Test LLMs first, then use them successfully

In concrete terms, this means: test the models in realistic scenarios, check them for consistency and possible distortions and simulate potential problem cases. This will ensure reliable results, strengthen the trust of your users and lay the foundation for long-term innovations. Test for:

1. Functionality
Realistic scenarios: Test the model with tasks that it should solve in practice. Answer accuracy Check whether the answers are correct and useful.

2. Consistency
Reproducibility: Ensure that the model delivers consistent results with similar inputs.
Context fidelity: Test whether the model retains context over longer interactions.

3. Bias and fairness
Bias detection: Analyze whether the model favors or discriminates against certain groups.
Fairness in responses: Check whether the model shows cultural, gender or social biases.

4. Robustness
Input errors: Test how the model responds to typos, incomplete sentences or unusual inputs.
Input variance: Test how the model reacts to particularly long inputs, for example.
Manipulation detection: Check whether it is susceptible to deliberately misleading inputs.

5. Security and ethics
Abuse possibilities: Test whether the model responds to dangerous requests (e.g. to create harmful content).
Data protection: Ensure that the model does not disclose sensitive or personal data.

6. Performance
Scalability: Check whether the model remains efficient even with high usage frequency.
Response speed: Test whether it delivers results in an acceptable time.

7. Continuity and update
Long-term use: Test whether the model delivers stable and reliable results over the long term.
Updates: Check how new data or optimizations affect performance.
Ideally, all these tests should be carried out systematically and automatically to continuously ensure the quality of the model.