Compare language models on the same maintenance reports. Assess output format, source accuracy, total waiting time and review effort before choosing a model.
The spoken explanation, organised by chapter.
This accompanies our article on integrating language models. A convincing answer does not establish a good model choice. Compare the work needed to complete the task.
A fictional maintenance report describes water under a pump housing. A model may propose an asset reference and problem description. It cannot decide urgency or schedule an engineer.
Give suitable options the same normal, incomplete and contradictory inputs. Include source text that appears to issue instructions. Define the expected checks before running the trial.
Specify permitted processing, output fields and missing values. Record model identification, instructions and configuration. Explain settings that cannot be matched, so differences remain visible in the assessment.
Correctly structured fields may contain incorrect information. A missing asset reference stays unknown. When two references conflict, the model must not choose an asset by guessing.
A refusal, invalid output or timeout needs a bounded recovery route. Each task keeps a stable reference, so retries do not silently create several review items.
Measure total waiting time, including validation and retries. Track human correction effort and usage costs too. A cheap model call does not necessarily produce a cheaply completed task.
Choose only after running the trial against agreed requirements. Record limitations and appoint a maintainer. Relevant changes to the model, instructions or schema require a fresh assessment.
The suitable option depends on your task and observed results. Our source article on LLM integration explains this evaluation approach and the boundaries around model output.
1:59See how model output becomes controlled application input. An illustrative report explains field validation, content checks, error handling and permissions.
Source articleOpenAI integration
1:51Use a fictional procedure question to trace a RAG failure to missing sources, missed passages, incorrect use of context or access controls before answering.
Source articleRAG system development
1:38See how AI can help process a fictional email using fixed rules, controlled actions, human review and a trial that measures the whole business process.
Source articleAI automation