Comparing AI-Augmented Information Retrieval Strategies

Overview

Explore AI-powered information retrieval strategies with Women of Search experts. Analyze fine-tuning ML models, RAG pipelines, and reranking methods for optimal use in search applications.

Syllabus

• Introduction to AI-Augmented Information Retrieval
Overview of traditional vs. AI-powered strategies
Importance of AI in modern search applications
Introduction to Women of Search experts
• Fine-Tuning Machine Learning Models for Information Retrieval
Understanding fine-tuning in ML
Techniques for fine-tuning models for optimal search
Evaluating performance improvements
• Retrieval-Augmented Generation (RAG) Pipelines
Concept and architecture of RAG pipelines
Integrating retrieval methods with generation models
Case studies of RAG applications in search systems
• Reranking Methods in AI-driven Search
Purpose of reranking in information retrieval
Common reranking algorithms and techniques
Implementing reranking for improved search accuracy
• Comparative Analysis of Strategies
Criteria for comparing AI retrieval strategies
Pros and cons of fine-tuning, RAG, and reranking
Selecting the best approach for specific search scenarios
• Case Studies and Practical Implementations
Real-world applications and success stories
Hands-on implementation exercises
Analyzing the impact of chosen strategies
• Ethical Considerations and Challenges
Addressing ethical concerns in AI-driven search
Mitigating biases in models and retrieval strategies
• Future Directions in AI-Augmented Information Retrieval
Emerging trends and technologies
Potential impact on various industries
• Course Summary and Key Takeaways
Recap of learning objectives and covered topics
Discussion on the future of AI in search applications

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Subjects

Data Science