Structured outputs in LLMs: Definition, techniques, applications, benefits
Structured outputs in large language models (LLMs) refer to the ability of these models to generate responses in specific, predefined formats rather than just free-form text.
Causal AI: Importance, use cases, benefits, challenges and implementation strategies
Causal AI is a branch of artificial intelligence focused on understanding and determining cause-and-effect relationships rather than merely identifying patterns or correlations.
ReAct Agents vs Function Calling Agents
ReACT and function calling agents represent two distinct but powerful approaches to extending the capabilities of LLMs, each with its own strengths and weaknesses.
Composite AI: Benefits, applications, implementation strategies, best practices, and future prospects
Composite AI refers to an advanced AI approach that integrates multiple artificial intelligence (AI) technologies to create a more sophisticated, flexible, and intelligent system.
Advanced RAG: Architecture, techniques, applications and use cases and development
Advanced RAG builds upon the basic principles of naive RAG by incorporating more sophisticated techniques to enhance retrieval accuracy and contextual relevance.
