SCC Comets

Data Extraction Prompts in Generative AI: How to Structure Outputs into JSON and Tables
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.
0 Artificial Intelligence
read more
Adversarial Prompt Testing: How to Find Hidden Weaknesses in AI Systems
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.
1 Artificial Intelligence
read more
Governance Committees for Generative AI: Roles, RACI, and Meeting Cadence Explained
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.
0 Artificial Intelligence
read more
Prompt-Tuning vs Prefix-Tuning: Choosing the Right Lightweight LLM Technique
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.
3 Artificial Intelligence
read more
Fine-Tuning for Faithfulness in Generative AI: How Supervised and Preference Methods Reduce Hallucinations
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.
7 Artificial Intelligence
read more
Risk Assessment for Generative AI Deployments: How to Evaluate Impact, Likelihood, and Controls
Tess Rempel Jan, 31 2026 Artificial Intelligence

Risk Assessment for Generative AI Deployments: How to Evaluate Impact, Likelihood, and Controls

Generative AI deployments carry real risks-from data leaks to legal violations. Learn how to assess impact, likelihood, and controls using proven frameworks like NIST and UC AI Council to protect your business.
6 Artificial Intelligence
read more
API Gateways and Service Meshes in Microservices Architecture
Tess Rempel Jan, 30 2026 Software Architecture

API Gateways and Service Meshes in Microservices Architecture

API Gateways manage external client traffic, while Service Meshes handle internal service communication. Learn how Kong, Istio, and Linkerd solve different problems in microservices architecture - and why using both correctly leads to more reliable systems.
5 Software Architecture
read more
Fine-Tuning Multimodal Generative AI: Dataset Design and Alignment Losses
Tess Rempel Jan, 28 2026 Artificial Intelligence

Fine-Tuning Multimodal Generative AI: Dataset Design and Alignment Losses

Fine-tuning multimodal AI requires precise dataset design and alignment losses to connect images with text. Learn how LoRA, QLoRA, and contrastive loss make this possible without massive compute-plus the hidden pitfalls most teams miss.
7 Artificial Intelligence
read more
Validation and Early Stopping Criteria for Large Language Model Training
Tess Rempel Jan, 27 2026 Artificial Intelligence

Validation and Early Stopping Criteria for Large Language Model Training

Validation and early stopping are critical for training large language models without overfitting. Learn how to use perplexity, validation datasets, and human review to build reliable, efficient LLMs.
6 Artificial Intelligence
read more
Prompting Strategies and Best Practices for Effective Vibe Coding
Tess Rempel Jan, 26 2026 Artificial Intelligence

Prompting Strategies and Best Practices for Effective Vibe Coding

Vibe coding lets developers build prototypes fast using AI prompts. Learn the six-step prompting method, what works best, common mistakes, and how to avoid technical debt. Used by 68% of tech teams for rapid prototyping.
0 Artificial Intelligence
read more
Quality Metrics for Generative AI Content: Readability, Accuracy, and Consistency
Tess Rempel Jan, 25 2026 Artificial Intelligence

Quality Metrics for Generative AI Content: Readability, Accuracy, and Consistency

Learn how to measure the quality of AI-generated content using readability, accuracy, and consistency metrics. Discover real-world tools, thresholds, and workflows used by enterprises to prevent errors and protect brand trust.
7 Artificial Intelligence
read more
Continuous Evaluation in Production: Shadow Testing Large Language Models
Tess Rempel Jan, 24 2026 Artificial Intelligence

Continuous Evaluation in Production: Shadow Testing Large Language Models

Shadow testing lets you evaluate new large language models in production without risking user experience. Learn how it catches hallucinations, safety issues, and cost spikes before they impact real users.
8 Artificial Intelligence
read more