How I think as an AI engineer

High-level notes on my reasoning, tradeoffs, and process.

Introduction

This page explains how I approach problems when there is no clear solution yet.
Not the tools I use. Not the buzzwords I know.
But how I reason, decide, and move from ambiguity to impact.

Projects show what I built.
This shows how I think.

When someone works with me, this is the mental model they are hiring.

How I Approach an Unknown Problem

When I encounter a new problem, I start by slowing down instead of rushing to solutions.

My first goal is not to build.
My first goal is to understand.

I ask four foundational questions:

  • What is the real problem behind the request
  • Who experiences this problem daily
  • What happens if this problem is not solved
  • What does success actually look like in measurable terms

I deliberately avoid assuming the solution early.
Most bad systems come from solving the wrong problem very well.

I break ambiguity into constraints, signals, and unknowns.
Only after this do I move forward.

How I Define the Problem Clearly

Before writing code, I write clarity.

I convert vague goals into concrete statements such as:

  • This system should reduce manual effort by a measurable percentage
  • This feature should save users time on a recurring basis
  • This model should improve accuracy without increasing operational cost beyond limits

If a problem cannot be explained simply, it is not understood deeply enough.

Clarity reduces waste.
It also prevents overengineering.

How I Decide MVP vs Scale

I treat MVP and scale as two different mindsets, not stages.

For MVP, my priorities are:

  • Fast feedback
  • Minimal surface area
  • Clear learning signals

I intentionally accept technical imperfections if they help me learn faster.

For scale, my priorities shift to:

  • Reliability
  • Cost predictability
  • Observability
  • Long term maintainability

I do not design for scale on day one unless constraints demand it.
Premature scale creates hidden complexity.

I earn the right to scale through usage and validation.

How I Choose Models, Tools, and Tech Stack

I choose tools based on constraints, not trends.

My decision framework is simple:

  • What problem does this tool solve better than alternatives
  • What are its operational and maintenance costs
  • How easy is it to debug, monitor, and replace
  • How well does it integrate with the rest of the system

For AI and ML systems, I avoid overpowered models unless they are justified.

I prefer:

  • The simplest model that meets accuracy requirements
  • The cheapest system that meets latency expectations
  • The most transparent approach that allows debugging

Complexity is a liability unless it creates real leverage.

How I Think About Trade-offs

Every engineering decision involves trade-offs.

  • Speed vs quality
  • Accuracy vs cost
  • Flexibility vs simplicity
  • Short term delivery vs long term health

I do not try to eliminate trade-offs.
I make them explicit.

I document why a decision was made and what was sacrificed.
This allows future iterations to be intentional instead of reactive.

Good engineering is not about perfect decisions.
It is about reversible decisions made consciously.

How I Validate Outcomes

I do not trust assumptions.
I trust feedback loops.

For validation, I focus on:

  • User behavior, not opinions
  • Metrics tied directly to the problem
  • Failure cases, not only success cases

For AI systems, I pay close attention to:

  • Edge cases
  • Confidence calibration
  • Model drift over time
  • Human override and review mechanisms

If something cannot be measured or observed, it cannot be improved reliably.

How I Handle Failure and Uncertainty

Failure is part of any meaningful system.

When something breaks or underperforms, I ask:

  • What signal did we miss
  • What assumption was incorrect
  • What constraint changed

I avoid blaming tools, models, or people.
I look for flaws in the reasoning process.

Every failure is feedback about how I think.
That feedback is valuable.

How I Think About Impact

I optimize for real-world impact, not cosmetic output.

Impact means:

  • Users save time
  • Teams move faster
  • Costs reduce
  • Decisions improve

A system that looks impressive but is not used is a failure.
A simple system that solves a real pain point is a success.

I value usefulness over novelty.

My Engineering Philosophy

I believe good engineers are defined by judgment, not syntax.

Code is temporary.
Reasoning scales.

My goal is to build systems that are:

  • Understandable
  • Adaptable
  • Measurably useful

If someone reads this page and understands how I think, then this page has done its job.

Conclusion

This is how I approach problems, decisions, and systems.

If you are hiring me, working with me, or evaluating my work,
this is the thinking you get alongside the code.

Everything else is just implementation.

~ Vansh Garg