How recommendations are made

Evidence in.
A useful answer out.

Ranking, writing, and fact-checking are separate jobs. No single model gets to choose a winner and justify itself.

The full process

Six checks between source data and a recommendation

  1. 1

    Catalog

    prices, context, capabilities

  2. 2

    Evidence

    benchmarks and developer reports

  3. 3

    Task score

    different job, different weights

  4. 4

    Write

    explain the fixed order

  5. 5

    Verify

    check every cited claim

  6. 6

    Publish

    all at once, or not at all

There is no overall winner

The question changes the scoreboard.

A coding agent needs proof on real repository work. A bulk extractor needs acceptable quality at a price that scales.

Coding agent

Real bug fixes 40%Agent tasks 40%Usage 20%

Cheapest capable

Price 70%Text quality 20%Usage 10%

Massive context

Context size 60%Text quality 40%

Built-in guardrails

The explanation cannot change the ranking.

Score

  1. 1Model A
  2. 2Model B
  3. 3Model C

The current data fixes the order.

Explain

The writer receives only the chosen models and evidence.

Verify

Unsupported claims fail before publication.

The practical takeaway

Start with the top pick. Test the shortlist.

The ranking narrows the field. Your prompts, latency target, and failure budget make the final call.

See a live recommendationRead the methodology