Introduction: Prompts are code. They determine how your LLM application behaves, and like code, they need version control, testing, and deployment pipelines. Yet many teams treat prompts as afterthoughts—hardcoded strings scattered across the codebase, changed ad-hoc without tracking. This leads to regressions, inconsistent behavior, and difficulty understanding why outputs changed. This guide covers practical prompt […]
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Knowledge Graph Integration: Structured Reasoning for LLM Applications
Introduction: Vector search finds semantically similar content, but it misses the structured relationships that make knowledge truly useful. Knowledge graphs capture entities and their relationships explicitly—who works where, what depends on what, how concepts connect. Combining knowledge graphs with LLMs creates systems that can reason over structured relationships while generating natural language responses. This guide […]
Read more →Fine-Tuning LLMs: From Data Preparation to Production Deployment
Introduction: Fine-tuning transforms a general-purpose LLM into a specialized model tailored to your domain, style, or task. While prompt engineering can get you far, fine-tuning offers consistent behavior, reduced token usage, and capabilities that prompting alone cannot achieve. This guide covers the complete fine-tuning workflow—from data preparation to deployment—using both cloud APIs (OpenAI, Together AI) […]
Read more →Inference Optimization Patterns: Maximizing LLM Throughput and Efficiency
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 […]
Read more →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 […]
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