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) […]
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Multi-Agent Systems and Orchestration – Part 3 of 5
Master multi-agent systems with ADK: coordinator-worker, pipeline, and hierarchical patterns. Build a production research assistant that coordinates 6 specialized agents for comprehensive analysis.
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 →AI Security Best Practices: Beyond Prompt Injection
Last year, our AI application was compromised. Not through prompt injection—through model extraction. An attacker downloaded our fine-tuned model in 48 hours. After securing 20+ AI applications, I’ve learned that prompt injection is just the tip of the iceberg. Here’s the complete guide to AI security beyond prompt injection. Figure 1: AI Security Threat Landscape […]
Read more →Hugging Face Transformers: The Complete Guide to Open-Source AI Model Deployment
Introduction: Hugging Face Transformers has become the de facto standard library for working with transformer-based models. With access to over 500,000 pre-trained models and 150,000 datasets through the Hugging Face Hub, it provides the most comprehensive ecosystem for deploying open-source AI models. Whether you’re running Llama, Mistral, or fine-tuning your own models, Transformers offers a […]
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 […]
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