aishwaryanr/awesome-generative-ai-guide

Think of this as a living textbook for anyone who needs to get serious about generative AI without drowning in scattered papers and blog posts. The repository pulls together weekly research digests, curated paper summaries, interview question banks covering LLMs and diffusion models, and runnable notebooks that let you move from theory to hands-on experimentation quickly. The structure is genuinely thoughtful, organized by topic rather than just dumped chronologically, which makes it useful as a reference you return to rather than a one-time skim. For founders building AI-native products, it covers the architectural decisions and emerging techniques that actually matter right now, grounding you before you make expensive infrastructure or model choices. For technical leads onboarding engineers who are new to generative AI, it cuts weeks off the ramp-up curve. The honest reservation is that it requires self-direction. There is no guided path or learning sequence, so someone without prior ML context may struggle to know where to start. -> Best for: technical founders and engineering leads who need a structured but self-serve generative AI knowledge base for themselves or their team.