Nikhil Sachan
Production notes on Next.js, Node.js, SaaS, APIs, cloud, and AI integration — the same stack I use to ship apps for clients and products like those on my portfolio.
How agentic RAG combines retrieval-augmented generation with autonomous agents — ReAct patterns, chain-of-thought planning, memory systems, and building multi-agent RAG pipelines.
A complete guide to building production RAG systems — tech stack selection, data ingestion pipelines, chunking strategies, evaluation frameworks, and deployment architecture with code examples.
Everything you need to know about RAG — from fundamentals and architecture to production deployment. The definitive guide for developers building AI systems with retrieval-augmented generation.
A technical deep dive into RAG architecture — embeddings models, vector database comparison (FAISS, Pinecone, Weaviate, Chroma), retrieval strategies, and system design patterns.
Real-world RAG implementations across industries — customer support AI, internal knowledge assistants, legal document search, medical AI, and coding assistants with lessons learned.
A beginner-friendly deep dive into how RAG works — the retriever-generator pattern, embeddings, and building your first RAG chatbot with practical code examples.
A practical comparison of RAG and fine-tuning — ideal use cases, anti-patterns, cost analysis, and a decision framework to help you choose the right approach for your AI application.