Topic
Prompt Engineering
Roles, context, constraints, examples, and practical prompt patterns for daily work.
38 stories (17 articles · 21 videos)
Start here
A few good first pieces before you browse the full feed.
8 min readArticle
The anatomy of a prompt: role, context, task, constraints, format
A five-part template that lifts answer quality from "meh" to "useful" on any prompt, in any AI tool. With worked examples, common mistakes, and the order that matters.
Build prompts with role, context, task, constraints, examples, and output format instead of relying on one-off wording tricks.
Beginner
6 min readArticle
The difference between a bad and good prompt (5 before/after examples)
Five real prompts shown in their weak version and their better version, with a short note on what changed. The fastest way to upgrade your AI output without learning any jargon.
Understand the idea well enough to try it safely in a low-risk setting.
New to AI
8 min readArticle
The ten prompt patterns every knowledge worker should know
Ten reusable patterns — not one-off prompts, but reusable shapes — that cover the vast majority of practical AI use at work. Memorise these and you have an entire prompt-engineering toolkit.
Choose prompt patterns by job type and pair them with validation instead of memorizing prompt recipes.
Beginner
More in this topic
12 min readArticle
Context engineering: managing 1M-token windows without context rot
1M-token context windows exist, but quality degrades long before that limit. Context engineering is the discipline of using context windows effectively — what to include, what to summarize, what to retrieve fresh, and the patterns that keep quality high as context grows.
Evaluate the implementation pattern, failure modes, and guardrails before building.
Advanced
12 min readArticle
Choosing between prompting, RAG, and fine-tuning (and when to combine)
Prompting, RAG, and fine-tuning are the three big levers for adapting LLMs to your problem. Each is right for some problems and wrong for others. A framework for choosing, the realistic costs of each, and the production patterns where combining them shines.
Use the article as decision context for adoption, risk, governance, or investment choices.
Advanced
12 min readArticle
Designing prompts for production: system, developer, and user layers
Production prompts are not 'tell the AI what you want'. They are a layered system — stable instructions, dynamic context, per-call variables — managed like code. The architecture, the patterns, and the discipline that distinguishes production from prototype.
Separate system, developer, and user instructions and test production prompts as versioned system components.
Advanced
10 min readArticle
Prompt engineering for reasoning models (o3, R1, Claude extended thinking)
Reasoning models are not fast models with extra steps. They reward different prompting, ignore some conventional patterns, and have their own pitfalls. A practical guide to working with them well.
Evaluate the implementation pattern, failure modes, and guardrails before building.
Intermediate
10 min readArticle
Chain-of-thought, self-critique, tree-of-thoughts — when to use each
Three reasoning techniques that genuinely improve AI output on hard problems — and the cost-benefit math of using them. With concrete prompts, side-by-side comparisons, and the gotchas modern reasoning models introduce.
Evaluate the implementation pattern, failure modes, and guardrails before building.
Intermediate
11 min readArticle
Multi-tool workflows: combining ChatGPT, Claude, Perplexity, and Notion
Most people use one AI tool for everything. Intermediate users orchestrate four or five — each for the part it does best. A practical guide to building multi-tool workflows that compound.
Design repeatable AI workflows across tools without losing source of truth, privacy boundaries, or handoff quality.
Intermediate
11 min readArticle
Building reusable prompt libraries: from snippets to shared templates
Once you use AI seriously, you write the same kinds of prompts over and over. A practical system for building, organising, and sharing a prompt library — what to capture, how to version, and what infrastructure to use.
Turn individual prompts into shared, versioned templates with owners, examples, and quality checks.
Intermediate
8 min readArticle
Using AI for better decisions: frameworks, pros/cons, and devil's advocate
AI is an unusually good sparring partner for decisions — if you stop using it as an answer machine. A workflow for using AI to make better choices, with the prompts that force it to disagree, not agree.
Turn the workflow into a small practical experiment with a clear quality check.
Beginner
7 min readArticle
Writing with AI without sounding like AI
AI writing has a smell. Once you know what it is, you can stop producing it. Six concrete tells, six fixes, and a workflow that uses AI to make your writing better without making it sound like everyone else's.
Turn the workflow into a small practical experiment with a clear quality check.
Beginner
7 min readArticle
Custom instructions and memory: set up your AI once
Spend ten minutes once and stop re-explaining yourself to ChatGPT every conversation. A practical guide to custom instructions, memory, and projects across the major AI tools.
Configure reusable assistant context while avoiding stale memory and accidental disclosure of sensitive details.
Beginner
7 min readArticle
Few-shot prompting: teach AI by example
When you cannot describe what you want, show it. Few-shot prompting — giving the model 2-3 examples of the output style — is the single fastest way to get consistent, on-brand results.
Turn the workflow into a small practical experiment with a clear quality check.
Beginner
8 min readArticle
Stop prompting once: the iterative conversation method
The single biggest gap between mediocre and good AI output is what happens after the first response. A workflow for turning any first draft into a real one through iteration — not by re-prompting from scratch.
Turn a weak first answer into usable output through critique, narrowing, pivots, and stress tests.
Beginner
6 min readArticle
How AI generates answers: the mental model that makes prompting click
AI does not think the way you do. It generates likely continuations from context. A plain-English mental model that, once it clicks, makes every prompting tip in the world easier to understand.
Use the next-token mental model to write better prompts without implying that models think like people.
New to AI
6 min readArticle
The seven things you can ask ChatGPT on day one
The blank input box is the hardest part. Seven copyable prompts that turn an empty conversation into a real win on your very first day.
Understand the idea well enough to try it safely in a low-risk setting.
New to AI
22 minutesVideo
Context Engineering for Agents
LangChain. Lance Martin's framework — write, select, compress, isolate — with concrete examples of when to summarise an action history, when to offload state to files, and when to spin up sub-agents purely to protect the parent's context. Maps almost directly onto the article's section on managing 1M-token windows in practice.
Advanced
8 minutesVideo
Context Rot: How Increasing Input Tokens Impacts LLM Performance
Chroma. Kelly Hong walking through Chroma's research on 18 models — why needle-in-haystack scores are misleading, how performance degrades with ambiguity and distractors, why even simple string-repetition tasks degrade past 500 tokens. Short, evidence-based, and exactly the case the article needs you to take seriously before getting to the engineering moves.
Advanced
25 minutesVideo
Prompting 101 | Code w/ Claude
Anthropic. A live build session by Anthropic's Applied AI team on an insurance-claims prompt — they start with a vague instruction and iterate to something a developer would actually ship, showing the kind of revisions the article describes for the system and developer layers. Watch this before re-reading the article's checklist on examples, output structure, and refusal handling.
Advanced
77 minutesVideo
AI prompt engineering: A deep dive
Anthropic. Four Anthropic prompt engineers (research, alignment, applied, developer relations) talking at length about what they actually do day to day — how they edit prompts under pressure, how they think about "honesty" in instructions, when XML scaffolds help, when they don't. The article's layered model maps cleanly onto how they describe the work; this is the best way to hear that mental model out loud.
Advanced
3 minutesVideo
Building OpenAI o1
OpenAI. Three minutes with the o1 team describing the moment the model started questioning its own reasoning during RL training. Useful as the primary source for the article's claim that the chain-of-thought is now happening inside the model, not in your prompt.
Intermediate
28 minutesVideo
o1 - What is Going On? Why o1 is a 3rd Paradigm of Model + 10 Things You Might Not Know
AI Explained. The clearest non-hype explanation of why o1 (and by extension o3, R1, Claude extended thinking) is trained for correctness instead of next-token prediction, and what that means for how you talk to it. Watch this and the article's "stop saying think step by step, start writing the spec" advice stops feeling arbitrary.
Intermediate
29 minutesVideo
Tree of Thoughts: Deliberate Problem Solving with Large Language Models (Full Paper Review)
Yannic Kilcher. Yannic walks through the original Tree-of-Thoughts paper from Princeton/DeepMind — the generate-score-prune-backtrack loop, why it lifts Game of 24 accuracy from 4% to 74%, and where the technique stops being worth its 10× token cost. Useful precisely because it forces you to see ToT as a search algorithm, not a magic prompt prefix.
Intermediate
25 minutesVideo
Prompting 101
Anthropic. Two members of Anthropic's Applied AI team build up a real-world prompt live, layering in role, structured sections and explicit thinking steps. It's the most credible "how to use chain-of-thought without ritualizing it" demo on YouTube, and it makes the article's "stop pasting Let's think step by step into everything" point concrete.
Intermediate
10 minutesVideo
The best ChatGPT Prompt I've ever created - I spent 2 months curating this prompt to write prompts
Lawton Learns. A concrete, copy-pasteable C.R.A.F.T. meta-prompt (Context, Role, Action, Format, Target audience) that the article references in spirit. Useful as the first entry in a new library — a prompt whose job is to generate well-structured prompts for everyone else.
Intermediate
20 minutesVideo
Google's 9 Hour AI Prompt Engineering Course In 20 Minutes
Tina Huang. Tina compresses Google's full prompting course into the two things a library actually stores: a five-part task/context/references/evaluate/iterate framework, and four named iteration moves to tighten a prompt over time. She also walks through agent and chain-of-thought patterns, so the same video doubles as the entry point for the more advanced templates you'll add to the library later.
Intermediate
26 minutesVideo
Every Essential AI Skill in 25 Minutes (2025)
Tina Huang. The prompting chapter (02:30–09:20) lays out two stackable mnemonics — "tiny crabs ride enormous iguanas" (task, context, references, evaluate, iterate) and "ramen saves tragic idiots" (revisit, separate, try analogous, introduce constraints) — that map cleanly onto the iterate-and-refine patterns in the article. Useful when a single-shot formula isn't getting you there.
Beginner
9 minutesVideo
Master the Perfect ChatGPT Prompt Formula (in just 8 minutes)!
Jeff Su. Six-part formula — task, context, exemplars, persona, format, tone — explained with the same office-work examples the article uses (workout plans, resumes, internal emails). Watch this first; almost every pattern in the article is a focused application of one of these six components.
Beginner
131 minutesVideo
How I use LLMs
Andrej Karpathy. Skip to the "ChatGPT memory, custom instructions" chapter at 1:53:29. Karpathy shows what he actually has in his own custom instructions, talks about when he tells ChatGPT to remember a fact versus letting it auto-capture, and notes the trade-offs — closer to the article's "set it up once with intent" framing than most beginner walkthroughs.
Beginner
36 minutesVideo
Every ChatGPT Feature In 37 Minutes
Tina Huang. The personalization, memory, and custom instructions panels are walked through inside a wider tour of every ChatGPT feature, which is the right framing — these settings only pay off when you also know what they're configuring (projects, GPTs, scheduled tasks, voice, etc.). Use it as a guided tour while you actually open Settings → Personalization in another tab.
Beginner
25 minutesVideo
Prompting 101 | Code w/ Claude
Anthropic. Examples are one of the ten ingredients Anthropic walks through here, and you can watch them get added to a real customer prompt and change the model's output. Useful if you want to see few-shot used inside a fuller prompt rather than as an isolated trick.
Beginner
42 minutesVideo
Prompt Engineering Tutorial – Master ChatGPT and LLM Responses
freeCodeCamp.org. Skip to the "Zero shot and few shot prompts" chapter at 31:20 for a clean, demoed comparison of the same task with no examples versus a few — exactly the contrast the article builds the chapter around. The earlier sections on prompt mindset and best practices are useful background if you've come straight from the article without the wider context.
Beginner
131 minutesVideo
How I use LLMs
Andrej Karpathy. Karpathy walks through his actual day-to-day use across ChatGPT, Claude, Gemini, Grok, and Perplexity, screen-sharing live conversations rather than polished one-shots. You see when he reaches for a follow-up, when he switches models mid-thread, and when he abandons a line and starts over — exactly the iterative loop the article describes, modeled by someone who builds these systems for a living.
Beginner
20 minutesVideo
Google's 9 Hour AI Prompt Engineering Course In 20 Minutes
Tina Huang. A condensed run through Google's prompt engineering course covering frameworks for clarity, iteration methods, and worked examples for emails, summaries, and analysis. Useful if you want a second pass that maps the same structural ideas onto the kinds of tasks you actually hit during a workday.
Beginner
25 minutesVideo
Prompting 101 | Code w/ Claude
Anthropic. Two members of Anthropic's Applied AI team build up a single prompt for a real customer use case (analyzing Swedish car accident reports), starting from a one-liner that fails and adding role, task context, dynamic content, examples, and final emphasis one layer at a time. It is the cleanest demonstration on YouTube of the exact "role + context + task + constraints + format" stack the article describes, with the failure modes shown along the way.
Beginner
42 minutesVideo
Prompt Engineering Tutorial – Master ChatGPT and LLM Responses
freeCodeCamp.org. A longer, more patient course that earns its length. The chapters on best practices, zero-shot vs few-shot, and what hallucinations look like in prompted output are exactly the deepening you want once the article's five examples have clicked.
New to AI
20 minutesVideo
Google's 9 Hour AI Prompt Engineering Course In 20 Minutes
Tina Huang. Tina condenses Google's official Prompting Essentials course into the five-part TCREI structure (Task, Context, References, Evaluate, Iterate) with worked examples on each. After the article shows you five rewrites, this hands you the recipe Google itself teaches employees, so you can do the rewrite on your own prompts without copying ours.
New to AI