I write about the messy reality of building through unfamiliar problems. RAG pipelines, LLM evaluation, voice agents, cost optimization, and what actually worked in production.
No spam. Just shipping stories.
Build logs, technical deep-dives, and lessons from production.
Adding AI to a product is easy. Deciding whether to keep it is hard. Here is the decision framework I use for AI feature lifecycle: when to add, how to measure, and when to kill.
Most teams add guardrails after the first incident. By then, data has leaked or the agent ran up a $2,000 API bill. This is the guide for building guardrails from day one.
Traditional observability was built for deterministic systems. AI systems are probabilistic -- same input, different output. Here is how I built monitoring that actually works for LLM-powered production systems.
Most agent tutorials show a toy ReAct loop that works on 3 test cases. Production agents need tool boundaries, retry logic, cost caps, and human-in-the-loop checkpoints. This is the playbook I use.
Most teams treat fine-tuning and RAG as alternatives. They are not. They solve different problems, cost differently, and sometimes you need both. Here is the decision framework I use in production.
The vector database market wants you to adopt Pinecone or Weaviate. For most teams, Postgres with pgvector eliminates an entire service from your stack -- and performs within 5% of dedicated solutions at 1M vectors.
Most RAG prototypes cost $2-5 per query. At 10,000 queries/day, that is $360K/year -- for a single feature. I cut retrieval costs by 99% in production. Here is the four-strategy playbook with real before/after numbers.
We don't deploy code without tests. Why are we deploying AI with nothing but gut feelings? Here is the eval harness I use to catch hallucinations before users do -- with code, CI/CD gates, and the reliability flywheel that lifted impressions 482%.
Vendor lock-in in AI is existential. One pricing change rewrites your unit economics overnight. Here is the three-layer architecture pattern that makes provider choice a routing decision -- with TypeScript code, real cost breakdowns, and the judgment call on when NOT to abstract.
The API times out Thursday night. The model hallucinates a legal citation. The bill arrives at 3x forecast. The problem was never the model -- it was that nobody designed the system. Here is the production engineering mindset that fixes it.
It knows my projects, architecture decisions, and the trade-offs behind every system I've shipped. Ask it anything.