Build a small evaluation set before deploying any AI system. Twenty to fifty examples with defined correctness criteria catches regressions that manual testing misses every time.

Replace fragile string parsing with schema-validated structured outputs. Use libraries like instructor to enforce JSON schemas and eliminate a whole class of production failures.

Resilient AI pipelines require idempotency, structured outputs, circuit breakers, and observability. Learn the patterns that keep agent systems running in production.

Prompt engineering in 2026: structural clarity and chain-of-thought still work, magic phrasing does not. Here is what practitioners should focus on for modern language models.

Local AI in 2026 is production-ready for most use cases. Explore the current state of open weights models, inference runtimes, and hardware accessibility for self-hosted deployments.

BuildWithAgents was born from a real production problem and a lack of practical resources. Jordan Reyes shares the story behind the site and what you can expect to find here.

Jordan Reyes is a full-stack developer turned AI agent specialist. Learn practical implementation patterns for RAG, agent orchestration, and local model deployment.

Run AI models on your own hardware. Our Local Models Deployment Guide covers quantization, inference runtimes, OpenAI-compatible APIs, and production monitoring.

The No-Code AI Automation Playbook is live. Build intelligent automations with n8n, Make, and Zapier using AI nodes, agent loops, and real-world workflow templates.

Our most comprehensive RAG guide is here. Learn embedding strategies, hybrid search, reranking, and evaluation frameworks for production-grade retrieval systems.