Introduction: Embeddings are the foundation of modern AI applications—they transform text, images, and other data into dense vectors that capture semantic meaning. Understanding how embedding models work, their strengths and limitations, and how to choose between them is essential for building effective search, RAG, and similarity systems. This guide covers the landscape of embedding models:… Continue reading
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.
Azure: What are Event Hubs?
Event Hubs is a feature within the Azure and is intended to help with the challenge of handling an event based messaging at huge scale. To be specific it is a Highly scalable data streaming platform. The idea is that if you have apps or devices publishing telemetry events then Event Hubs can be the… Continue reading
Prompt Optimization Strategies: From Structure to Automatic Refinement
Introduction: Prompt optimization is the systematic process of improving prompts to achieve better LLM outputs—higher accuracy, more consistent formatting, reduced latency, and lower costs. Unlike ad-hoc prompt engineering, optimization treats prompts as artifacts that can be measured, tested, and iteratively improved. This guide covers the techniques that make prompts more effective: structural patterns that improve… Continue reading
Scalability – Scale Out/In vs Scale Up/Down (Horizontal Scaling vs Vertical Scaling)
When you work with Cloud Computing or normal Scalable highly available applications you would normally hear two terminologies called Scale Out and Scale Up or often called as Horizontal Scaling and Vertical Scaling. I thought about covering basics and provide more clarity for developers and IT specialists. What is Scalability? Scalability is the capability of… Continue reading
LLM Inference Optimization: From KV Cache to Speculative Decoding
Introduction: LLM inference optimization is the art of making models respond faster while using fewer resources. As LLMs grow larger and usage scales, the difference between naive and optimized inference can mean 10x cost reduction and sub-second latencies instead of multi-second waits. This guide covers the techniques that matter most: KV cache optimization to avoid… Continue reading
Redis Cache–Azure Plans
Azure Redis Cache, a secure data cache based on Open source Redis Cache, which will provide you a fully managed/serviced instance from Microsoft. Means you don’t have to bear the burden of managing the server/software patches etc.. What is Redis Cache? Redis is an open source (BSD licensed), in-memory data structure store, used as a… Continue reading
Knowledge Distillation: Transferring Intelligence from Large to Small Models
Introduction: Knowledge distillation transfers the capabilities of large, expensive models into smaller, faster ones that can run efficiently in production. Instead of training a small model from scratch, distillation leverages the “dark knowledge” encoded in a teacher model’s soft probability distributions—information that hard labels alone cannot capture. This guide covers the techniques that make distillation… Continue reading
Semantic Caching Strategies: Reducing LLM Costs Through Intelligent Query Matching
Introduction: Semantic caching revolutionizes how we handle LLM requests by recognizing that similar questions deserve similar answers. Unlike traditional exact-match caching, semantic caching uses embeddings to find queries that are semantically equivalent, returning cached responses even when the wording differs. This can reduce LLM API costs by 30-70% while dramatically improving response latency for common… Continue reading
Vector Search Algorithms: From Brute Force to HNSW and Beyond
Introduction: Vector search is the foundation of modern semantic retrieval systems, enabling applications to find similar items based on meaning rather than exact keyword matches. Understanding the algorithms behind vector search—from brute-force linear scan to sophisticated approximate nearest neighbor (ANN) methods—is essential for building efficient retrieval systems. This guide covers the core algorithms that power… Continue reading
LLM Routing and Load Balancing: Optimizing Cost and Performance Across Model Fleets
Introduction: LLM routing and load balancing are critical for building cost-effective, reliable AI systems at scale. Not every query needs GPT-4—many can be handled by smaller, faster, cheaper models with equivalent quality. Intelligent routing analyzes incoming requests and directs them to the most appropriate model based on complexity, cost constraints, latency requirements, and current system… Continue reading