Applied AI Systems
Know when a trained model is the right answer, and when it is an expensive detour.
Choose between prompting, retrieval, fine-tuning, and purpose-built models on cost, latency, and evidence.
Most problems presented as model-training problems are not. A frontier model with the right context and evaluation will often outperform a hastily trained one, reach production sooner, and stay adaptable as the requirement moves. The cases where training genuinely wins are real, but narrower than the market suggests.
This is where budgets disappear. A team commits to fine-tuning before establishing what correct looks like, then discovers the labeled data does not exist, the objective is still ambiguous, and the resulting model is harder to change than the prompt it replaced.
Model Selection & Training makes that decision explicit. It gives the team a clearer basis for choosing among prompting, retrieval, fine-tuning, smaller purpose-built models, and classical methods such as classification and ranking, using volume, latency floors, cost floors, and the availability of ground truth as the deciding inputs. What matters next is picking the lightest approach that meets the standard, and knowing when that is no longer enough.
Let’s get going
- Establish correct before choosing an approach — Build the evaluation set first. Without ground truth, no training decision can be justified and no improvement can be proven.
- Price the alternatives honestly — Compare prompting, retrieval, fine-tuning, and a smaller trained model on cost per outcome, latency, and the effort to change each one later.
- Train where the economics are clear — Commit to training when volume, latency, or cost floors make it the only path that meets the standard, and when labeled data actually exists.
Outcomes
- Right-sized approach — Each workload uses the lightest method that meets its quality bar rather than the most impressive one available.
- Defensible model decisions — The choice among prompting, retrieval, and training is documented against cost, latency, and evidence rather than preference.
- Lower cost to change — Approaches that can be revised quickly are preferred where the requirement is still moving.