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RAG Pipeline Design for Non-Hallucinating AI: What We Learned Shipping to Production
Most RAG implementations that work in demos fail in production. Here is the architecture — chunking strategy, embedding model choice, hybrid retrieval, confidence thresholding — behind a pipeline that achieves >80% retrieval accuracy at scale
Gaurang Ghinaiya
June 6, 2026
Why AI Chatbots Hallucinate
Hallucination is an inherent property of how language models work, not a bug that will be patched. Here is how to architect AI systems that cannot hallucinate about the things that matter.
Gaurang Ghinaiya
June 3, 2026
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RAG vs Fine-Tuning: Which One Does Your Business Actually Need?
Every business exploring AI hits the same fork: fine-tune a model, or build a RAG system? The distinction matters enormously for budget and accuracy. Here is how to choose
Gaurang Ghinaiya
May 28, 2026
How We Cut Product Image Costs by 80% for an E-commerce Seller Processing 1,500 Images Per Week
A UK-based e-commerce seller was spending three days and significant labour cost between photo shoot and live listing for every watch in their inventory. We built an automated image pipeline that reduced per-image cost by 80% and eliminated the backlog. Here is the architecture and what it actually cost to build
Gaurang Ghinaiya
May 6, 2026
The AI Feature Your E-commerce Platform Actually Needs (It Is Not a Chatbot)',
AI chatbots are the most visible e-commerce AI implementation and usually the least valuable. The highest-ROI AI applications in e-commerce are invisible to the customer — they run in the background and make every other part of the operation more efficient.
Gaurang Ghinaiya
April 3, 2026
Why Prompt Engineering Alone Will Not Save Your LLM Product
Prompt engineering is a starting point, not a strategy. The LLM products that survive production are built on output validation, fallback architecture, and human-in-the-loop design — not on a carefully worded system prompt.
Gaurang Ghinaiya
April 1, 2026
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