Generative AI & Agents

Compare RAG, fine-tuning and prompt engineering for enterprise AI. Review architectures, evaluation, security and a transparent 100,000-query cost model.

Compare LangChain, LangGraph and AutoGen architecture, lifecycle, APIs, costs and governance. Evaluate Microsoft Agent Framework for new projects.

Compare agentic and generative AI architectures, enterprise use cases, security, costs and adoption steps. Choose a controlled, measurable approach.

A practical guide to enterprise RAG architecture: indexing, permission-aware retrieval, Azure implementation, evaluation, cost planning and production deployment.

AI agents go beyond rule-based automation: they understand context, make decisions and work across your systems. Here is how businesses are putting them to work.

Generative AI can do far more than answer chat questions. From document intelligence to code generation, these are the use cases delivering real business value.

There is no single best large language model. Here is a practical framework for choosing between commercial and open-source LLMs for your use case.