Learning a pattern is different from using it
During training, an optimization procedure adjusts model parameters using data and an objective. During inference, the trained system produces outputs from inputs. In a language model, generated text is shaped by the model, the supplied context and the decoding process.
A conversation that accumulates messages can change the next response without changing model weights. Retrieval and saved skills can change the information available to an agent. These are important capabilities, but should not be confused with every interaction retraining the underlying model.
Capability has a context
A benchmark measures performance under particular tasks, inputs and scoring rules. It does not establish that the same model is reliable for every workflow. Tool quality, context construction, error handling and the surrounding harness can materially affect the observed result.
Choose a representative task set before choosing a winner. Include normal cases, ambiguous inputs, missing information and an adversarial example. Separate correctness, source fidelity, format compliance, latency and cost. A single total score can hide a failure you cannot accept.
Make the choice reproducible
Freeze the task and evaluation criteria. Save the exact prompt, supplied context, model identifier, host version and output. Repeat enough examples to notice variability. Avoid tuning your instructions only to the cases used for the final comparison.
The source directory below links to multiple labs because model selection should remain an empirical decision. This course claims no partnership or endorsement. Begin with the provider documentation, then make a comparison that reflects your own constraints. A documented capability is a hypothesis to test in your system.
A worked example · Illustrative
An evaluation matrix
Case: public announcement with conflicting dates
Correctness: preserves the conflict
Evidence: cites both supplied passages
Format: matches the comparison schema
Boundary: does not fetch or publish without permission
Operational: record elapsed time and usage
Decision: reject any candidate that silently invents a datePut the idea to work
Your practice
- Choose one task and four representative cases.
- Define separate quality and operational criteria.
- Run the same inputs through two candidate configurations.
- Explain your choice, including a case where it might fail.