Techniques for building AI systems that retrieve knowledge effectively – covering embeddings, vector databases, document chunking, semantic search, and scalable retrieval pipelines for modern applications.
When developers first build a Retrieval-Augmented Generation (RAG) system, they usually follow what’s now known as “classical RAG“: chunk documents, convert them into dense vector embeddings using an API like OpenAI, save them to a vector database, and query them…
In some cases, traditional approaches fall short. Retrieval-Augmented Generation (RAG) has become one of the most effective techniques for improving the accuracy of Large Language Models (LLMs). Instead of relying solely on the model’s training data, a RAG memory pipeline…
AI engineering is shifting from pure model building to system design, where integrating models into reliable backend services is the main challenge. Modern AI stacks combine Large Language Models, data pipelines, and observability, making them closer to distributed systems than…
Semantic search over local Markdown documentation is a common backend requirement. Traditional tools like grep work well for keyword matching, but they fail when the query requires intent understanding. This is where RAG (Retrieval-Augmented Generation) becomes useful. To explore this…
Imagine having a conversation with an AI that can not only understand what you’re saying but also provide accurate and relevant information on the fly. This is made possible by a revolutionary architecture called RAG, which combines the power of…
