llm-patterns

Balancing LLM capabilities with traditional code in application architecture requires using LLMs for classification and decision-making while relying on deterministic code for data validation, API handling, and database operations to ensure optimal performance.

Use LLMs versus traditional code for AI integration by delegating reasoning, classification, and content generation tasks to the LLM, and assigning robust, deterministic tasks like data validation and API handling to traditional code.

The best way to structure LLM logic management for software development is applying a pattern-based approach that defines exactly when to use LLMs for generation and decision-making, separating these from deterministic code operations.

Pattern-based approaches for LLM data validation and API handling suggest using traditional code rather than the LLM itself, ensuring robust and deterministic execution for critical database operations and API interactions.

Integrating LLMs into AI-first applications using this pattern-based approach requires no specific external framework dependencies, allowing developers to apply the architectural guidelines directly within their existing software development workflows.

Use traditional code for database operations in AI-first application architecture because deterministic code provides the robust validation and precise API handling that LLMs cannot reliably guarantee, ensuring data integrity.