Introduction: LLM APIs have strict rate limits—requests per minute, tokens per minute, and concurrent request limits. Hit these limits and your application grinds to a halt with 429 errors. Effective rate limiting isn’t just about staying under limits; it’s about maximizing throughput while maintaining reliability. This guide covers practical rate limiting patterns: token bucket algorithms […]
Read more →Category: Technology Engineering
Technology Engineering
Advanced RAG Patterns: From Naive Retrieval to Production-Grade Systems (Part 1 of 2)
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 →LLM Security: Defense Patterns for Production Applications (Part 2 of 2)
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 →LLM Latency Optimization: Techniques for Sub-Second Response Times
Introduction: LLM latency is the silent killer of user experience. Even the most accurate model becomes frustrating when users wait seconds for each response. The challenge is that LLM inference is inherently slow—autoregressive generation means each token depends on all previous tokens. This guide covers practical techniques for reducing perceived and actual latency: streaming responses […]
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 →LLM Memory Systems: Building Contextually Aware AI Applications
Introduction: Memory is what transforms a stateless LLM into a contextually aware assistant. Without memory, every interaction starts from scratch—the model has no knowledge of previous conversations, user preferences, or accumulated context. This guide covers the memory architectures that enable persistent, intelligent AI systems: conversation buffers for recent context, summary memory for long conversations, vector-based […]
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