Designing Human-in-the-Loop AI Systems
Place human review where it changes risk: define authority, evidence, escalation paths, feedback quality, and audit records.
Evaluation and Safety
Author archive
llms.help Editorial Team
Research, testing, and editorial analysis published by llms.help Editorial Team.
Context Window: What It Means and What It Does Not
A direct definition of context windows, token budgets, usable context, position effects, and practical input design.
LLM Fundamentals
Embeddings: A Developer-Friendly Definition
Understand what embedding vectors represent, how similarity search uses them, and how to test whether they fit your retrieval task.
Retrieval and RAG
Open-Weight vs Hosted LLMs: A Workload-First Comparison
Compare deployment control, privacy boundaries, reliability work, model quality, licensing, and total cost without a universal winner.
Model Selection
Retrieval-Augmented Generation: A Practical Tutorial
Design a small RAG system with traceable ingestion, retrieval, context assembly, citations, and stage-by-stage evaluation.
Retrieval and RAG
Prompt Engineering Fundamentals
Build prompts as testable contracts with explicit inputs, constraints, examples, output formats, and failure handling.
Prompt Engineering
How to Choose the Right LLM for Your Application
A durable, task-first framework for comparing model quality, reliability, latency, privacy, and total operating cost.
Model Selection
How Large Language Models Work
A practical mental model for tokens, attention, training, inference, context, and the limits of generated answers.
LLM Fundamentals