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Ever wonder how AI apps make sense of images, text, or video? It starts with a vector database. From vectorization to HNSW indexing, @bsbodden, Redis’ Principal Applied AI Engineer, reveals how they work, why they matter, and the techniques behind them.

AI’s impact isn’t just about its capabilities. It also comes from the people who bring AI to life. Our VP of AI Product Management, Manvinder Singh, recently shared practical advice on turning AI potential into real-world results alongside other tech leaders. Read the full

We’re co-hosting a Founder Game Night next Thursday, July 24, in Brooklyn with @TavilyAI, Eden, and @JakeZegil at @FractalTechNYC. Come for an evening of board, card, and social games. Stay for the great conversations with other folks building in AI infrastructure. Bring your

RT @thesysdev: Don’t miss this one 👇. @rabi_guha joins leaders from @OpenPipeAI , @Plaid & @Redisinc on the AGI Stage at @genaisummitsf….

RT @TechNative: How tech leaders are delivering real impact with AI this AI Appreciation Day . @PingIdentity @Genesys @Dynatrace @LucidSoft….

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Principal Applied AI Engineer @bsbodden dives into:. ▶️ Query rewriting, dense retrieval & semantic chunking.▶️ How to structure data for better grounding.▶️ What’s happening behind the scenes.▶️ Why RAG improves accuracy, reduces hallucinations & keeps outputs fresh.

Retrieval Augmented Generation (RAG) is one of the most powerful architectural patterns in GenAI today—combining the strengths of LLMs with real-time, external context from your own data.

If you're working with vector search, LLM pipelines, or unstructured data, you’re relying on embedding models. Redis Developer Advocate @RaphaelDeLio breaks down what an embedding model is and more:

For leaderboard use cases, what’s your go-to Redis data structure?. When 100M+ fans stream live @IPL matches on @JioCinema, performance can’t falter. That’s why they use Redis to power real-time features like leaderboards, live view tracking, and interactive stickers, all with.

RT @cognee_:

RT @cognee_: New vector database unlocked: @Redisinc 🚀. We’ve teamed up with Redis to bring blazing-fast vector + graph memory to every AI….

Redis Released📍 SF • Sept 4. A one-day event for teams building fast, scalable AI. Learn side-by-side with Redis engineers and AI leaders like @riferrei, who’ll share how to keep vector embeddings fresh in production. Save your seat: 🔗

Now available in public preview:

Redis Insight on Cloud lets you manage and query your Redis data directly from your browser. New query autocompletion in the web and desktop apps suggests schema, index, and key names in real time to speed up your workflow.

RT @tavilyai: Headed to #AWSSummitNYC? . Tavily is teaming up with @Redisinc @LangChainAI and @thekonginc to host a happy hour for AI buil….

For more, check out these additional resources on GitHub:

In this video, you’ll learn:.✅ What embedding models are and how they represent meaning using vectors.✅ How they help computers understand text, images, and unstructured data .✅ Why embeddings drive use cases like search, recommendations, and fraud detection.

Redis Developer Advocate @RaphaelDeLio breaks down embedding models and shows how they power search, recommendations, and AI features behind the scenes.