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How to Integrate AI into Existing Applications: A Practical Guide
You have a mature, stable application. It works well, your users understand it, and your team knows the codebase inside and out. But the world is buzzing with AI, and you’re feeling the pressure to add “smart” features. The last thing you want to do is rewrite your entire application from scratch. The good news is, you don’t have to.…
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What Is a Vector Database? Embeddings, Similarity Search, and RAG
If you are building with LLMs, you will eventually run into a simple question: what is a vector database, and why does everyone use one for RAG? The short answer is this: a vector database stores numerical representations of text, images, or other data so you can find items by meaning, not just by exact keywords.…
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Production RAG Architecture: Citations, Caching, Evaluation, and Guardrails
Building a RAG prototype is one thing. Shipping it to real users is something else. In a prototype, you can get away with a rough retrieval pipeline and a forgiving prompt. In production, people expect the system to be fast, reliable, and honest about where its answers come from. They also expect it to handle bad inputs, noisy…