FROM THE LAB

Insights

Technical deep-dives on AI systems engineering. Real patterns from production deployments.

AGENTIC AI•12 min read

Why Agent Orchestration is Harder Than It Looks

Most agentic systems fail not because of bad models, but because of poor orchestration design. We break down the architectural patterns that actually work in production, from single agents to hierarchical swarms.

Neumyth Engineering • Jan 15, 2025
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RAG SYSTEMS8 min read

The Hidden Costs of Naive RAG Implementation

Chunking, embedding, and retrieval strategies can make or break your RAG system. Here's what most teams get wrong.

Jan 10, 2025
LLM OPS10 min read

Fine-tuning vs Prompt Engineering: When to Use Each

Not every problem needs a fine-tuned model. A cost-benefit analysis and decision framework for enterprise AI teams.

Jan 5, 2025
ORCHESTRATION15 min read

Building Reliable AI Systems with MCP

Model Context Protocol (MCP) is changing how we build AI orchestration layers. Here's what we've learned deploying it at scale.

Dec 28, 2024
PROMPT ENGINEERING11 min read

Systematic Prompt Testing: Beyond Trial and Error

Prompt engineering needs the same rigor as software engineering. Our framework for evaluation, versioning, and testing.

Dec 20, 2024
AGENTIC AI14 min read

Multi-Agent Systems: Coordination Patterns That Scale

From simple task delegation to complex agent hierarchies, the patterns we use to coordinate autonomous agents in production.

Dec 15, 2024
LLM OPS9 min read

Cost Optimization for LLM Inference at Scale

Strategies for reducing inference costs by 60% without sacrificing quality: caching, routing, and smart fallbacks.

Dec 8, 2024