SCC Comets

Scientific Workflows with Large Language Models: How Researchers Are Using Sci-LLMs to Accelerate Discovery
Tess Rempel Mar, 19 2026 Artificial Intelligence

Scientific Workflows with Large Language Models: How Researchers Are Using Sci-LLMs to Accelerate Discovery

Scientific Large Language Models (Sci-LLMs) are accelerating research by automating literature reviews, generating hypotheses, and designing experiments. Learn how they work, where they excel, and where they still fail.
0 Artificial Intelligence
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Token Probability Calibration in LLMs: How to Fix Overconfidence in AI Predictions
Tess Rempel Mar, 18 2026 Artificial Intelligence

Token Probability Calibration in LLMs: How to Fix Overconfidence in AI Predictions

Token probability calibration fixes AI overconfidence by aligning predicted probabilities with real accuracy. Learn how GPT-4, Llama-2, and other models misjudge confidence-and what to do about it.
1 Artificial Intelligence
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Natural Language to Schema: How to Prompt Databases and ER Diagrams Effectively
Tess Rempel Mar, 17 2026 Artificial Intelligence

Natural Language to Schema: How to Prompt Databases and ER Diagrams Effectively

Natural Language to Schema lets non-technical users query databases using plain English. Learn how ER diagrams, schema awareness, and LLMs work together to turn spoken questions into accurate SQL-plus real-world accuracy stats, tool comparisons, and implementation tips.
0 Artificial Intelligence
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Domain-Specialized Large Language Models: Code, Math, and Medicine
Tess Rempel Mar, 15 2026 Artificial Intelligence

Domain-Specialized Large Language Models: Code, Math, and Medicine

Domain-specialized LLMs like CodeLlama, Med-PaLM 2, and MathGLM are outperforming general AI models in code, math, and medicine - with higher accuracy, lower costs, and real-world impact.
4 Artificial Intelligence
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Autoscaling Large Language Model Services: Policies, Signals, and Costs
Tess Rempel Mar, 13 2026 Artificial Intelligence

Autoscaling Large Language Model Services: Policies, Signals, and Costs

Autoscaling LLM services requires specialized metrics like prefill queue size and slots_used-not CPU or GPU usage. Learn how to balance cost and latency with real-world policies for chatbots, batch jobs, and real-time apps.
5 Artificial Intelligence
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Multilingual RAG for Large Language Models: Overcoming Cross-Language Retrieval Challenges
Tess Rempel Mar, 11 2026 Artificial Intelligence

Multilingual RAG for Large Language Models: Overcoming Cross-Language Retrieval Challenges

Multilingual RAG lets LLMs answer questions across languages using external data-but hidden biases favor English. Learn how cross-language retrieval fails, why translation alone isn’t enough, and what new frameworks like DKM-RAG are doing to fix it.
7 Artificial Intelligence
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Vision-Language Applications with Multimodal Large Language Models
Tess Rempel Mar, 7 2026 Artificial Intelligence

Vision-Language Applications with Multimodal Large Language Models

Vision-language models now combine image and text understanding in real-time, transforming document processing, healthcare, and robotics. Open-source models like GLM-4.6V are outperforming proprietary systems in key areas, despite high compute costs and hallucination risks.
6 Artificial Intelligence
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Token Budgets and Quotas: How to Stop LLM Cost Overruns Before They Happen
Tess Rempel Mar, 6 2026 Artificial Intelligence

Token Budgets and Quotas: How to Stop LLM Cost Overruns Before They Happen

Token budgets and quotas are the only way to prevent runaway LLM costs. Learn how to set limits on token usage, apply graduated throttling, and avoid expensive mistakes before they happen.
8 Artificial Intelligence
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How Large Language Models Use Probabilities to Choose Words and Phrases
Tess Rempel Mar, 5 2026 Artificial Intelligence

How Large Language Models Use Probabilities to Choose Words and Phrases

Large language models generate text by calculating the probability of each next word based on patterns learned from massive datasets. This article explains how they use probabilities, decoding strategies like top-p and temperature, and why they sometimes make mistakes - even when they sound convincing.
7 Artificial Intelligence
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How to Set Realistic Expectations for Vibe Coding on Enterprise Projects
Tess Rempel Mar, 4 2026 Artificial Intelligence

How to Set Realistic Expectations for Vibe Coding on Enterprise Projects

Vibe coding isn't magic - it's a new way to build software using AI as a proactive team member. Learn how to set realistic expectations, write clear specs, and avoid common enterprise pitfalls that derail adoption.
6 Artificial Intelligence
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Why Tokenization Still Matters in the Age of Large Language Models
Tess Rempel Mar, 3 2026 Artificial Intelligence

Why Tokenization Still Matters in the Age of Large Language Models

Tokenization remains critical in the age of large language models, impacting cost, accuracy, and efficiency. Learn why subword tokenization, vocabulary size, and domain-specific tuning still make or break LLM performance.
8 Artificial Intelligence
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Continuous Improvement Loops: How Feedback, Retraining, and Prompt Updates Keep AI Models Accurate
Tess Rempel Mar, 2 2026 Artificial Intelligence

Continuous Improvement Loops: How Feedback, Retraining, and Prompt Updates Keep AI Models Accurate

Continuous improvement loops keep AI models accurate by using real-world feedback, automated retraining, and prompt updates. Without them, models degrade quickly. Here’s how to build one that actually works.
5 Artificial Intelligence
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