Ai Engineering
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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 you design around. The engineering work is building the detection layers, confidence mechanisms, and fallback behaviors that make a product trustworthy even when the model is wrong.
Gaurang Ghinaiya
December 4, 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
Multi-Tenant SaaS Architecture: Isolation Models, Data Strategies, and the Decisions That Scale
The multi-tenancy architecture decision you make when you have 10 customers is the architecture you will live with when you have 10,000. This is the tradeoff analysis for each isolation model and the implementation patterns that hold up at scale.
Gaurang Ghinaiya
April 8, 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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