Introduction: LLM inference is expensive—both in compute and latency. Every token generated requires a forward pass through billions of parameters, and users expect responses in seconds, not minutes. Inference optimization techniques reduce costs and improve responsiveness without sacrificing output quality. This guide covers practical optimization strategies: batching requests to maximize GPU utilization, managing KV caches […]
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Model Routing Strategies: Intelligent Request Distribution Across LLMs
Introduction: Not every request needs GPT-4. Simple questions can be handled by smaller, faster, cheaper models, while complex reasoning tasks benefit from more capable ones. Model routing intelligently directs requests to the most appropriate model based on task complexity, cost constraints, latency requirements, and quality needs. This approach can reduce costs by 50-80% while maintaining […]
Read more →Conversation Memory Patterns: Building Stateful LLM Applications
Introduction: LLMs are stateless—each request starts fresh with no memory of previous interactions. Building conversational applications requires implementing memory systems that maintain context across turns while staying within token limits. The challenge is balancing completeness (keeping all relevant context) with efficiency (not wasting tokens on irrelevant history). This guide covers practical memory patterns: buffer memory […]
Read more →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 […]
Read more →Testing LLM Applications: Unit Tests, Integration Tests, and Evaluation
Introduction: Testing LLM applications presents unique challenges compared to traditional software. Outputs are non-deterministic, quality is subjective, and the same input can produce different but equally valid responses. This guide covers practical testing strategies: unit testing with mocked LLM responses, integration testing with real API calls, evaluation frameworks for quality assessment, and regression testing to […]
Read more →RESTful AI API Design: Best Practices for LLM APIs
Designing RESTful APIs for LLMs requires careful consideration. After building 30+ LLM APIs, I’ve learned what works. Here’s the complete guide to RESTful AI API design. Figure 1: RESTful AI API Architecture Why LLM APIs Are Different LLM APIs have unique requirements: Async operations: LLM inference can take seconds or minutes Streaming responses: Need to […]
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