Patterns

This section documents common patterns for building LLM applications using NudgeLang. Each pattern represents a proven approach to solving specific challenges in LLM application development.

Available Patterns

Prompt Chaining

A pattern where multiple LLM calls are executed in sequence, with each call building upon the output of the previous one. Ideal for complex tasks that require multiple steps of processing or refinement.

Orchestrator-Workers

A pattern that implements a main orchestrator that coordinates multiple worker LLMs, each handling specific subtasks. Perfect for breaking down complex problems into manageable pieces.

Autonomous Agent

A pattern for creating self-directed agents that can make decisions and take actions based on their goals and environment. Useful for building autonomous systems that can operate independently.

Parallelization

A pattern for executing multiple LLM calls concurrently to improve performance and throughput. Best for scenarios where multiple independent tasks can be processed simultaneously.

Evaluator-Optimizer

A pattern that combines evaluation and optimization steps to iteratively improve LLM outputs. Ideal for tasks that require quality control and continuous improvement.

Routing

A pattern for directing inputs to different processing paths based on content or requirements. Useful for creating flexible systems that can handle various types of inputs appropriately.

How to Use These Patterns

Each pattern includes:

• A detailed explanation of the pattern
• Common use cases
• Example implementations
• Best practices and considerations

Choose the pattern that best fits your specific use case, or combine multiple patterns to create more complex solutions.