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Browse 7 notes tagged prompt engineering.

#prompt engineering

Structured Output: The AI Returned JSON. Now What?

Compare format prompts, JSON mode, and schema constraints. Use metadata checks, timestamps, and a token-estimation counterexample to see what the application still needs to validate.

Context Placement: Where Should the Important Bits Go?

You supplied the context, but the answer missed it. Explore information placement, long documents, conversation summaries, and ways to test each change separately.

Core Prompting Techniques: When Should I Add Examples?

When one instruction isn't enough, should you add rules or examples? Compare standard, zero-shot, one-shot, and few-shot prompts using GitHub issues.

Standard Prompts: Start with a Clear Request

A short Practical Prompt Engineering note: improve a newsletter-form request with context, constraints, targeted clarification, and concrete checks.

Temperature and Top P: How a Model Selects the Next Token

Practical Prompt Engineering notes: use a five-token probability example to understand temperature, nucleus sampling, Top P 0.5, and the limits of consistent output.

Token Limits and Context Windows: What Can the Model See in This Turn?

Practical Prompt Engineering notes: distinguish tokens from words, context from output limits, and conversation history from memory, with a worked token budget.

What Is Prompt Engineering? Turning a Question into a Testable Task

Notes from Practical Prompt Engineering: use a JavaScript closure example to understand prompts, context, output requirements, and repeatable checks.