Use Cases
Kamiwaza enables a wide variety of AI applications and workflows. This section provides practical guidance for implementing common use cases, complete with step-by-step instructions, best practices, and example code.
What You'll Find Here
Each use case guide includes:
- Overview - What the use case accomplishes and when to use it
- Prerequisites - Required models, services, and setup steps
- Implementation - Step-by-step instructions with code examples
- Best Practices - Tips for optimization and production deployment
- Troubleshooting - Common issues and solutions
Featured Use Cases
📖 Building a RAG Pipeline
Learn how to create a Retrieval-Augmented Generation (RAG) system that combines your documents with large language models to provide accurate, context-aware responses.
What you'll build:
- Document ingestion and preprocessing
- Vector embeddings for semantic search
- Retrieval system for relevant context
- LLM integration for response generation
Perfect for: Customer support, internal knowledge bases, document Q&A systems
Coming Soon
We're working on additional use case guides including:
🤖 Multi-Agent Systems
Build sophisticated AI agents that can collaborate to solve complex tasks, with coordination, memory, and tool usage capabilities.
🎯 Custom Model Fine-tuning
Deploy and serve your own fine-tuned models, including setup for training workflows and model versioning.
🔄 Real-time Data Processing
Stream processing pipelines that combine AI models with live data feeds for real-time insights and actions.