Guardrails and Safety Filters: Protecting LLM Applications from Harmful Content

Introduction: LLMs can generate harmful, biased, or inappropriate content. They can be manipulated through prompt injection, jailbreaks, and adversarial inputs. Production applications need guardrails—safety mechanisms that validate inputs, moderate content, and filter outputs before they reach users. This guide covers practical guardrail implementations: input validation to catch malicious prompts, content moderation using classifiers and LLM-based […]

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Semantic Search Optimization: Building High-Quality Retrieval Systems

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 […]

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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 […]

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C# 8.0 New Feature–Interface Default Implementation for Methods

With upcoming C# 8.0, there is an interesting feature called default implementation body for methods within an interface definition. That means if you have few methods signatures defined and you want make implementation classes to implement these methods optionally (remember, previously all interface methods needs to be implemented in implementation classes) , with C# 8.0, […]

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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 […]

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