The quality of your RAG (Retrieval-Augmented Generation) system depends more on your embedding strategy than on your choice of LLM. Poor embeddings mean irrelevant context retrieval, which no amount of prompt engineering can fix. This comprehensive guide explores production-ready embedding strategies—covering model selection, chunking approaches, hybrid search techniques, and optimization patterns that directly impact retrieval […]
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Kubernetes 1.35: In-Place Pod Resource Updates and AI Model Image Volumes
Kubernetes 1.35, released in January 2026 and now supported on Amazon EKS and EKS Distro, marks a significant milestone in container orchestration—particularly for AI/ML workloads. This release introduces In-Place Pod Resource Updates, allowing you to resize CPU and memory without restarting pods, and Image Volumes, a game-changer for delivering large AI models using OCI container […]
Read more →AI Engineering in 2025: The Year That Changed Everything – A Comprehensive Review
A comprehensive review of the most transformative year in AI engineering history. From GPT-5.2 to Gemini 3, xAI’s Grok 4, DeepSeek’s rise, Kimi K2’s emergence, regulatory shifts, and what’s coming in 2026.
Read more →Tips and Tricks – Cache LLM Responses for Cost Reduction
Implement semantic caching to avoid redundant LLM calls and reduce API costs.
Read more →Tips and Tricks – Implement Prompt Templates for Consistent LLM Output
Use structured prompt templates to get reliable, formatted responses from LLMs.
Read more →Tips and Tricks – Use Embeddings for Semantic Search
Implement semantic search using text embeddings for more relevant results than keyword matching.
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