Small Language Models (SLMs)
Efficient, lightweight models like Phi-3, Gemma, and TinyLlama that bring AI to edge devices. SLMs reduce computational costs while maintaining strong performance for specific tasks.
Phi-3GemmaTinyLlamaEdge AI
Model Quantization
Techniques like INT8/INT4 quantization, GPTQ, AWQ, and GGUF formats that compress models for faster inference. Quantization reduces memory footprint by up to 75%.
INT8/INT4GPTQAWQGGUFQLoRA
Multi-Agent Frameworks
Coordinated AI agent systems using LangGraph, CrewAI, and AutoGen for complex task orchestration. Enable autonomous workflows, tool use, and collaborative problem-solving.
LangGraphCrewAIAutoGenTool Use
Model Context Protocol (MCP)
Anthropic's open standard for connecting AI assistants to external systems. MCP provides a unified interface for LLMs to access databases, APIs, and tools.
Anthropic MCPTool IntegrationContext Mgmt
Retrieval Augmented Generation (RAG)
Combining vector databases with LLMs for knowledge-grounded responses. RAG systems overcome knowledge cutoff limitations and reduce hallucinations.
Vector DBsEmbeddingsSemantic Search
Vision-Language Models (VLMs)
Multimodal AI systems like GPT-4V, LLaVA, and CLIP that understand both images and text. Enable visual question answering and cross-modal reasoning.
GPT-4VLLaVACLIPMultimodal AI
Reinforcement Learning Agents
Intelligent agents that learn optimal policies through environment interaction. From game-playing AI to robotics control using reward signals.
PPODQNA3CSACReward Shaping
AI Calling Agents
Voice-enabled AI agents for automated phone interactions. Combine speech recognition, NLU, and TTS to handle customer calls and provide 24/7 support.
TwilioVapiBland AISpeech Synthesis
Agentic Automation
End-to-end automation powered by AI agents that can plan, execute, and adapt workflows autonomously. Combine reasoning, tool use, and memory.
AutoGPTBabyAGITask PlanningOrchestration
