RAG

A deep dive into the four foundational components of AI agents—Memory, Tools, Planning, and Execution—and how to implement them using modern LLMs.

Six months after the formation of Microsoft AI (MAI), the tech giant releases three powerful foundational models specializing in voice-to-text, audio generation, and image synthesis to compete with OpenAI and Anthropic.

A deep dive into the 3-layer architecture required to transform reactive LLMs into proactive, self-running autonomous agents using Heartbeat mechanisms, Cron scheduling, and persistent Memory systems.

A deep dive into optimizing LLM costs and latency on the edge, transitioning from brute-force context injection to sophisticated RAG architectures using embeddings and prompt distillation.

Discover how the LangChain and MongoDB partnership simplifies the AI agent stack by integrating vector search, persistent memory, and natural-language querying into a single, trusted database platform.

Learn how to install Ollama, deploy models like Llama 3 and DeepSeek-V3 locally, and integrate them with Python and RAG workflows for maximum privacy and zero cost.

A comprehensive guide and checklist for evaluating AI agents, covering error analysis, dataset construction, grader design, and production readiness.

A comprehensive guide on creating high-fidelity evaluation systems for AI agents, focusing on data sourcing, metric definition, and iterative experimentation.

A comprehensive guide on transforming industrial monitoring using Amazon Bedrock Agents, AI-enriched API documentation, and RAG to create predictive maintenance assistants.

Explore how Kensho, S&P Global's AI arm, built 'Grounding'—a sophisticated multi-agent framework using LangGraph to unify fragmented financial data and ensure trusted retrieval at enterprise scale.

Move beyond basic API calls with 7 production-proven LLM architectures, covering RAG, multi-agent systems, human-in-the-loop, and batch processing for enterprise applications.

Building production-grade AI requires moving beyond model capability to model governability. Learn how to implement a multi-layered guardrail architecture to mitigate hallucinations, prompt injections, and data leakage.