10 posts tagged with "AI"
Three Azure services now offer serious vector search capabilities. Azure AI Search, Cosmos DB for NoSQL, and Azure Database for PostgreSQL with pgvector each have a distinct sweet spot. Here is which to use and when.
Three serious options exist for building AI agents on Azure, and they solve different problems. Azure AI Agent Service is fully managed but opinionated. Semantic Kernel is Microsoft-native and enterprise-ready. LangChain is flexible but complex. Here is how to choose.
Prompt injection is the SQL injection of the AI era. Unlike traditional vulnerabilities, it requires no code exploit — just carefully crafted text. Here are the seven patterns attackers use and the Azure defenses that stop them.
Prompts are code. They need version control, testing, deployment pipelines, and rollback strategies. Most teams treat them like config files and wonder why their AI quality is inconsistent. Here is how to manage prompts properly.
Most teams instrument their AI apps with latency and error rate dashboards. Those are necessary but not sufficient. The failures that hurt production AI are invisible to standard APM tooling. Here are the four signals that actually matter.
Pure vector search sounds like the obvious choice for RAG. In practice, it misses exact matches, product codes, and proper nouns that keyword search catches trivially. Hybrid search using RRF consistently outperforms either approach alone.
Chunking is the step most teams rush past when building RAG systems. The wrong chunking strategy makes your search retrieve the right document but the wrong passage — and the LLM hallucinates the rest. Here is what works.
Not every Azure OpenAI deployment should look the same. Direct API, APIM gateway, and private VNet topologies each solve a different set of requirements. Here is when to use each one.
Deploying Azure OpenAI in a demo is easy. Running it in production at scale is a different problem. Here are the seven failure modes we hit repeatedly — and how to fix them before they hit you.
Your AI app can fail even when nothing is technically broken. The server is healthy, the API is fast, the dashboards are green — but your chatbot still gives a completely wrong answer. That is the weird thing about GenAI systems.
