Description

  • Conducted extensive research on Large Language Models (LLMs) to determine the most effective models for specific use cases, performance and efficiency.

  • Explored and implemented diverse embedding models, significantly enhancing overall accuracy and relevance of information in retrieval tasks.

  • Utilized Qdrant for efficient storage and retrieval of vector embeddings, improving the performance of the Retrieval Augmented Generation (RAG) pipeline.

  • Designed and fine-tuned prompts, ensuring high-quality, contextually accurate and high-quality responses tailored to diverse use cases.