AI Engineering
From Prototype to Production: Building Reliable AI Systems
Turn your AI prototype into a production-ready system with concrete strategies for contracts, fallbacks, and evaluation
Blog
Thoughts on AI engineering, backend, frontend, and building modern software.
AI Agents
Why Multi-Agent Systems Fail: Tool-Call Permissions and Approval Boundaries
Most multi-agent systems fail in production due to uncontrolled tool-call permissions. Learn how to avoid catastrophic errors with strict approval boundaries and state-aware design.
RAG & Retrieval
Why Retrieval Quality Is the Hidden Weak Link in RAG Systems
Poor retrieval quality undermines RAG systems. Learn how chunking, metadata, and stale documents sabotage AI accuracy in production.
Embeddings & Vector Search
Chunk Boundaries and Retrieval Quality: Hidden Pitfalls in Embedding Search
Discover how chunk boundaries and metadata filters can silently degrade embedding search results in production systems. Learn practical strategies to avoid these pitfalls.
Software Architecture
Decoupling AI Logic: When to Isolate Model Calls Behind Internal APIs
Decide where AI logic belongs in your architecture—frontend, backend, or platform layer—with practical examples and tradeoffs for maintainability, observability, and future-proofing.
Security
Scoped API Keys for AI Features: Preventing Unauthorized Access in Production
Scoped API keys are a critical security boundary for AI features. Learn how to implement them to prevent unauthorized access and ensure safe deployment in production systems.
Prompt Engineering
Structuring AI Outputs: How Prompt Contracts Prevent Ambiguity in Production Systems
Structured outputs as an API contract between code and models. Learn how to design reliable, predictable AI systems with clear boundaries, testable edge cases, and role separation.
MLOps
Cost Spikes from Quiet Prompt Changes: Monitoring LLM Features in Production
After deploying an LLM feature, monitoring for cost spikes from subtle prompt changes is critical. Learn how to detect and mitigate these risks in production systems.
LLMs
Structured Outputs as an API Contract: Building Predictable AI Systems
Structured outputs ensure reliable AI behavior in production. Learn how schemas, validation, and retries create predictable model behavior without relying on clever prompts.
Data Engineering
How Stale Data Sabotages AI Systems: A Data Engineering Deep Dive
Stale data silently breaks AI systems. Learn how to detect and prevent it in production data pipelines.