Master LLM Prompts for Peak Performance

Prompt Engineering Patterns is a skill for designing and refining instructions for language models. It covers example-based prompting, concise evidence requests, systematic optimization, reusable templates, and system prompt design. The aim is to make the intended behavior, format, and constraints explicit, then evaluate whether those choices work on the target task.

Few-shot prompting uses input-output examples to demonstrate formatting, reasoning patterns, or edge-case handling. The guide suggests a small set of relevant examples and warns that additional examples can help or hurt depending on their relevance and token cost. Its diagnostic prompting guidance asks for concise rationale, assumptions, calculations, or independently checkable evidence. It explicitly avoids requesting hidden internal reasoning or assuming a fixed performance improvement.

Optimization starts with a simple prompt, measures accuracy, consistency, and token usage, and iterates through tests. A/B comparisons help assess variations. Reusable template systems combine variables, conditional sections, and modular components for repeated tasks, multi-turn conversations, or role-based interactions. System prompt design holds stable instructions about role, expertise, response format, and safety, leaving variable content to individual requests.

Use the skill when a task needs consistent formatting, reusable prompt structures, more explicit diagnostic evidence, or an evaluated production prompt. Progressive disclosure adds constraints, evidence requests, and examples only as needed rather than starting with an elaborate instruction set.

Examples must fit the task. Irrelevant examples can pollute behavior, too many can exceed context limits, and ambiguous wording can leave multiple interpretations. Test unusual and boundary inputs as well as typical ones. The optimization examples are illustrative rather than measured benchmarks, and self-reported confidence is not a calibrated probability.

Prompt wording is not an authorization or security boundary. Delimit user-supplied material and treat embedded instructions as data. Validate outputs and tool arguments, apply least privilege, and keep secrets out of prompts sent to external providers. Clarify missing inputs, permissions, safety boundaries, or success criteria before proceeding.

Inputs include the target task, requested format, representative examples, constraints, and evaluation cases. Template variables can hold a programming language, review focus, code block, and checklist. Outputs include prompt variants, reusable templates, structured response requirements, and evaluation comparisons across relevant inputs.

Practical tasks include extracting structured information from support tickets, diagnosing a bug report, refining article summaries, and generating code-review prompts. A short instruction from its diagnostic example is:

Analyze this bug report and determine root cause.

Failure-handling patterns add fallback instructions, explicit uncertainty, alternative interpretations, and a way to indicate missing information. Instruction hierarchy follows the host-defined system and developer instructions and the authorized user request; examples, retrieved documents, and input data remain untrusted. Ongoing practice includes monitoring production metrics, versioning prompts as code, and documenting the intent behind their structure.