Introduction: RAG quality depends heavily on retrieval quality, and retrieval quality depends on query quality. Users often ask vague questions, use different terminology than your documents, or need information that spans multiple topics. Query optimization bridges this gap—transforming user queries into forms that retrieve the most relevant documents. This guide covers practical query optimization techniques: […]
Read more →Retrieval Augmented Generation Patterns: Building RAG Systems That Actually Work
Introduction: Retrieval Augmented Generation (RAG) grounds LLM responses in your actual data, reducing hallucinations and enabling knowledge that wasn’t in the training set. But naive RAG—embed documents, retrieve top-k, stuff into prompt—often disappoints. Retrieval misses relevant documents, context windows overflow, and the model ignores important information buried in long contexts. This guide covers advanced RAG […]
Read more →Embedding Model Selection: Choosing the Right Model for Your RAG System
Introduction: Choosing the right embedding model is critical for RAG systems, semantic search, and similarity applications. The wrong choice leads to poor retrieval quality, high costs, or unacceptable latency. OpenAI’s text-embedding-3-small is cheap and fast but may miss nuanced similarities. Cohere’s embed-v3 excels at multilingual content. Open-source models like BGE and E5 offer privacy and […]
Read more →DevSecOps: Integrating Security into DevOps – Part 2
Continuing from my previous blog, let’s dive deeper into the implementation of DevSecOps. Integrating Security into DevOps To implement DevSecOps, it is essential to integrate security into every phase of the DevOps lifecycle. The following are the key phases in DevOps and how to integrate security into each phase: DevSecOps Best Practices Here are some […]
Read more →Google to begin offering Cloud storage
In the “coming weeks” you will find that your Google Docs account will allow you to upload any kind of file for online storage. Google Docs will support uploading any kind of file as long as it is under 250MB in size. You will then be able to store your videos, raw images, zip files […]
Read more →Memory Systems for LLMs: Buffers, Summaries, and Vector Storage
Introduction: LLMs have no inherent memory—each request starts fresh. Building effective memory systems enables conversations that span sessions, personalization based on user history, and agents that learn from past interactions. Memory architectures range from simple conversation buffers to sophisticated vector-based long-term storage with semantic retrieval. This guide covers practical memory patterns: conversation buffers, sliding windows, […]
Read more →