Introduction: Fine-tuning transforms general-purpose LLMs into specialized models that excel at your specific tasks. While prompting can get you far, fine-tuning unlocks capabilities that prompting alone cannot achieve: consistent output formats, domain-specific knowledge, reduced latency from shorter prompts, and behavior that would require extensive few-shot examples. This guide covers the practical aspects of LLM fine-tuning: […]
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Running LLMs on Kubernetes: Production Deployment Guide
Deploying LLMs on Kubernetes requires careful planning. After deploying 25+ LLM models on Kubernetes, I’ve learned what works. Here’s the complete guide to running LLMs on Kubernetes in production. Figure 1: Kubernetes LLM Architecture Why Kubernetes for LLMs Kubernetes offers significant advantages for LLM deployment: Scalability: Auto-scale based on demand Resource management: Efficient GPU and […]
Read more →Batch Inference Optimization: Maximizing Throughput and Minimizing Costs
Introduction: Batch inference optimization is critical for cost-effective LLM deployment at scale. Processing requests individually wastes GPU resources—the model loads weights once but processes only a single sequence. Batching multiple requests together amortizes this overhead, dramatically improving throughput and reducing per-request costs. This guide covers the techniques that make batch inference efficient: dynamic batching strategies, […]
Read more →LLM Monitoring and Alerting: Building Observability for Production AI Systems
Introduction: LLM monitoring is essential for maintaining reliable, cost-effective AI applications in production. Unlike traditional software where errors are obvious, LLM failures can be subtle—degraded output quality, increased hallucinations, or slowly rising costs that go unnoticed until the monthly bill arrives. Effective monitoring tracks latency, token usage, error rates, output quality, and cost metrics in […]
Read more →Structured Output from LLMs: JSON Mode, Function Calling, and Pydantic Patterns (Part 1 of 2)
Introduction: Getting reliable, structured data from LLMs is one of the most practical challenges in building AI applications. Whether you’re extracting entities from text, generating API parameters, or building data pipelines, you need JSON that actually parses and validates against your schema. This guide covers the evolution of structured output techniques—from prompt engineering hacks to […]
Read more →Context Compression Techniques: Fitting More Information into Limited Token Budgets
Introduction: Context window limits are one of the most frustrating constraints when building LLM applications. You have a 100-page document but only 8K tokens of context. You want to include conversation history but it’s eating into your prompt budget. Context compression techniques solve this by reducing the token count while preserving the information that matters. […]
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