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Architecture Review: Prompt Versioning and Regression Testing

Prompt Versioning and Regression Testing. Practical guidance for reliable, scalable platform operations.

Production AI Pipelines: Building End-to-End ML Systems

Learn how to build production-ready AI pipelines from data ingestion to model serving. Complete architecture guide with MLOps best practices.

Architecture Review: LLM Gateway Design for Multi-Provider Inference

LLM Gateway Design for Multi-Provider Inference. Practical guidance for reliable, scalable platform operations.

AI Security and Safety: Protecting Your AI Applications

Learn how to secure AI applications against prompt injection, data leakage, and adversarial attacks. Best practices for AI security in production.

Real-World RAG Incidents: Lessons from a Production Rollout

A field report from rolling out retrieval-augmented generation in production, including cache bugs, bad embeddings, and how we fixed them.

Kiril urbonas

Embedding Models Comparison: Choosing the Right Model for Your Use Case

Compare popular embedding models including OpenAI, Sentence-BERT, and open-source alternatives. Learn which model fits your RAG, search, or similarity tasks.

AI Cost Optimization: Reducing LLM Inference Costs by 80%

Learn proven strategies to reduce AI inference costs including model quantization, caching, batching, and efficient prompt design. Real-world cost savings examples.

Fine-tuning vs Few-Shot Learning: When to Use Each Approach

Compare fine-tuning and few-shot learning for adapting LLMs. Learn when to use each approach and their trade-offs in terms of cost, performance, and complexity.

Real-World RAG Incidents: Lessons from a Production Rollout

A field report from rolling out retrieval-augmented generation in production, including cache bugs, bad embeddings, and how we fixed them.

Kiril urbonas

AI Observability and Monitoring: Tracking Model Performance in Production

Learn how to monitor AI models in production. Track performance, detect drift, and ensure model reliability with comprehensive observability strategies.

Multi-Agent AI Systems: Building Collaborative AI Applications

Learn how to build multi-agent AI systems where multiple AI agents collaborate to solve complex tasks. Architecture patterns and implementation guide.