[#28] Navigating the New Terrain: Application development around Foundational Models
What changes, what gives, what remains the same... what does it all mean?
Fundamentally, LLMs mostly provide/include the core application-building logic which is very different from say cloud which is abstracted APIs to access infrastructure
This changes a lot of things technically w/ a whole lot of implications: certain things become commodities, certain whitespaces emerge & certain new things become possible that seemed previously impossible
Some of the changes in the tech stack include:
1/ Testing: Testing with Gen AI models is more complex than traditional software testing. It must account for the inherent unpredictability and non-determinism of AI outcomes. This involves:
Data diversity: Ensuring the test data covers a wide range of scenarios, including edge cases.
Behavioral testing: Observing how the model reacts to different inputs, focusing on ethical and unbiased responses.
Performance testing: Evaluating the model’s response time and resource usage under various conditions.
2/ Versioning: As AI models evolve, version control becomes crucial. Each version may behave differently, impacting the application’s functionality.
Model tracking: Keeping track of which model version was used for specific outcomes or decisions.
Backward compatibility: Ensuring newer model versions don't break existing application functionality.
3/ Training: Training Gen AI models requires careful consideration, especially when tailored to specific applications.
Data selection and preprocessing: Choosing the right training data and preparing it effectively.
Model fine-tuning: Adjusting model parameters to suit the specific needs of the application.
Monitoring and retraining: Continuously monitoring model performance and retraining as needed with new data.
4/ Support: Ongoing support for AI-powered applications involves:
User feedback integration: Regularly updating the model based on user interactions and feedback.
Ethical and compliance monitoring: Ensuring the model adheres to ethical standards and regulatory requirements.
Continual learning and updating: Keeping the model current with the latest data and trends.
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