Adding AI to real products: RAG chatbots, vector search, LLM features in Laravel and the guardrails that make them reliable in production.
Showing 6 of 7 articles
Should your company chatbot use retrieval-augmented generation or a fine-tuned model? Here is how to decide based on how your knowledge changes, how accurate answers must be, and what you can maintain.
RAG chatbot giving wrong answers? Learn how to find whether retrieval, the prompt or your content is at fault, and the fixes that work in order: chunking, hybrid search, grounding and testing.
What an AI chatbot really costs to build and run: ballpark ranges by scope, the factors that move the price, the monthly costs people forget, and how to keep a first version small.
HNSW or IVFFlat? A practical guide to choosing and tuning a pgvector index for RAG search on PostgreSQL, covering recall, build time, memory, tenant filtering, and when to skip the index entirely.
Users needed answers, not database screens. I built an AI-powered Laravel chatbot that connects OpenAI with real application data.
AI can generate a feature quickly, but a working demo is not a production-ready system. Here is how I approach closing that gap.
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AI demos are easy. AI features that customers can rely on are not. These articles focus on the engineering around the model: retrieval quality, vector search with PostgreSQL and pgvector, prompt and persona guardrails, cost control, and integrating LLMs into existing Laravel and React applications without making a mess.
Everything here comes from shipping AI features to real users, including a RAG knowledge platform and AI-assisted workflows in a safety management SaaS.
Have an AI use case in mind? See AI integration & AI-powered applications, or book a free call to check whether it's worth building.