SCC Comets

Tess Rempel Feb, 15 2026 Artificial Intelligence

Practical Applications of Generative AI Across Industries and Business Functions in 2025

Generative AI is transforming healthcare, finance, manufacturing, and customer service in 2025 with real-world results-cutting costs, speeding up workflows, and improving accuracy. Learn how top companies are using it-and where it still falls short.

Tess Rempel Feb, 14 2026 Software Architecture

Team Size Compression: How Smaller Teams Deliver More Without Burning Out

Smaller teams can deliver more by cutting waste, not people. Learn how compressing your team to 60% of its size boosts speed, reduces burnout, and unlocks real productivity.

Tess Rempel Feb, 13 2026 Artificial Intelligence

Evaluating Internal Deliberation Costs in Reasoning Large Language Models

Reasoning large language models deliver superior accuracy on complex tasks-but at a steep computational cost. Learn how deliberation expenses stack up, where they pay off, and how top companies are cutting waste without losing performance.

Tess Rempel Feb, 12 2026 Artificial Intelligence

Prompt Costs in Generative AI: How to Reduce Tokens Without Losing Context

Learn how to cut generative AI prompt costs by reducing tokens without losing quality. Real strategies, real savings, and what works today-based on enterprise data from 2024.

Tess Rempel Feb, 10 2026 Artificial Intelligence

How Prompt Templates Reduce Waste in Large Language Model Usage

Prompt templates cut LLM waste by 65-85% by reducing token usage, energy consumption, and processing time. Learn how structured inputs save money, improve accuracy, and lower carbon emissions in AI workflows.

Tess Rempel Feb, 9 2026 Artificial Intelligence

Comparing LLM Pricing: OpenAI, Anthropic, Google, and More in 2026

In 2026, LLM pricing has dropped 98% since 2023. Learn how OpenAI, Anthropic, Google, and Meta compare on cost, accuracy, and hidden fees-so you don’t overpay for AI.

Tess Rempel Feb, 8 2026 Artificial Intelligence

Embeddings in Large Language Models: How Meaning Is Represented in Vector Space

Embeddings turn words into numbers that capture meaning, letting AI understand context, similarity, and relationships. From Word2Vec to BERT and beyond, this is how large language models make sense of language.

Tess Rempel Feb, 7 2026 Artificial Intelligence

Data Extraction Prompts in Generative AI: How to Structure Outputs into JSON and Tables

Learn how to design prompts that turn unstructured text and images into clean JSON and tables using generative AI. Reduce manual data entry, avoid common errors, and integrate AI extraction into your workflows.

Tess Rempel Feb, 6 2026 Artificial Intelligence

Adversarial Prompt Testing: How to Find Hidden Weaknesses in AI Systems

Adversarial prompt testing uncovers hidden vulnerabilities in AI systems before attackers do. Learn how to test large language models for jailbreaks, data leaks, and safety bypasses using practical tools and step-by-step methods.

Tess Rempel Feb, 5 2026 Artificial Intelligence

Governance Committees for Generative AI: Roles, RACI, and Meeting Cadence Explained

Governance committees for generative AI ensure ethical and compliant AI use. This guide explains roles, RACI framework, meeting schedules, and implementation steps. Learn from real-world examples to avoid common pitfalls.

Tess Rempel Feb, 4 2026 Artificial Intelligence

Prompt-Tuning vs Prefix-Tuning: Choosing the Right Lightweight LLM Technique

Learn how prompt-tuning and prefix-tuning let you adapt large language models with minimal compute. Compare their mechanisms, performance, and ideal use cases. Discover real-world examples and limitations. Get actionable tips for choosing between them. Understand why these lightweight techniques are revolutionizing LLM deployment.

Tess Rempel Feb, 2 2026 Artificial Intelligence

Fine-Tuning for Faithfulness in Generative AI: How Supervised and Preference Methods Reduce Hallucinations

Supervised and preference-based fine-tuning methods reduce AI hallucinations by preserving reasoning integrity. Learn how QLoRA, RLHF, and reasoning validation improve faithfulness in generative models.