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About

Hi, I’m Sarath. I design systems that are simple to run and hard to break.

[Two or three sentences on your background: where you started, the kinds of systems you have built, and how you moved into AI architecture.]

[One or two sentences on why you work freelance and the clients you do your best work with.]

SP

[Headshot goes here]

Principles

How I design systems

Measure before you tune

An AI feature without an evaluation set is a guess. I build the eval set first, then change prompts and models against it.

Boring where possible

Proven databases and plain services first. New tools have to earn their place with a clear win.

Decisions in writing

Every significant choice gets a short decision record, so your team knows why, not just what.

Cost is a requirement

Token spend, latency and infrastructure cost are designed for up front, with budgets and alerts.

Leave the team stronger

Handover is part of the job: docs, runbooks and pairing until your engineers own it.

Ship in small slices

Working software every week beats a big reveal. Weekly demos keep scope honest.

Under the hood

How this site’s assistant works

Every time the site is published, a script collects the text of each page into one knowledge file. When you ask a question, a small server function sends it to the model together with that file, and the model must answer only from it and cite the page.

With a few dozen pages, the whole site fits in one prompt, so there is no vector database to run. Prompt caching keeps the cost to a few dollars a month, and a rate limit caps it.

  1. On publish

    Pages → knowledge file

  2. On question

    Rate limit → question + knowledge → model

  3. On answer

    Streamed reply with links to source pages

  4. Always

    Off-topic refusals, no invented facts, monthly spend cap

Credentials

Certifications and talks

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