The AI Pioneer / AI inside your software
AI inside your software
Adding AI to the app you already have: RAG, choosing a model, prompts, structured output, guardrails, cost control and evals.
- 30Adding AI to the software you already run: the four safe first features
Where to start when you already have a web app or an internal tool, the four features that almost always pay off first, the architecture in plain terms, and what to leave for the second year.
11 minute read - 31What is RAG, and why does every AI vendor say they use it
Retrieval-augmented generation explained with a library instead of a diagram: why it exists, what it fixes, what it cannot fix, and the three questions that tell you whether a vendor's RAG is real.
11 minute read - 32RAG implementation best practices: the eight parts that fail and how to build each one
Chunking, embeddings, retrieval quality, citations, freshness, permissions and evaluation, explained plainly enough to judge a vendor and specifically enough to build, plus the cheap version most businesses should start with.
11 minute read - 33Choosing an LLM provider: OpenAI, Anthropic, Google, open-weight, and how not to marry one
Quality tiers, cost, data terms, rate limits, the setup that lets you switch providers in an afternoon, and when a small cheap model is all you need. Written for the owner whose name goes on the account.
11 minute read - 34Prompt engineering for business: prompts are specifications and belong in version control
Role, rules, examples, the output format, what to do when unsure, and why the prompt lives in your code repository and not in a text box. Enough for an owner to judge the work and a developer to do it.
11 minute read - 35Structured output and tool calling: how an LLM talks to your systems without breaking them
JSON output, schemas, tool calling, validation, and why "the model said so" is never the last word before money moves. Plain enough for the owner to judge, specific enough for the developer to build.
11 minute read - 36AI hallucination guardrails: shipping AI that cannot lie to your customers
Grounding, citations, allowed-actions lists, human approval for anything irreversible, refusals, and the audit log. The layers that keep an AI feature honest, in the order to build them, and how to tell if your vendor built any.
11 minute read - 37LLM cost optimization: keeping the AI bill boring once the feature is live
Caching, model tiers (a cheap model first and the expensive one on escalation), batching, prompt length, streaming, rate limits, spending caps, and the monthly cost report. How to run AI features without a surprise invoice.
11 minute read - 38How to know an AI feature works before your customers find out it does not
What an eval is, how to build a golden set from your own real cases, how to grade answers with rules and with a second model, and the go-live gate that stops a bad AI feature from shipping.
11 minute read
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