Introduction: Semantic search goes beyond keyword matching to understand the meaning and intent behind queries. By converting text to dense vector embeddings, semantic search finds conceptually similar content even when exact words don’t match. However, naive implementations often underperform—poor embedding choices, suboptimal indexing, and lack of reranking lead to irrelevant results. This guide covers practical […]
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.
Image Classification vs Pattern Recognition vs Object Detection vs Object Tracking–A Primer
It is a common question that has been asked in all Artificial Intelligence Conference or Discussion Forums. Based on my knowledge, I thought of answering some of these questions: 1.) Image Classification (also called Image Recognition): is the process of creating a thematic image where each pixel is assigned a number representing a class / […]
Read more →Azure Cognitive Services–Experience Image Recognition using Custom Vision (Build an Harrison Ford Classifier)
Custom Vision Service as part of Azure Cognitive Services landscape of pretrained API services, provides you an ability to customize the state-of-the-art Computer Vision models for your specific use case. Using custom vision service you can upload set of images of your choice and categorize them accordingly using tags/categories and automatically train the image recognition […]
Read more →LLM Caching Strategies: Reducing Costs and Latency at Scale
Introduction: LLM API calls are expensive and slow. A single GPT-4 request can cost cents and take seconds—multiply that by thousands of users and costs spiral quickly. Caching is the most effective way to reduce both cost and latency. But LLM caching is different from traditional caching: exact string matches are rare, and semantically similar […]
Read more →Prompt Compression Techniques: Fitting More Context in Less Tokens
Introduction: Context windows are limited and tokens are expensive. Long prompts with extensive context, examples, or retrieved documents quickly hit limits and drive up costs. Prompt compression techniques reduce token count while preserving the information LLMs need to generate quality responses. This guide covers practical compression strategies: token pruning to remove low-information tokens, extractive summarization […]
Read more →Azure Cosmos DB – TTL (Time to Live) – Reference Usecase
TTL capability within Azure Cosmos DB is a live saver, as it would take necessary steps to purge redudent data based on the configurations you may. Let us think in terms of an Industrial IoT scenario, devices can produce vast amounts of telemetry information, logs and user session information that is only useful until we […]
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