Ai Automation
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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's the architecture, including the chunking strategy, embedding model choice, hybrid retrieval, and confidence thresholding, behind a pipeline that achieves over 80% retrieval accuracy at scale.
Gaurang Ghinaiya
June 6, 2026
Why AI Chatbots Hallucinate and How to Fix It
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
Production RAG Architecture: Chunking, Embeddings, Hybrid Retrieval, and Anti-Hallucination. The Complete Guide
RAG is not a single thing. It is a pipeline with seven or eight discrete engineering decisions, each of which significantly affects accuracy. This is the complete architecture guide based on what we have learned shipping RAG systems to production.
Gaurang Ghinaiya
June 3, 2026
LLM Integration Patterns for B2B SaaS: From API Wrapper to Production-Grade AI Feature
Adding an LLM to your B2B product is not the same as building a consumer chatbot. Token costs, reliability, latency, multi-tenant data isolation, and auditability all look different when your customers are businesses using your AI feature in production workflows every day
Gaurang Ghinaiya
May 28, 2026
Engineering Post-Sale Customer Engagement: Retention Automation Without the Spam
Post-purchase sequences that convert are data-driven systems built on behavioral signals, purchase history, and product feedback loops, not emails written once and forgotten. This is the engineering behind retention systems that actually move the needle.
Gaurang Ghinaiya
May 15, 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 Anti-Hallucination Stack: Engineering LLM Products That Are Accurate Enough to Trust
Hallucination is not a bug you fix. It is a property of the model that you design around. The engineering work involves building detection layers, confidence mechanisms, and fallback behaviors that make a product trustworthy, even when the model is wrong.
Gaurang Ghinaiya
May 4, 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 and 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
Vector Database Selection for Production RAG: Pinecone vs pgvector vs Weaviate vs Qdrant
Choosing a vector database for a RAG system is a decision that is hard to reverse after you have indexed 500,000 chunks. This is the comparison framework we use, the benchmarks that matter in practice, and the decision we reached for different project types.
Gaurang Ghinaiya
March 10, 2026
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