a quick reference for the AI fluency framework and related terminology. bookmark this and use it alongside the series.
the 4Ds — core competencies#
AI Fluency the ability to work with AI effectively, efficiently, ethically, and safely.
Delegation deciding what work to do yourself, what to collaborate on with AI, and what to let AI handle independently.
- Problem Awareness — understand your goals before involving AI
- Platform Awareness — know the capabilities and limits of different AI systems
- Task Delegation — distribute work intelligently between human and AI
Description communicating clearly and intentionally with AI systems.
- Product Description — define what you want in terms of output, format, audience, style
- Process Description — guide how AI should approach the task
- Performance Description — specify how AI should behave during the collaboration
Discernment critically evaluating AI outputs, reasoning, and behavior.
- Product Discernment — evaluate accuracy, relevance, coherence of outputs
- Process Discernment — assess how AI arrived at its output
- Performance Discernment — evaluate whether AI’s behavior served your needs
Diligence using AI responsibly and taking accountability for AI-assisted work.
- Creation Diligence — be thoughtful about which AI systems you use
- Transparency Diligence — be honest about AI’s role in your work
- Deployment Diligence — verify and vouch for outputs before sharing
three ways to work with AI#
Automation AI executes specific tasks based on your instructions.
Augmentation you and AI collaborate as thinking partners, iteratively.
Agency you configure AI to work independently on your behalf.
generative AI and LLM concepts#
Generative AI AI that creates new content rather than just analyzing existing data.
Large Language Models (LLMs) generative AI trained on vast text data to understand and generate human language.
Parameters mathematical values within a model that determine how it processes information. modern LLMs contain billions.
Transformer Architecture the 2017 breakthrough that enabled LLMs to process text in parallel while understanding long-range relationships between words.
Pre-training initial training phase where models learn language patterns from massive datasets.
Fine-tuning additional training that shapes raw capability into helpful, safe, instruction-following behavior.
Context Window the maximum amount of information an AI can consider at one time. exceeding it causes earlier context to drop.
Hallucination when AI confidently states something plausible but incorrect.
Knowledge Cutoff Date the point after which an AI has no built-in knowledge of the world.
Temperature controls randomness in AI responses. higher = more creative. lower = more predictable.
RAG (Retrieval Augmented Generation) connects AI to external knowledge sources to improve accuracy and reduce hallucinations.
Bias systematic patterns in AI outputs that unfairly favor or disadvantage certain groups, often reflecting training data patterns.
prompting concepts#
Prompt the input given to an AI model — instructions, context, documents.
Prompt Engineering the practice of designing effective prompts to produce desired outputs.
Chain-of-thought Prompting guiding AI to work through a problem step by step before answering.
Few-shot Learning teaching AI by showing examples of desired input-output patterns.
Role or Persona Definition specifying a character, expertise level, or communication style for AI to adopt.
Output Constraints specifying format, length, structure, or other characteristics of the desired response.
Think-first Approach explicitly asking AI to reason through a problem before providing a final answer.
this cheat sheet is based on the AI Fluency: Framework and Foundations course by Rick Dakan, Joseph Feller, and Anthropic. released under CC BY-NC-SA 4.0.
took ai help to clean up typos. my brain works faster than my fingers. xd
back to series index — still figuring out AI, one post at a time
