Importance and relevance of embedding models, their construction, and the methodology for selecting the appropriate architecture

On May 3, Ilia State University will host an event as part of Tbilisi AI Meetups:

“Importance and relevance of embedding models, their construction, and the methodology for selecting the appropriate architecture.”

In the age of powerful large language models, embeddings remain a critical component in retrieval, recommendation, and understanding systems. This session explores why embeddings still matter, how they’re built, and how to choose the right architecture for your domain and application.

Agenda Highlights:

• Context length limitations and the rise of RAG;
• Why LLMs aren’t ideal for embedding generation;
• Causal attention vs contrastive learning;
• Sparse vs dense retrieval (TF-IDF, SPLADE, hybrid approaches);
• Bi-Encoders vs Cross-Encoders vs Late Interaction;
• Latent interaction models and HNSW search;
• Contrastive learning, triple loss, Siamese networks;
• Speed vs accuracy trade-offs;
• Domain-specific embedding tuning (e.g., code vs text);
• Evaluation with BEIR and MTEB benchmarks.