – Cross-checks generated answers against retrieved documents.
• Fact-checking & Consistency Checking – Uses additional models or logic rules to ensure accuracy.
• Human-in-the-Loop Feedback – Uses human reviewers to refine the retrieval and generation process over time.