- SDK Examples: These examples demonstrate how to use the Latitude SDK to build AI applications, including running prompts, integrating tools, and managing conversation context.
- Prompting Techniques: This section covers advanced prompting techniques that can enhance the quality and capabilities of your LLM applications, such as reasoning methods, memory management, and input/output strategies.
- Real-world Cases: These examples showcase complete solutions for common business and technical challenges, demonstrating how to combine various techniques into production-ready applications.
SDK Examples
Popular SDK Examples
Popular SDK Examples
- Run Prompt - Execute prompts with dynamic parameters
- Run with Tools - Integrate external tools with your prompts
- Render Chain - Connect multiple prompts in sequence
- RAG Retrieval - Implement retrieval-augmented generation
Prompting Techniques
These the main techniques for advanced prompting that can significantly improve the performance and reliability of your LLM applications. Each technique is designed to address specific challenges in AI interactions, from enhancing reasoning capabilities to managing context and improving output quality.Few-Shot Prompting
Learn how to implement few-shot learning with examples to improve AI performance on specific tasks
Role Prompting
Enhance AI performance by assigning specific roles, personas, and expertise areas
CoT (Chain of Thought) Prompting
Improve reasoning and problem-solving capabilities with structured thought processes
ToT (Tree of Thought) Prompting
Enable complex reasoning by breaking down problems into manageable sub-tasks
Contextual prompting
Manage conversation context effectively to maintain coherence and relevance
Self-Consistency
Enhance output reliability by generating multiple responses and selecting the best one
Step back prompting
Improve output quality by iteratively refining responses through feedback loops
ReAct (reason & act)
Combine reasoning and action to enhance decision-making capabilities
Advanced prompting techniques can dramatically improve the quality, reliability, and capabilities of your LLM applications. These examples demonstrate proven approaches to enhance your AI systems.
Real-world Cases
Our case examples showcase complete solutions for common business and technical challenges, demonstrating how to combine various techniques into production-ready applications. Explore real-world implementations that you can adapt to your specific needs:Customer Support Email Generator
Create personalized, empathetic customer service emails with multi-agent architecture
Content Moderation System
Implement robust content filtering and moderation with constitutional AI principles
Deep Search
Build an advanced information retrieval system with multi-stage processing
Stock Market Analysis
Analyze financial data and generate insights using specialized agents
Need help choosing the right example? Check out our Getting Started Guide or contact support for personalized recommendations.