In the last part of this series, we experienced how to create a new wordpress blog instance in Azure App Service. In this part we will learn, how to configure your wordpress instance for publishing. Now that we have WordPress instance deployed in Azure App Service, lets expore the app service instance a bit. Step […]
Read more →Category: Emerging Technologies
Emerging technologies include a variety of technologies such as educational technology, information technology, nanotechnology, biotechnology, cognitive science, psychotechnology, robotics, and artificial intelligence.
General Availability of Azure Database Services for MYSQL and PostgreSQL
It has been a while I have written something on my blog. I thought of getting started again with a good news that Microsoft Azure team has announced the general availability of Azure Database Services for MySQL and PostgreSQL. In my earlier posts, I have provided some oversight into Preview Availability of these services as […]
Read more →Prompt Injection Defense: Securing LLM Applications Against Adversarial Inputs
Introduction: Prompt injection is one of the most significant security risks in LLM applications. Attackers craft inputs that manipulate the model into ignoring its instructions, leaking system prompts, or performing unauthorized actions. As LLMs become more integrated into production systems—handling sensitive data, executing code, or making API calls—the attack surface grows dramatically. This guide covers […]
Read more →LLM Evaluation Metrics: Measuring Quality in Non-Deterministic Systems
Introduction: Evaluating LLM outputs is fundamentally different from traditional ML metrics. You can’t just compute accuracy when there’s no single correct answer, and human evaluation doesn’t scale. This guide covers the full spectrum of LLM evaluation: automated metrics like BLEU, ROUGE, and BERTScore for measuring similarity; semantic metrics that capture meaning beyond surface-level matching; LLM-as-judge […]
Read more →Vector Database Optimization: Scaling Semantic Search to Millions of Embeddings
Introduction: Vector databases are the backbone of modern AI applications—powering semantic search, RAG systems, and recommendation engines. But as your vector collection grows from thousands to millions of embeddings, naive approaches break down. Query latency spikes, memory costs explode, and recall accuracy degrades. This guide covers practical optimization strategies: choosing the right index type for […]
Read more →RAG Patterns: Advanced Retrieval Augmented Generation Strategies
Introduction: Retrieval Augmented Generation (RAG) has become the standard pattern for grounding LLM responses in factual, up-to-date information. But basic RAG—retrieve chunks, stuff into prompt, generate—often falls short in production. Queries get misunderstood, irrelevant chunks pollute context, and answers lack coherence. This guide covers advanced RAG patterns that address these challenges: query transformation to improve […]
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