Black Seed USA AI Hub - Page 3
Prompt Injection Attacks Against Large Language Models: How to Detect and Defend Against Them
Prompt injection attacks manipulate AI systems by tricking them into ignoring instructions and revealing sensitive data. Learn how these attacks work, real-world examples, and proven defense strategies to protect your LLM applications.
- 10
- Read More
How to Evaluate Compressed Large Language Models: Modern Protocols That Actually Work
Modern evaluation protocols for compressed LLMs go far beyond perplexity. Learn how LLM-KICK, EleutherAI LM Harness, and LLMCBench catch silent failures that traditional metrics miss-and why you can't afford to skip them.
Confidential Computing for LLM Inference: How TEEs and Encryption-in-Use Protect AI Models and Data
Confidential computing uses hardware-based Trusted Execution Environments to protect LLM inference by keeping data encrypted while in use. Learn how TEEs and GPU-based encryption are solving AI privacy risks for healthcare, finance, and government.
Secure Human Review Workflows for Sensitive LLM Outputs
Human review workflows are essential for preventing data leaks from LLMs in healthcare, finance, and legal sectors. Learn how to build secure, compliant systems that catch 94% of sensitive data exposures.
Deterministic vs Stochastic Decoding in Large Language Models: When to Use Each
Learn when to use deterministic vs stochastic decoding in large language models for accurate answers or creative outputs. Discover which methods work best for code, chatbots, and content generation.
How to Protect Model Weights and Intellectual Property in Large Language Models
Learn how to protect your LLM's model weights and intellectual property using watermarking, fingerprinting, and legal strategies. Essential for companies using AI in regulated industries.
- 10
- Read More
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.
- 10
- Read More