A Repeatable AI Research Workflow: From Question to Verified Brief
A disciplined research process that uses AI for planning and synthesis while keeping every important claim tied to evidence you have checked.
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Everything published here, newest first. Drafts are private until a person has verified their facts, sources and attribution, so this list only ever shows reviewed work.
25 articles · page 1 of 3
How to Summarise Long Documents with AI Without Missing What Matters
A practical, source-grounded workflow for turning long documents into reliable summaries while preserving caveats, contradictions and important detail.
AI Meeting Notes: A Safe Workflow from Transcript to Action Items
A careful end-to-end method for using AI to draft meeting notes without inventing decisions, owners or deadlines.
Prompting for Structured Output: JSON, Tables and Schemas
Learn how to prompt AI for dependable JSON, tables and schema-based responses, then validate the results before they enter your workflow.
Transformers vs RNNs: Why Attention Changed Machine Learning
Transformers changed machine learning by making relationships between tokens easier to learn at scale, but understanding their advantages means looking closely at what recurrent networks do well, where attention helps and what it still cannot solve.
What Is a Context Window and Why It Limits What AI Can Do
A context window determines how much information an AI model can work with at once, but using that space well matters as much as its size.
Retrieval-Augmented Generation (RAG) Explained for Beginners
Retrieval-augmented generation helps AI answer from selected sources rather than model memory alone, but its usefulness depends on what it retrieves and how carefully it uses the evidence.
Pre-training, Fine-tuning and RLHF: How Chatbots Are Trained
Pre-training builds a chatbot’s broad capabilities, fine-tuning shapes its responses, and RLHF uses human preferences to make its behaviour more useful—but none guarantees accuracy.
Why AI Hallucinates: Causes, Types and How to Reduce Them
AI hallucinations are plausible outputs that lack reliable support, and reducing them means improving evidence, task design and verification rather than simply asking a model to be accurate.