Practical LLM engineering: DeepSeek-R1, Transformer Attention, DPO, vLLM benchmarks, RAG Caching, and Vision-Language Models.
Unpacking DeepSeek-R1's Multi-head Latent Attention (MLA), DeepSeekMoE 210B sparse routing, and group relative policy optimization (GRPO) reinforcement learning.
How Grouped-Query Attention (GQA) and Multi-Query Attention (MQA) reduce KV-cache memory bandwidth consumption in LLM inference.
Avoid repeating $0.03 LLM queries by matching semantically similar user prompts in vector cache before hitting API endpoints.
Moving beyond naive vector lookup: building adaptive multi-agent retrieval routers that decide when to query vector databases vs SQL databases.
DPO eliminates the need for training separate reward models and complex PPO reinforcement loops by deriving the implicit reward directly from binary preference data.
How malicious instructions hidden inside scraped web pages or user uploaded PDFs can hijack your LLM agent's execution flow.
