SCC Comets

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.

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.

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.

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.

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.

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.

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.

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.

Tess Rempel Jan, 23 2026 Artificial Intelligence

Scheduling Strategies to Maximize LLM Utilization During Scaling

Learn how dynamic batching, sequence prediction, and token budgeting can boost LLM GPU utilization by up to 87%, slash costs, and cut latency. Real-world strategies used by top AI teams in 2026.

Tess Rempel Jan, 22 2026 Artificial Intelligence

Model Selection for Vibe Coding: Claude, GPT-4, and Gemini Compared

Learn how to choose between Claude Opus, GPT-4 Turbo, and Gemini Flash for vibe coding in 2026. Save time, reduce costs, and build better software by matching each AI model to the right task.

Tess Rempel Jan, 21 2026 Artificial Intelligence

Legal Review Steps for Vibe-Coded Features That Handle Customer Data

Vibe coding speeds up development but creates serious legal risks when handling customer data. Learn the 9-step legal review process to avoid GDPR fines, data breaches, and regulatory audits in 2026.

Tess Rempel Jan, 20 2026 Artificial Intelligence

Security by Design in Vibe-Coded Architectures: How to Build Secure AI-Generated Code

Vibe coding speeds up development but introduces serious security risks. Learn the top threats in AI-generated code and how to implement real, proven controls to build secure systems without slowing down.