Practical articles on AI, DevOps, Cloud, Linux, and infrastructure engineering.
Embedding indexes degrade silently. The signals that catch drift, how often to re-embed, and the operational patterns we built after one quiet quality regression.
Streaming LLM responses is easy until the client disconnects, the model stalls, or the user cancels. The patterns that keep streaming responsive without leaking spend.
When LLMs can call tools that change real state, the design decisions that matter most are about what's gated, what's automatic, and what triggers a human checkpoint.
Embeddings turn text into numbers a computer can compare. Here's the working mental model, a runnable Python example, and where embeddings fit in real apps.
A hands-on intro to prompt engineering. Learn the four levers (role, format, examples, constraints) and watch a vague prompt turn into a reliable one.
A working retrieval-augmented generation app you can run today. Markdown ingestion, embeddings, semantic search, and an LLM answer — start to finish in one afternoon.
We've shipped all three patterns to production. They're not interchangeable. Here's the framework we now use to decide which approach fits a given task.
We invalidate ~6% of LLM outputs before they reach a downstream system. Here's how we structure prompts and validators to catch malformed responses early.
We ran the same RAG workload across three vector stores for a quarter each. Here's what we learned about latency, cost, and operational overhead.
We ran the same workload on both for half a year. The break-even point isn't where most blog posts say it is — and the latency story has more nuance than throughput-per-dollar charts admit.
Six months running RAG in production taught us that the retrieval step matters far more than the model. Concrete techniques that moved the needle, with before/after numbers.
Battle-tested prompt patterns from running LLM features in production: structured output, chain-of-thought, and graceful failure handling.
