About How Professionals Use AI: Text Generation

Retrieval-Augmented Generation (RAG) Tool-Augmented Language Models and Function Calling Matching Review Effort to the Cost of Being Wrong Source-Bound Generation: Restricting Output to a Supplied Corpus Conversational Agents and Dialogue Systems Where Prompt Tricks Still Pay: Small and Local Models Error Analysis: Reading Traces Until Failure Modes Stop Appearing Hallucination in Large Language Models In-Context Learning in Large Language Models Crafting Effective Prompts Large Language Models Predict Next Words Using Transformer Attention Mechanisms Context Layout and Ordering: Where in the Window Information Lands Improving a Prompt from Its Failure Cases Rather Than by Rewording Scoring a Prompt Against a Dataset Instead of Eyeballing One Output Task Specification: Job, Audience, Constraints, Output Shape Human Evaluation and Rank Correlation in NLP Prompt Engineering for Text Generation Role-Play Persona Preambles in Prompts Hallucination Risk and Verification in AI Research Workflows Assembling a Small Labelled Eval Set Before a Prompt Reaches Production Machine-Checkable Output Contracts for Generated Text Worked Examples in the Prompt as a Specification of the Output You Want Temperature and Sampling in Language Model Decoding Large Language Models in Natural Language Processing Sycophancy in Large Language Models Chain-of-Thought Prompting Chunking and Retrieval Quality as the Ceiling on a Grounded Answer NotebookLM as a Closed-Corpus Research Assistant Magic Phrases and Incentive Framing in Prompts Chain-of-Thought as a Model Behaviour You Can Read, Not a Phrase You Type LLM-Based Autonomous Agents Guarding Against Regression When a Prompt or Model Changes Uncertainty Estimation in Large Language Models Compaction: Summarising a Long Session to Reclaim Context Self-Refinement in Large Language Models Controlling Reasoning Effort Instead of Scripting the Reasoning Corpus Poisoning and Prompt-Injection Attacks on Retrieval Systems The Context Window as a Finite Budget You Spend Token-Efficient Prompt Engineering Strategies Context Engineering as the Successor to Prompt Engineering Context Rot: Accuracy Decay Well Before the Window Is Full Long-Context Language Modeling Confidence Calibration in Classification Models Checking That a Citation Supports the Sentence Attached to It Automatic Evaluation Metrics for Natural Language Generation Model Context Protocol as a Standard Way to Hand a Model Tools LLM-as-Judge Grading Calibrated Against Human Labels Automatic Evaluation and Benchmarking of Large Language Models