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TECHNICAL
RAG & Embedding Fine-Tuning
Overview
Providing the NLP technical grounding required for robust memory systems. This project involved fine-tuning embedding models on domain-specific conversational datasets to improve retrieval accuracy in Retrieval-Augmented Generation pipelines.
Details
COMPONENTS
Contrastive learning techniques applied to Sentence-Transformers, vector database optimization, and custom reranking heuristics.
RESULTS
Achieved a 24% improvement in top-k retrieval accuracy for ambiguous conversational queries compared to baseline models.