Black Seed USA AI Hub - Page 2
How Large Language Models Use Probabilities to Choose Words and Phrases
Large language models generate text by predicting the next word based on probabilities learned from massive datasets. They don't understand meaning - they guess statistically likely sequences. This is how they sound smart without knowing anything.
Prompt Hygiene for Factual Tasks: How to Write Clear LLM Instructions That Avoid Errors
Learn how to write clear, precise LLM instructions that reduce hallucinations, prevent security risks, and ensure factual accuracy in high-stakes tasks like healthcare and legal work.
Optimizing Attention Patterns for Domain-Specific Large Language Models
Optimizing attention patterns in domain-specific LLMs improves accuracy by guiding models to focus on relevant terms and relationships. Techniques like LoRA cut costs and boost performance without full retraining.
Build vs Buy for Generative AI Platforms: Decision Framework for CIOs
CIOs must choose between building or buying generative AI platforms. This guide breaks down when to buy, when to build, and how hybrid approaches deliver the best results with real-world data and cost comparisons.
Data Strategy for Generative AI: How Quality, Access, and Security Drive Real Results
A solid data strategy for generative AI isn't optional-it's the difference between a tool that helps and one that hurts your business. Learn how quality, access, and security drive real results.
Multimodal Generative AI: Models That Understand Text, Images, Video, and Audio
Multimodal generative AI now understands text, images, audio, and video together-changing healthcare, manufacturing, and education. See how GPT-4o, Llama 4, and other models work, where they excel, and where they still fail.
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How Human Feedback Loops Make RAG Systems Smarter Over Time
Human feedback loops turn static RAG systems into adaptive ones by using real user and expert input to improve retrieval accuracy over time. Learn how this approach boosts relevance, reduces errors, and outperforms automated methods.
Self-Ask and Decomposition Prompts for Complex LLM Questions: How to Break Down Hard Problems for Better AI Answers
Learn how self-ask and decomposition prompting help LLMs solve complex, multi-step questions by breaking them into clear, verifiable steps. Boost accuracy by over 13% with proven techniques used by enterprises in 2025.
Citation Strategies for Generative AI: How to Link AI Claims to Real Sources
Generative AI can't be trusted as a source of facts. Learn how to properly cite AI tools without falling for hallucinations, and how to trace every claim back to real, verifiable sources.
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Life Sciences Research with Generative AI: Protein Design and Literature Reviews
Generative AI is transforming life sciences by designing entirely new proteins and automating literature reviews. From cancer therapies to enzyme engineering, AI is enabling breakthroughs that were impossible just years ago.
Mathematical Reasoning Benchmarks for Next-Gen Large Language Models
Current large language models can solve many math problems but don't truly reason. Benchmarks reveal they rely on memorization, not logic. True mathematical understanding remains out of reach.
Funding Models for Vibe Coding Programs: Chargebacks and Budgets
Vibe coding slashes development time but creates unpredictable costs. Learn how chargebacks happen, why budgets fail, and how to control spending on AI-powered coding tools before your next bill shocks your finance team.