7/🚀 Time to move beyond toy demos — build production-grade RAG. Let’s connect if you’re scaling your custom AI stack! #RAGstack #AIInfrastructure #MLOps #BentoML #LangChain #MachineLearning #Logimonk

RAG, LLMs, embeddings, retrieval pipelines… Sounds like a tech talk. But it’s actually the architecture behind how women find answers faster in our system. Tech = care, when done right. #AIforHealth #GenAI #RAGstack #responsibleai #empoweringpatients #femispace #marinavieva

The AI stack in 2025 isn’t a playground. It’s the backend. → Retrieval as your brain → Agents as your teammates → Observability as your safety net Ship fast, monitor deeply, and scale with eyes open. More here: @zeroxaitales #LLMOps #AItools #ragstack #agentframeworks

Layer 1: Context Retrieval LLMs without context are hallucination machines. This is where RAG (retrieval-augmented generation) shines. Your AI is only as smart as what you feed it. #RAGstack #vectorDB

7. LlamaIndex github.com/jerryjliu/llam… Go-to RAG toolkit. File loaders, memory layers, chunkers, vector integration. Used in most doc-based chatbots today. #llamaindex #RAGstack

8. LlamaIndex The RAG-native framework. — Load data from PDFs, Notion, APIs — Build indexes — Plug into LangChain or standalone Go-to for any LLM connected to real data. #LlamaIndex #RAGstack

Caching LLMs forget. Your infra shouldn’t. What to cache: – Embeddings – RAG responses – Model outputs – Common prompts Reuse saves cost and improves UX. #ModelCaching #SemanticSearch #RAGstack

Example: An AI writing tool uses GPT-4 to win early users. Hits 10K DAUs → moves to Mixtral + Qdrant stack. Cost drops 80%. Output tuned to domain. #ragstack #AIscaling #AIeconomics

Use cases: → RAG pipelines (retrieval-augmented generation) → Domain copilots → Document search → AI agents with long-term memory Your data becomes recallable intelligence. #ragstack #agentarchitecture #LLMops

“Just hook up a vector DB with good embeddings…” You’ve heard it. But what does it actually mean? In 2025, every real AI product is powered by 4 invisible tools: → Embeddings → Vector DBs → Tokenizers → Transformers Let’s break it down. #AIinfra #ragstack

5️⃣ Memory + Retrieval Infra Want your AI to “remember”? Use vector DBs like: → Pinecone → Weaviate → Chroma → LanceDB They power RAG pipelines—retrieval-augmented generation. Context is the new compute. #RAGstack #VectorSearch #MemoryInfra

2025 memory stack recap: • Embeddings: OpenAI, Cohere, HuggingFace • Vector DB: FAISS, Weaviate, Pinecone • RAG Logic: LangChain, LlamaIndex • State Track: LangGraph, Redis • Orchestration: CrewAI, AutoGen #memorystack #llmsystems #ragstack

RAG done right includes: • Reranking results • Semantic chunking • Multi-hop queries • Real-time freshness RAG is the glue between static data and dynamic decisions. It’s context injection—at scale. #retrievalAI #aiengineering #ragstack

There are 3 memory layers every serious agent needs: Long-term (embeddings, files, docs Short-term (active thread context) Stateful (task vars, history, logic) Each unlocks a different tier of capability. #ragstack #vectorstores #aidevelopment