Published : Oct 23, 2024
NOT ON THE CURRENT EDITION
This blip is not on the current edition of the Radar. If it was on one of the last few editions, it is likely that it is still relevant. If the blip is older, it might no longer be relevant and our assessment might be different today. Unfortunately, we simply don't have the bandwidth to continuously review blips from previous editions of the Radar.
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Oct 2024
Trial
When building LLM applications based on retrieval-augmented generation (RAG), the quality of embeddings directly impacts both retrieval of the relevant documents and response quality. Fine-tuning embedding models can enhance the accuracy and relevance of embeddings for specific tasks or domains. Our teams fine-tuned embeddings when developing domain-specific LLM applications for which precise information extraction was crucial. However, consider the trade-offs of this approach before you rush to fine-tune your embedding model.
