# Pathway - AI-Driven Applications
# AI-Driven Applications Introduction
# M5 Integrating AI in Applications
- Overview of popular LLM APIs (Groq)
- Setting up and authenticating with APIs
- LLM API calls and response handling
- Integrating AI services
# M6 LLMs and Prompt Engineering
- Introduction to prompt engineering
- Understanding and managing LLM API parameters (temperature, max tokens, etc.)
- ChatML format / system messages and user messages
- Prompt design strategies for different tasks
- Few-shot learning and in-context learning
- Prompt chaining for complex tasks
- RISEN Framework for Prompting
# M8 Context and Knowledge Management
- Managing Context
- Vector Databases
- RAG (Retrieval-Augmented Generation)
- Implementing custom knowledge bases
# M9 - AI Workflow Orchestration
- Introduction to frameworks for building AI workflows (LangChain)
- Chains and flows
- Tool Calling
- Agents (REACT)
# M10 - Model Customization
- Fine-tuning LLMs (conceptual overview and practical considerations)
- Brief overview of transfer learning in the context of fine-tuning
- Discussing the trade-offs between fine-tuning and prompt engineering
# M11 - Deployment and Practical Considerations
- Deploying AI applications (e.g., using Flask, FastAPI)
- Managing API costs and optimizing usage
- Monitoring and logging for AI applications
- Implementing feedback loops for continuous improvement
- Privacy and data security in AI applications
- Ethical guidelines for AI application development and responsibilities
- User experience considerations for AI-powered apps
- Future trends and emerging technologies in AI applications