Thoughts, architectures, and tutorials on building scalable Generative AI and machine learning systems.
Generic foundation embeddings struggle with specialized jargon. Learn how contrastive fine-tuning on bi-encoders dramatically increases Top-1 retrieval accuracy.
Deploying open-source LLMs at scale requires overcoming the memory bandwidth bottleneck. Here is how KV cache management and continuous batching yield 10x throughput gains.
Retrieval-Augmented Generation (RAG) is easy to prototype but hard to productionize. Here's how to structure your vector embeddings and chunking strategies to scale efficiently.