Confidential technical evaluation
Confidential AI/Technical Evaluation
A structured investigation of tools, hardware, software, and AI approaches for an early-stage internal project whose direction was still being formed.
- My role
- Technical research and evaluation contributor
- Context
- Porsche Digital internship, confidential internal project
- Reading time
- 4 min read
01
Working before the architecture was settled
The project began before the team had a final technical direction. At that stage, the useful work was not implementing a large system quickly. It was reducing uncertainty without hiding the assumptions behind a recommendation.
I researched and benchmarked more than ten LLM approaches, AI tools, and hardware options. The subject, candidates, findings, and decision are confidential, but the evaluation method is transferable.
02
Comparing options on the same questions
A comparison only helps when each option is tested against a consistent set of needs. I organised findings around what an option could do, what it required, where it failed, how difficult it was to integrate, and what remained unknown.
Selected options were tested rather than judged only from documentation. That made it possible to separate a promising claim from behavior the team could reproduce in its own environment.
03
Writing for a decision, not for volume
I documented observations and prepared internal summaries that made tradeoffs visible. A useful report needed to state the evidence, the limitation, and the consequence for the next decision. A long feature list without that link would have added reading without reducing risk.
Where evidence was incomplete, I kept it incomplete. Early technical evaluation is full of gaps, and pretending otherwise can push a team into an architecture for the wrong reason.
04
What I carried into later projects
This work sharpened a habit I now use in AI and platform projects: define the decision before comparing tools. The right option depends on operating constraints, not on which demo looks most impressive.
It also taught me to make uncertainty easy to review. A recommendation becomes stronger when another engineer can see which parts were tested, which were inferred, and which still need an experiment.
Outcomes
- 01A reproducible evaluation process for narrowing early technical options.
- 02Test notes and internal summaries that made tradeoffs and open questions visible.
- 03Evidence that supported the team as it shaped the next technical direction.
What I learned
- 01Tool evaluation should begin with the decision and constraints, not with a product list.
- 02Small tests are more useful than confident assumptions when architecture is still fluid.
- 03A good technical summary preserves uncertainty instead of sanding it away.