AI Engineering Insights: Page 3

Synchronous, streaming and async job response patterns for AI-powered APIs

Designing AI-Powered APIs: Patterns and Pitfalls

Learn practical patterns for building reliable AI-powered APIs, from streaming responses and validation to observability and cost control.

Engineering Production AI

18 Mar, 20262 min read

Orchestrator delegating to researcher, writer and validator agents over a shared message log

Building a Multi-Agent AI System That Actually Works in Production

What the multi-agent AI demos leave out: probabilistic failures, state management, cost control, and how to make autonomous agents reliable in production.

Agents Engineering

15 Mar, 20262 min read

Cost levers alongside the break-even point where self-hosted inference beats API pricing

GPU Inference on a Budget: Optimizing Costs for Self-Hosted Models

Optimize the cost of self-hosted GPU inference with practical guidance on model serving, utilization, batching, and infrastructure choices.

Infrastructure Cost Optimization

15 Mar, 20262 min read

Versioned prompt templates moving through a test suite and canary rollout into production

Prompt Engineering at Scale: Moving Beyond Hacks

Move beyond ad hoc prompts with versioning, testing, observability, and systems that make prompt engineering reliable at scale.

LLM Engineering

10 Mar, 20262 min read

Comparison of Pinecone, Weaviate, Qdrant and pgvector across operations, scale, filtering and cost

Vector Databases Explained: Choosing the Right One for Your AI Application

Compare vector database options for production AI applications, including performance, scalability, cost, and operational trade-offs.

Infrastructure LLM

05 Mar, 20262 min read