Optimizing RAG-Based AI – Part 1: Why Prompt Engineering Can’t Replace Data Labeling

Optimizing RAG with Labeling

In the rapidly advancing field of artificial intelligence, optimizing Retrieval-Augmented Generation (RAG) systems requires a comprehensive approach that integrates dynamic prompt generation, fine-tuning of embedding models, and advanced retrieval techniques. This blog delves into how these strategies, alongside data labeling and prompt engineering, enhance the accuracy and adaptability of RAG systems, ultimately leading to more precise and contextually relevant AI outputs.

Data Labeling for RAG Systems

The synergy between data labeling and Retrieval-Augmented Generation (RAG) is a powerful combination that significantly enhances the accuracy and effectiveness of AI systems. Here’s how these two techniques work together to improve overall performance:

• • Foundation for Precise Retrieval: Data labeling provides a structured knowledge base, enabling RAG systems to retrieve highly relevant information with greater accuracy.
  • Contextual Understanding: Labeled data helps RAG models better interpret the relationships between entities, leading to more coherent and contextually appropriate responses.
  • Reduced Hallucinations: By grounding the model in labeled, factual information, RAG systems are less likely to generate false or misleading content.
  • Enhanced Citation Capabilities: Structured data allows RAG models to provide accurate citations, improving transparency and trustworthiness.
  • Improved Prompt Engineering: Labeled data informs more effective prompt creation, resulting in more precise and tailored outputs.
  • Scalability Across Domains: The combination of labeled data and RAG enables AI systems to adapt more easily to diverse and specialized fields while maintaining high performance.
  • Real-time Learning: RAG systems can dynamically incorporate newly labeled data, allowing for continuous improvement and adaptation to changing information landscapes.

By leveraging the strengths of both data labeling and RAG, organizations can create AI systems that are not only more accurate but also more reliable, transparent, and adaptable to complex real-world applications.

Data Label and RAG Integration Options

Data labeling and annotation are crucial steps in optimizing RAG systems, enhancing model performance, and improving overall AI application quality. This guide provides a step-by-step approach to leveraging data labels throughout the AI pipeline, from data preparation to model evaluation and fine-tuning.

Data Labeling and Annotation

• • Advantage: Provides structured information for better retrieval and understanding.
  • Function: Identifies key entities, relationships, and attributes in unstructured data.

from spacy import displacy
import spacy
nlp = spacy.load("en_core_web_sm")
text = "John Smith was diagnosed with hypertension by Dr. Jane Doe on January 15, 2024."
doc = nlp(text)
entities = [(ent.text, ent.label_) for ent in doc.ents]
print(entities)
# Output: [('John Smith', 'PERSON'), ('Jane Doe', 'PERSON'), ('January 15, 2024', 'DATE')]
displacy.serve(doc, style="ent")	  

Fine-tuning Embedding Models

• • Advantage: Improves semantic understanding of domain-specific terminology.
  • Function: Adapts pre-trained models to capture nuanced meanings in specialized fields.

from sentence_transformers import SentenceTransformer, losses 
from torch.utils.data import DataLoader
  
model = SentenceTransformer('all-MiniLM-L6-v2')
train_examples = [
["patient symptoms", "medical history"], 
["diagnosis", "treatment plan"]
]
train_dataloader = DataLoader(train_examples, shuffle=True, batch_size=16)
train_loss = losses.MultipleNegativesRankingLoss(model)
  
model.fit(
    train_objectives=[(train_dataloader, train_loss)], 
    epochs=1, 
    warmup_steps=100
)
model.save('fine-tuned-medical-embeddings')	  

Query Expansion in LLM Applications

• • Advantage: Enhances retrieval by broadening search terms.
  • Function: Generates related terms to improve query coverage.

from transformers import pipeline
expander = pipeline("text2text-generation", model="t5-small")
def expand_query(query):
    expanded = expander(
        f"Expand the query: {query}", 
        max_length=50
    )[0]['generated_text']
     return query + " " + expanded
original_query = "heart disease symptoms"
expanded_query = expand_query(original_query)
print(expanded_query)
# Output: heart disease symptoms chest pain shortness of breath fatigue irregular heartbeat
	  

Data Labels in Prompt Engineering

• • Advantage: Enables creation of more precise and context-aware prompts.
  • Function: Incorporates labeled entities and attributes into prompt templates.

def generate_medical_prompt(patient_data, labeled_entities):
    template = """
    Patient: {patient_name}
    Age: {age}
    Symptoms: {symptoms}
    Medical History: {medical_history}  
    Based on the above information, suggest a possible diagnosis and treatment plan.
    """
    return template.format(**patient_data, **labeled_entities)
patient_data = {
    "patient_name": "John Smith",
    "age": 45,
}
labeled_entities = {
    "symptoms": "chest pain, shortness of breath",
    "medical_history": "hypertension, obesity"
}
prompt = generate_medical_prompt(patient_data, labeled_entities)
print(prompt)	  

Labels in Model Evaluation and Fine-tuning

• • Advantage: Provides a structured framework for assessing model performance.
  • Function: Enables targeted improvements based on labeled data.

from sklearn.metrics import classification_report
import numpy as np
  
def evaluate_model(model, test_data, labels):
    predictions = model.predict(test_data)
    return classification_report(labels, predictions)
  
# Simulated model and data
class DummyModel:
   def predict(self, X):
       return np.random.choice(['A', 'B', 'C'], size=len(X))
  
model = DummyModel()
test_data = ["sample1", "sample2", "sample3", "sample4"]
true_labels = ['A', 'B', 'A', 'C']
  
print(evaluate_model(model, test_data, true_labels))  

Creating Small Language Models (SLMs)

• • Advantage: Develops task-specific models with reduced computational requirements.
  • Function: Utilizes labeled data to train focused, efficient models for specific domains.

from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments
import torch

tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased", num_labels=3)
  
train_texts = ["Text 1", "Text 2", "Text 3"]
train_labels = [0, 1, 2]
  
train_encodings = tokenizer(train_texts, truncation=True, padding=True)
  
class Dataset(torch.utils.data.Dataset):
   def __init__(self, encodings, labels):
       self.encodings = encodings
       self.labels = labels  
   def __getitem__(self, idx):
       item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
       item['labels'] = torch.tensor(self.labels[idx])
       return item
   def __len__(self):
       return len(self.labels)
train_dataset = Dataset(train_encodings, train_labels)
  
training_args = TrainingArguments(
    output_dir='./results',
    num_train_epochs=3,
    per_device_train_batch_size=16,
    per_device_eval_batch_size=64,
    warmup_steps=500,
    weight_decay=0.01,
    logging_dir='./logs')
  
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset
)
trainer.train()
	  

Summing Up

Bringing it all together, optimizing Retrieval-Augmented Generation (RAG) systems requires a multifaceted approach that combines data labeling, prompt engineering, and advanced AI techniques. This comprehensive strategy enhances the accuracy, relevance, and reliability of AI outputs across various domains. Key components include:

• • Data labeling to create structured knowledge bases for precise retrieval.
  • Fine-tuning embedding models for improved semantic understanding.
  • Query expansion to broaden search capabilities.
  • Integrating labeled data into prompt engineering for context-aware responses.
  • Utilizing labeled data for model evaluation and fine-tuning.
  • Developing Small Language Models (SLMs) for efficient, task-specific applications.

By synergizing these elements, organizations can significantly reduce hallucinations, improve citation accuracy, and enhance the overall performance of their RAG systems. This approach not only increases the trustworthiness of AI-generated content but also enables scalability across diverse and specialized fields, making RAG a powerful tool for knowledge-intensive tasks.

Gowri Shanker, the CEO of the organization, is a visionary leader with over 20 years of expertise in AI, data engineering, and machine learning, driving global innovation and AI adoption through transformative solutions.