The hype around AI is loud - but what actually works in production? I’ve curated six cutting-edge whitepapers from Google that go beyond flashy demos.
All in one place!
These deep dives cover Retrieval-Augmented Generation (RAG), vector search, agent frameworks, prompt engineering, and MLOps practices used to deploy generative systems at scale.
📥 Download. Learn. Build smarter AI.
Google Whitepaper Emebddings And Vectorstores
9.19MB ∙ PDF file
A foundational primer on how embeddings power semantic understanding in LLMs and how vector databases enable fast, relevant retrieval. Essential reading for anyone building or using RAG systems.
Download
Google Whitepaper Operationalizing Generative AI On Vertex AI
11.8MB ∙ PDF file
This whitepaper goes beyond theory, showing how to productionize GenAI applications on Vertex AI with tool orchestration, prompt tuning, MLOps, and RAG pipelines. Includes architectural blueprints and best practices.
Download
Google Whitepaper Solving Domain-Specific Problems Using Llms
7.76MB ∙ PDF file
This whitepaper goes beyond theory, showing how to productionize GenAI applications on Vertex AI with tool orchestration, prompt tuning, MLOps, and RAG pipelines. Includes architectural blueprints and best practices.
Download
Google Whitepaper Agents
8.87MB ∙ PDF file
A deep dive into the emerging world of autonomous AI agents. Learn how LLMs interact with tools, reason across multiple steps, and use memory/state to complete complex tasks. Also includes LangChain and Vertex AI agent examples.
Download
Google Whitepaper Prompt Engineering
6.5MB ∙ PDF file
Your tactical guide to writing effective prompts. Covers zero-shot to CoT prompting, ReAct, Tree-of-Thoughts, and best practices for controlling model behavior. Great for developers, researchers, and prompt power users.
Download
Google Whitepaper Foundational Large Language Models & Text Generation
6.81MB ∙ PDF file
This is the architectural backbone. From transformer basics to MoE, fine-tuning techniques, latency trade-offs, and real-world applications—this is your end-to-end understanding of how LLMs are built and used.
DownloadIf you found this post valuable, consider subscribing ✅ for more deep dives into applied AI, architecture patterns, and tools that actually scale.
Feel free to share 🔄 this with a colleague or team that’s exploring GenAI - and drop a comment 📝 if you're working on something cool or want to jam on ideas.
Let’s make AI that doesn’t just impress - but works.
Excellent breakdown! I like all the added deep dive resources
Thank you for sharing Jakub!