Introduction: Retrieval-Augmented Generation (RAG) has become the go-to architecture for building LLM applications that need access to private or current information. By retrieving relevant documents and including them in the prompt, RAG grounds LLM responses in factual content, reducing hallucinations and enabling knowledge that wasn’t in the training data. But naive RAG implementations often disappoint—the […]
Read more →Category: Artificial Intelligence(AI)
LLM Security: Defending Against Prompt Injection and Data Leakage
Introduction: LLM applications face unique security challenges—prompt injection, data leakage, jailbreaking, and harmful content generation. Traditional security measures don’t address these AI-specific threats. This guide covers defensive techniques for production LLM systems: input sanitization, prompt injection detection, output filtering, rate limiting, content moderation, and audit logging. These patterns help you build LLM applications that are […]
Read more →Introduction to Generative AI: A Comprehensive Guide
The first time I watched a generative model produce coherent text from a simple prompt, I knew we had crossed a threshold that would reshape how we build software. After two decades of working with various AI and ML systems, from rule-based expert systems to deep learning pipelines, I can say with confidence that generative […]
Read more →Embedding Strategies: Model Selection, Batching, and Long Document Handling
Introduction: Embeddings are the foundation of semantic search, RAG systems, and similarity-based applications. Choosing the right embedding model and strategy significantly impacts retrieval quality, latency, and cost. Different models excel at different tasks—some optimize for semantic similarity, others for retrieval, and some for specific domains. This guide covers practical embedding strategies: model selection based on […]
Read more →Structured Output from LLMs: JSON Mode, Function Calling, and Instructor
Introduction: Getting LLMs to return structured data instead of free-form text is essential for building reliable applications. Whether you need JSON for API responses, typed objects for downstream processing, or specific formats for data extraction, structured output techniques ensure consistency and parseability. This guide covers the major approaches: JSON mode, function calling, the Instructor library, […]
Read more →LLM Testing and Evaluation: Building Confidence in AI Applications
Introduction: LLM applications are notoriously hard to test. Outputs are non-deterministic, “correct” is often subjective, and traditional unit tests don’t apply. Yet shipping untested LLM features is risky—prompt changes can break functionality, model updates can degrade quality, and edge cases can embarrass your product. This guide covers practical testing strategies: building evaluation datasets, implementing automated […]
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