Expert insights on generative AI, LLM engineering, AI agents, and data analytics from the TUTAI team.
A structured roadmap for data analysts moving into AI roles, with skill gap analysis, phased learning from prompting to AI agents, and career positioning.
Step-by-step cohort analysis guide covering retention methodology, Python pandas implementation, and frameworks for turning insights into strategy.
Practical framework for chart selection, Python visualization with matplotlib and plotly, data storytelling, and anti-patterns to avoid in business reporting.
Technical guide to the Model Context Protocol: architecture, server primitives, LangChain integration, and building MCP servers for data pipelines.
Multimodal AI overview covering vision-language models, image and audio understanding, cross-modal RAG pipelines, and practical use cases for data teams.
RAG architecture explained: retrieval pipeline, embedding models, chunking strategies, vector databases, GraphRAG, and a LangChain implementation.
Guide to prompting strategies from zero-shot to Tree of Thoughts and context engineering, with technique selection frameworks and empirical rankings.
Operate AI agents in production with four-layer architecture, Langfuse monitoring, safety guardrails, hallucination prevention, and scaling strategies.
Learn AI agent architectures, the ReAct pattern, multi-agent systems, and production deployment with LangChain, LangGraph, FastAPI, and Langfuse.
Guide to LLM API integration covering prompt engineering, cost management, function calling, structured outputs, semantic search, and RAG patterns.
Technical walkthrough of LLM evolution from GPT-1 to o3: scaling laws, RLHF training pipeline, chain-of-thought reasoning, and inference-time compute.
AI's impact on data careers through 2030: task-level automation, role-by-role changes, three scenarios, and an upskilling roadmap.
Technical introduction to generative AI: model classification, transformer architecture, LLM ecosystem, and practical applications for data teams.
