Free AI system‑design guide drops
A free AI System Design guide covering RAG fundamentals, hybrid retrieval (BM25 + vectors), production architectures, evaluation metrics, and hallucination fixes was published — a practical primer for backend and ML infra interviews. The guide bundles retrieval strategy with production concerns, not just model selection. (x.com)
The AI System Design Guide is published as an actively maintained GitHub repository by user "ombharatiya" and bills itself as "The Complete Interview & Production Reference." (github.com) The repo's file tree organizes material into topical folders such as 00-interview-prep, 01-foundations, 04-inference-optimization, 06-retrieval-systems, 10-production-case-studies, and 11-infrastructure-and-mlops. (github.com) The 00-interview-prep section contains a question bank, structured answer frameworks, and whiteboard-style exercises aimed at interview depth and staff-level questions. (github.com) The production case-studies chapter presents six end-to-end architectures with explicit implementation notes, cost breakdowns, and "lessons learned" sections for real deployments. (deepwiki.com) The guide's README lists model references updated as of March 2026, naming Claude 3.7 Sonnet, GPT-4.5, o3, Gemini 2.0 Flash, and Grok 3 as current entries. (github.com) Dedicated technical chapters cover inference optimization (KV cache, batching, speculative decoding), infrastructure and MLOps patterns, and evaluation/observability playbooks for production LLM systems. (github.com) The author profile and repo notes describe the project as a "living document" that aims to keep pricing, model references, and design patterns current while reaching an audience the maintainer estimates at tens of thousands of engineers. ( )
