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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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 […]
Read more →LLM Inference Optimization: KV Cache, Quantization, and Speculative Decoding (Part 2 of 2)
Introduction: LLM inference optimization is the art of making models respond faster while using fewer resources. As LLMs grow larger and usage scales, the difference between naive and optimized inference can mean 10x cost reduction and sub-second latencies instead of multi-second waits. This guide covers the techniques that matter most: KV cache optimization to avoid […]
Read more →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 →Streaming LLM Responses: Building Real-Time AI Applications (Part 2 of 2)
Introduction: Waiting 10-30 seconds for an LLM response feels like an eternity. Streaming changes everything—users see tokens appear in real-time, creating the illusion of instant response even when generation takes just as long. Beyond UX, streaming enables early termination (stop generating when you have enough), progressive processing (start working with partial responses), and better error […]
Read more →GraphQL for AI Services: Flexible Querying for LLM Applications
GraphQL provides flexible querying for LLM applications. After implementing GraphQL for 15+ AI services, I’ve learned what works. Here’s the complete guide to using GraphQL for AI services. Figure 1: GraphQL Architecture for AI Services Why GraphQL for AI Services GraphQL offers significant advantages for AI services: Flexible queries: Clients request exactly what they need […]
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