Category: Artificial Intelligence
Model Parallelism and Pipeline Parallelism in Large Generative AI Training
Pipeline parallelism enables training of massive generative AI models by splitting them across GPUs, overcoming memory limits. Learn how it works, why it's essential, and how it compares to other parallelization methods.
Citation Strategies for Generative AI: How to Link Claims to Source Documents Without Falling for Hallucinations
Generative AI can't be trusted as a source. Learn how to cite AI tools responsibly, avoid hallucinated facts, and verify claims using real sources-without risking your academic integrity.
RAG System Design for Generative AI: Mastering Indexing, Chunking, and Relevance Scoring
RAG systems reduce AI hallucinations by retrieving real-time data instead of relying on static training. Learn how indexing, chunking, and relevance scoring make RAG accurate, reliable, and enterprise-ready.
Code Generation with Large Language Models: How Much Time Do You Really Save?
AI code generators like GitHub Copilot save developers hours on routine tasks but introduce hidden risks in security and correctness. Learn where they excel, where they fail, and how to use them safely.
Domain-Specific RAG: Building Compliant Knowledge Bases for Regulated Industries
Domain-specific RAG transforms compliance in regulated industries by grounding AI in verified regulations. Learn how healthcare, finance, and legal teams use it to cut errors, speed up audits, and stay compliant-with real data from 2025 deployments.
Tempo Labs and Base44: The Two AI Coding Platforms Changing How Teams Build Apps
Tempo Labs and Base44 are leading the rise of vibe coding-AI platforms that turn natural language into working apps. See how they differ, who they're for, and which one fits your team in 2026.
How to Manage Latency in RAG Pipelines for Production LLM Systems
Learn how to reduce latency in production RAG pipelines using Agentic RAG, streaming, batching, and vector database optimization. Real-world benchmarks and fixes for sub-1.5s response times.
Product Management for Generative AI Features: Scoping, MVPs, and Metrics
Managing generative AI features requires a new approach to scoping, MVPs, and metrics. Learn how to avoid common pitfalls, build capability-based tracks, and measure real user impact-not just clicks.
Emergent Abilities in NLP: When LLMs Start Reasoning Without Explicit Training
Large language models suddenly gain reasoning skills at certain sizes-without being trained for them. This phenomenon, called emergent ability, is reshaping AI development-and creating serious risks.
Evaluating Reasoning Models: Think Tokens, Steps, and Accuracy Tradeoffs
Reasoning models improve accuracy on complex tasks but at a steep cost in tokens and dollars. Learn when they help, when they hurt, and how to use them wisely without breaking the bank.
Value Capture from Agentic Generative AI: End-to-End Workflow Automation
Agentic generative AI is transforming enterprise workflows by automating end-to-end processes without human intervention. Discover how companies are capturing 20-60% productivity gains and real ROI in 2025.
How to Calibrate AI Personas for Consistent Responses Across Sessions and Channels
Learn how to keep AI personas consistent across sessions and channels using structured prompts, memory systems, and human oversight. Fix persona drift before users lose trust.
