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AI Ops

Running AI systems in production

Testing AI Agents: How to Know Your Agent Actually Works

AI Ops

A practical guide to testing AI agents: unit/integration/e2e tests, eval frameworks (DeepEval 4.0, RAGAS, Promptfoo, Braintrust), metrics, CI/CD pipeline. With code examples.

Context Engineering vs RAG: When to Use Which

AI Ops

A deep comparison of context engineering and RAG: when long context replaces retrieval, when you actually need RAG, a decision framework, and the modern 2026 stack.

AI Agents: The Complete Guide — What They Are, How to Build Them, Where to Use Them

AI Ops

A comprehensive guide to AI agents: architecture, frameworks (LangGraph, CrewAI, OpenAI Agents SDK, Google ADK, Pydantic AI, MCP), practical use cases, and a hands-on tutorial.

Prompt Library Template: Role → Context → Task → Constraints → Format

Tutorials AI Ops

A 5-component prompt template for building a reusable library. Structure, examples for different tasks, plus organization and versioning of a prompt library.

AI Cost Optimization: How to Cut LLM Spending by 60% Without Losing Quality

Cases & Practice AI Ops

Five techniques to cut LLM API costs: prompt caching, smart routing, Batch API, semantic caching, model downsizing. Before/after tables, prompts, and configs.

Human-in-the-Loop for AI Products: When the Model Decides and When a Person Does

Tutorials AI Ops

A decision-making framework for HITL in AI products: confidence thresholds, risk matrix, escalation patterns. Production-ready implementation examples with code.

Prompt A/B Testing: a scientific approach to improving AI response quality

Tutorials AI Ops

Methodology for A/B testing prompts: quality metrics, statistical significance, tools (Langfuse, DeepEval). Step-by-step guide from hypothesis to production decision.

Prompt Engineering System: Managing 50+ Prompts in Production

Tutorials AI Ops

How to build a prompt management system: versioning, testing, A/B deployment, regression monitoring. Practical patterns and tools for production.

Multi-Agent Architecture Patterns: When One AI Isn't Enough

Tutorials AI Ops

Multi-agent system architecture patterns for production: Sequential Pipeline, Parallel Fan-Out, Classifier+Router orchestration, task routing, agent specialization with code examples.

LLM-as-Judge: Automated Quality Gate for LLM Outputs in Production

Tutorials AI Ops

How to use LLM-as-Judge for automated LLM output evaluation. Metrics, judge prompts, DeepEval, Langfuse integration, and CI/CD pipeline setup.