In this section of Latitude’s documentation you will find 3 kind of materials.

  1. SDK Examples: These examples demonstrate how to use the Latitude SDK to build AI applications, including running prompts, integrating tools, and managing conversation context.
  2. 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.
  3. Real-world Cases: These examples showcase complete solutions for common business and technical challenges, demonstrating how to combine various techniques into production-ready applications.

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SDK Examples

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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.

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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