Augmentation Method

Retrieval-Augmented Generation (RAG)

Grounding AI outputs in verified knowledge sources through dynamic retrieval and context injection

Overview

Retrieval-Augmented Generation (RAG) combines the power of large language models with the precision of information retrieval systems. By dynamically fetching relevant documents and injecting them into the generation context, RAG significantly reduces hallucination while improving factual accuracy and source attribution.

Key Advantages

📚

Source Attribution

Every claim traceable to original documents

🎯

Reduced Hallucination

64% reduction in factual errors

🔄

Dynamic Updates

Knowledge base updates without retraining

🔍

Full Traceability

Complete provenance chains

RAG Architecture

1. Document Processing

Document chunking (512-1024 tokens)
Metadata extraction
Embedding generation (SBERT/MPNet)
Index creation (FAISS/Pinecone)

2. Query Processing

Query embedding
Semantic search (k=3-5)
Relevance scoring
Context assembly

3. Generation

Context injection
Prompt augmentation
Constrained generation
Citation insertion

4. Post-Processing

Fact verification
Citation formatting
Confidence scoring
Audit logging

RAID-T Performance Analysis

Responsibility

4.7/5.0

Strong factual grounding, minimal hallucination

Auditability

4.8/5.0

Complete retrieval logs and source tracking

Interpretability

4.9/5.0

Clear source-to-claim mapping

Dependability

4.6/5.0

Consistent retrieval and generation

Traceability

5.0/5.0

Perfect provenance chains from source to output

Implementation Details

Document Preparation

# Chunking strategy
chunk_size = 512
overlap = 50

# Embedding model
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('all-mpnet-base-v2')

# Vector store
import faiss
index = faiss.IndexFlatL2(768)

Retrieval Pipeline

# Semantic search
def retrieve_context(query, k=3):
    query_emb = model.encode(query)
    distances, indices = index.search(query_emb, k)
    
    contexts = []
    for idx in indices[0]:
        contexts.append({
            'text': documents[idx],
            'score': distances[0][idx],
            'source': metadata[idx]
        })
    return contexts

Context Injection

# Augmented prompt
prompt = f"""
Based on the following documents:
{retrieved_contexts}

Answer the question: {query}

Cite sources using [Doc-X] notation.
"""