Serverless RAG with Next.js: Building AI-Powered Knowledge Bases using AWS Lambda & ElasticSearch
Master the art of building scalable, AI-powered knowledge bases using Retrieval Augmented Generation (RAG) in a serverless environment. This comprehensive course guides you through integrating Next.js for dynamic frontends, AWS Lambda for efficient serverless backends, and ElasticSearch (or AWS OpenSearch Service) for powerful vector search and data storage, enabling advanced conversational AI and intelligent information retrieval.
Serverless RAG with Next.js: Building AI-Powered Knowledge Bases using AWS Lambda & ElasticSearch
Skills you build, step by step.
Practical lessons designed to get you writing working code. Every topic builds directly on the previous one.
Understand the core principles and benefits of Retrieval Augmented Generation (RAG) for AI applications.
Develop dynamic and responsive user interfaces with Next.js for interacting with AI-powered knowledge bases.
Implement scalable and cost-effective serverless APIs using AWS Lambda and API Gateway.
Utilize ElasticSearch (or AWS OpenSearch Service) for efficient data indexing, storage, and advanced vector search (k-NN).
Build complete RAG pipelines, including text chunking, embedding generation, and contextual retrieval.
Who this course is made for.
Clear expectations before you begin. No unnecessary background needed to get moving with confidence.
Target Level
Suitable for advanced learners seeking hands-on build experience.
Prerequisites
- Intermediate knowledge of JavaScript/TypeScript.
- Basic understanding of React.js and Next.js framework.
- Familiarity with fundamental AWS services (S3, IAM, general cloud concepts).
- Basic understanding of AI/Machine Learning concepts, especially concerning LLMs and embeddings.
- Comfort with using command-line interface (CLI) and Git.
What You Need
A computer with an internet connection, a text editor, and time for weekly practice.