prompt engineering
Techniques for writing prompts themselves.
42 tagged items
- PromptUniversal
Name the assumptions in a plan before you commit to it
Reads a written plan and lists what it quietly assumes without saying so, so you can check those assumptions before they turn into failures.
No-padding everyday default
The one to set if you set only one. Answer first, nothing before it, nothing after it, and no offer of further help.
Tested on Claude Sonnet · May 2026
- PromptUniversal
Audit a prompt for injection risk
Reviews your own system prompt the way an attacker would read it, and points to the exact sentence that gives something away.
Tested on Claude Sonnet · May 2026
- Before and afterUniversal
Asking for a summary
Same document, same model. The weak prompt returns the article rearranged. The strong one returns something you can act on, because it says who reads…
Tested on Claude Sonnet · May 2026
- GuideUniversal
Giving a model a role and constraints
How naming the audience, the format and the exclusions turns unpredictable answers into repeatable ones.
- PromptUniversal
Turn a vague request into a written brief
Takes a one-line request from a colleague or client and returns the questions that have to be answered before work starts.
- GuideUniversal
Working with long documents and code
Why long sessions drift, what a large context window actually buys you, and how to keep a model on task across a big input.
- GuideUniversal
Choosing a model for the job
A practical comparison of what to reach for when, based on the shape of the task rather than benchmark scores.
- PromptMidjourneyStable Diffusion
Image prompt skeleton
A fill-in structure for image models that keeps subject, environment, light, lens and style in the order the models weight them.
- GuideUniversal
Moving your rules into a config file
How to stop repeating the same instructions in every message, and what belongs in a system prompt against what belongs in the task.
- GuideUniversal
What a prompt actually is
The three parts every working prompt has, why their order changes the answer, and how to tell a vague prompt from a specific one.
- PromptUniversal
Strict code review of a diff
A review prompt that reports only what would break in production, gives the smallest fix for each finding, and is allowed to say the diff is fine.
- PromptUniversal
Extract structured data as JSON
A strict extraction prompt that returns valid JSON only, with nulls for missing fields instead of invented values.
Related terms
Chain of thought CoT
Prompt the model to write out its intermediate steps before giving the final answer.
Context window
The most text, counted in tokens, that a model can consider at once. Your prompt and its answer share that space.
Embeddings
Lists of numbers that stand for text, built so that similar meanings end up close together. Semantic search runs on them.
Few-shot prompting
Put two or three worked examples of the task in the prompt, on top of the description.
Fine-tuning
More training for an existing model on your own examples. It changes how the model behaves, not what it knows.
Hallucination
A fluent, confident answer that is plain wrong: an invented citation, function, statistic or fact.
Multimodal
A model that takes in or produces more than one kind of data, such as text with images, audio or video.
Retrieval-augmented generation RAG
Fetch the relevant documents first, paste them into the prompt, and the model answers from your data instead of its training.
System prompt
Instructions given to the model outside the conversation. They apply to every turn, not to one message.
Temperature
A sampling setting that decides how much randomness goes into picking each next token.
Token
The unit a model reads and writes, roughly a word fragment. Price, context limits and speed are all counted in tokens.