LLM Use Cases: Applications Across Industries and How to Implement Them
Where large language models are actually being deployed today, how businesses implement them, and what to weigh before committing to a use case.
All
AI 69
Geology 26
Benchmarking 24
Machine Learning 16
Evaluation 12
Agentic AI 10
Open Source 9
Reinforcement Learning 5
China 4
Complexity Theory 3
AI
Evaluation Metrics in Machine Learning: Key Metrics and How to Choose
Evaluation metrics quantify how well a model performs, but the right metric depends on the task. The key metrics used in machine learning, and how to choose between them.
Trust Boundaries for a Local MCP Server That Reads Your Files
MCP server security for a local server that reads your files: where Stratigraphic Amenity draws its trust boundaries, and what it leaves to the host and OS.
How to Find the UTM Zone EPSG Code of a Scanned Map
A UTM zone EPSG code is a simple sum once datum and hemisphere are known. How we turn map text like "UTM N83 Zone 15" into EPSG:26915, and where it fails.
Agentic AI Benchmarks: The Benchmarks Shaping AI Agent Evaluation
Agentic AI benchmarks test whether an AI agent can finish real tasks with tools. What SWE-bench, τ-bench, GAIA and others check, and what they miss.
Aspect-Based Sentiment Analysis (ABSA): Types, Examples, and How to Train a Model
Aspect-based sentiment analysis scores sentiment per aspect of a text, not the text as a whole. The types of ABSA, a worked example, and how a model gets trained.
Weak Supervised Learning: Types, Techniques, and Frameworks
Weak supervised learning trains models on noisy, imprecise or indirect labels instead of hand-labeled ground truth. The types, techniques, and where it's actually used.
Synthetic Data vs Real Data: Differences, Limitations, and When to Use Each
Synthetic and real data fail differently and suit different stages of an AI pipeline. The core differences, the limitations, and when combining them beats choosing one.
Agent Skills vs. MCP: Main Differences and When to Use Each
Agent Skills and MCP solve different parts of the same problem: giving an agent new capability. What each one is, how they differ, and when to use which.
LLM Pre-Training: What It Is, Why It's Needed, and How the Process Works
LLM pre-training is what turns raw text into a language model before any fine-tuning happens. What the process involves, why it's necessary, and where post-training picks up.