Tools
Products, apps, dev tools, and workflows
Guardrails: filtering inputs and outputs around an LLM
A model alone is not a safe product. Guardrails are the input and output filters that keep an LLM inside the boundaries you actually need.
Document parsing for AI: PDFs, tables, and the messy rest
Before a model can reason over your documents, something has to turn them into clean text. That unglamorous step quietly decides everything downstream.
Streaming responses: why and how it helps UX
Streaming does not make a model faster — it makes the wait feel shorter. Here is why that matters and what it costs you to build.
Choosing an embedding model for your project
Picking an embedding model is less about leaderboards than fit. Here is what actually decides whether retrieval works for your data and your budget.
Choosing an AI coding assistant: a sober comparison framework
AI coding assistants all demo beautifully. Here is a framework for judging them on the things that actually matter to your day-to-day work.
The modern AI app stack, end to end
A clear map of the layers that make up a real AI application — model, orchestration, retrieval, evaluation, and the unglamorous glue that holds it together.
Choosing between an API and self-hosting your LLM
Call a hosted API or run the model yourself? The honest answer depends on volume, control, and how much operations work you can absorb.
Structured output: getting reliable JSON from models
When your code needs data, not prose, the model has to return clean, parseable structure. Here is how to get reliable JSON instead of hope.
Vector databases without the hype: what they do and when you need one
Vector databases became a buzzword overnight. Here is what they actually do, the problem they solve, and the honest signs you do or do not need one.
Observability for LLM apps: logging what matters
When an LLM app misbehaves, "it gave a bad answer" is not a debuggable fact. Here is what to log so you can actually find out why.
Prompt management: keeping prompts out of your code
Hardcoded prompts feel fine until you have a dozen scattered across files. Here is how to treat prompts as managed assets, not buried strings.
Running LLMs locally: a practical primer for a single laptop
You can run a capable open-weight model on one laptop today. Here is what actually determines whether it works — memory, quantization, tooling — and honest expectations for each.
Function calling and tools: connecting models to actions
Function calling lets a model decide to use your code — without ever running it. Here is what actually happens, and where it goes wrong.
Caching LLM responses: when and how
Caching can cut LLM cost and latency dramatically — or quietly serve stale, wrong answers. Here is how to tell the difference and do it safely.
Evaluating AI tools: a checklist that survives the demo
AI tools are designed to dazzle in a demo. This checklist helps you judge them on the durable questions that decide whether they hold up in real use.
Build vs buy: when to use an AI platform
Assemble your own AI stack or adopt a platform that bundles it? The answer turns on where your real advantage lives — and where it does not.
Rate limits and retries: building resilient LLM calls
Hosted LLMs fail in ordinary ways — limits, timeouts, transient errors. A little retry discipline turns a fragile integration into a dependable one.