1. Introduction “In theory, theory and practice are the same. In practice, they’re not.” – Yogi Berra When I first started playing around with Retrieval-Augmented Generation (RAG), I ran into the same trap I’ve seen others fall into: bolting an LLM onto a vector store and expecting magic. Spoiler: it doesn’t work like that. This … Read more

I. Introduction: Why I Turned to CRAG to Fix RAG “A model is only as good as the context you feed it.” If you’ve worked with Retrieval-Augmented Generation (RAG) in production, you already know the pain points. On paper, RAG sounds great—pull in some relevant chunks, stuff them into a prompt, and boom, better outputs. … Read more

1. Introduction “LLMs are brilliant… until they’re confidently wrong.”That’s a sentence I’ve had to explain more times than I can count. I’ve worked on multiple GenAI apps over the past year, and one thing became painfully clear: pure LLMs just don’t cut it for production. They’re great at general language understanding, sure—but if your app … Read more

1. Introduction “If you want to search across text and images like a pro, you can’t treat them the same.” I’ve had to build several retrieval systems that go beyond plain text—think of scenarios where you want to search images with text queries or retrieve descriptions based on an image input. The first time I … Read more

1. Introduction “The map is not the territory.” – Alfred Korzybski That quote hits different when you realize how often vector search leads your RAG system confidently into the weeds. I’ve had this happen more than once—especially when working with real-world domain-specific corpora where the embeddings just… missed the mark. Legal jargon, internal product names, … Read more