vector search
-
Blog
How to Rank in ChatGPT and Perplexity: AI Search Optimization Explained
How to Rank in ChatGPT and Perplexity: AI Search Optimization Explained By Digitpatrox Editorial · May 11, 2026 We are observing a fundamental shift in how information is discovered online. For the last decade, SEO was about satisfying a keyword-based index. Today, it is increasingly about satisfying a retrieval pipeline. When an AI engine like Perplexity or SearchGPT answers a…
Read More » -
Guides
How to Build a RAG System with pgvector and LangChain: The Production Architecture
How to Build a RAG System with pgvector and LangChain: The Production Architecture Most production AI failures are not model failures. They are retrieval failures. If you want to understand why your RAG system is hallucinating, stop looking at your prompt. A perfect prompt with the wrong data yields a confident hallucination. An average prompt with the correct data yields…
Read More » -
Blog
What Is Context Engineering?
What Is Context Engineering? Why Prompt Engineering Is No Longer Enough Most production AI failures are not model failures. They are retrieval failures. For the last two years, the internet was flooded with “Prompt Engineering Cheat Sheets,” as if knowing how to tell an LLM to “take a deep breath” was a technical moat. Typing instructions into a chat box…
Read More » -
Blog
Vector Databases vs SQL Expained: What Actually Breaks in AI Search
Vector Databases Explained: What Actually Breaks in Production A lot of teams assume semantic search “just works” once the embeddings are in place. It usually doesn’t. A year ago, every AI architecture diagram suddenly started including a vector database-Pinecone, Weaviate, Qdrant, Milvus. For a while, it felt like you needed one just to be taken seriously. In reality, most companies…
Read More »