// Crafting effective prompts for LLMs. Use when designing prompts, improving output quality, structuring complex instructions, or debugging poor model responses.
| name | prompt-engineering |
| description | Crafting effective prompts for LLMs. Use when designing prompts, improving output quality, structuring complex instructions, or debugging poor model responses. |
Prompt engineering is the practice of designing inputs that guide LLMs to produce desired outputs. Effective prompts reduce errors, improve consistency, and unlock model capabilities.
Golden rule: Show your prompt to a colleague with minimal context. If they're confused, the model will be too.
Treat the model as a capable but context-free collaborator. Specify:
# Vague
Analyze this data and give insights.
# Specific
Analyze this Q2 sales data for our board presentation.
1. Identify the top 3 revenue trends
2. Flag any anomalies exceeding 15% variance
3. Recommend 2-3 actionable next steps
Format as bullet points, max 200 words.
Use numbered lists for multi-step tasks:
Your task is to anonymize customer feedback.
Instructions:
1. Replace customer names with "CUSTOMER_[ID]"
2. Replace emails with "EMAIL_[ID]@example.com"
3. Redact phone numbers as "PHONE_[ID]"
4. Leave product names intact
5. Output only processed messages, separated by "---"
Provide 3-5 diverse examples to demonstrate expected behavior. Examples reduce misinterpretation and enforce consistent formatting.
Categorize customer feedback by issue type and sentiment.
<examples>
<example>
Input: The dashboard loads slowly and the export button is hidden.
Category: UI/UX, Performance
Sentiment: Negative
Priority: High
</example>
<example>
Input: Love the Salesforce integration! Would be great to add Hubspot.
Category: Integration, Feature Request
Sentiment: Positive
Priority: Medium
</example>
</examples>
Now categorize: {{FEEDBACK}}
<example> tags for clarityFor complex tasks, guide the model to reason before answering, but ask for concise conclusions or verifiable work rather than unrestricted hidden chain-of-thought.
Determine the best investment option for this client. Think step-by-step.
Specify what steps to consider:
Think before answering:
1. Consider the client's risk tolerance given their 5-year timeline
2. Calculate potential returns for each option
3. Factor in market volatility history
4. Then provide your recommendation
Separate private analysis instructions from the required answer format:
Analyze this contract for legal risks.
Before answering, check:
- Indemnification implications
- Liability exposure
- IP ownership concerns
Then provide:
<answer>
- Top risks
- Recommended edits
- Open questions
</answer>
When you need auditability, ask for brief supporting rationale, calculations, cited evidence, or a checklist of checks performed. Do not require full chain-of-thought unless the target model or product explicitly supports exposing it.
Use XML tags to separate prompt components. This prevents instruction/content confusion and improves parseability.
<instructions>Task steps and requirements</instructions>
<context>Background information</context>
<document>Source material to process</document>
<example>Demonstration of expected behavior</example>
<constraints>Boundaries and limitations</constraints>
<output_format>Expected response structure</output_format>
<documents>
<document index="1">
<source>annual_report_2023.pdf</source>
<content>{{REPORT_CONTENT}}</content>
</document>
<document index="2">
<source>competitor_analysis.xlsx</source>
<content>{{ANALYSIS_CONTENT}}</content>
</document>
</documents>
<instructions>
Compare revenue trends across both documents.
Identify strategic advantages mentioned in the annual report.
</instructions>
Be explicit when referring to tagged content:
Using the contract in <contract> tags, identify all clauses
related to termination.
Set expertise context via system prompts to improve domain-specific performance.
system = "You are a senior securities lawyer at a Fortune 500 company."
user = "Review this acquisition agreement for regulatory risks."
# General
You are a [role] at [organization type].
# Specific (better)
You are the General Counsel of a Fortune 500 tech company
specializing in M&A transactions.
# With behavioral guidance (best)
You are a senior data scientist. You prioritize statistical
rigor over speed. When uncertain, you state assumptions
explicitly and suggest validation approaches.
For prompts with large documents (20K+ tokens):
Place long documents at the top, before instructions:
<documents>
{{LARGE_DOCUMENT_CONTENT}}
</documents>
<instructions>
Summarize the key findings from the document above.
Focus on financial implications.
</instructions>
Ask the model to cite sources before analyzing:
<documents>
{{PATIENT_RECORDS}}
</documents>
First, find and quote the relevant sections in <quotes> tags.
Then provide your diagnosis in <analysis> tags, referencing
the quoted evidence.
Include source information for attribution:
<documents>
<document index="1">
<source>quarterly_report_q2.pdf</source>
<date>2024-07-15</date>
<content>{{CONTENT}}</content>
</document>
</documents>
<output_format>
- Default responses: 3-6 sentences or ≤5 bullets
- Simple factual questions: ≤2 sentences
- Complex analysis: 1 overview paragraph + ≤5 tagged bullets
</output_format>
Output requirements:
- Use markdown tables for comparisons
- Code blocks for any technical content
- No introductory phrases ("Here's...", "Sure...")
- End with exactly 3 action items
Prevent drift from original intent:
Implement EXACTLY and ONLY what is requested.
- Do not add features beyond the specification
- Do not refactor surrounding code
- Choose the simplest valid interpretation
- Ask for clarification rather than assuming
For high-stakes outputs, include verification steps:
<verification>
Before finalizing your response:
1. Re-read the original request
2. Check that all requirements are addressed
3. Verify any specific claims against provided documents
4. Soften language where certainty is low
5. Flag any assumptions you made
</verification>
When uncertain:
- Explicitly state "Based on the provided context..."
- Offer 2-3 plausible interpretations if ambiguous
- Never fabricate specific details (dates, numbers, quotes)
- Say "I don't have enough information to..." when applicable
Patterns and architectures for building AI agents and workflows with LLMs. Use when designing systems that involve tool use, multi-step reasoning, autonomous decision-making, or orchestration of LLM-driven tasks.
Strategies for managing LLM context windows effectively in AI agents. Use when building agents that handle long conversations, multi-step tasks, tool orchestration, or need to maintain coherence across extended interactions.
Parameter-efficient fine-tuning with Low-Rank Adaptation (LoRA). Use when fine-tuning large language models with limited GPU memory, creating task-specific adapters, or when you need to train multiple specialized models from a single base.
Running and fine-tuning LLMs on Apple Silicon with MLX. Use when working with models locally on Mac, converting Hugging Face models to MLX format, fine-tuning with LoRA/QLoRA on Apple Silicon, or serving models via HTTP API.
Building and training neural networks with PyTorch. Use when implementing deep learning models, training loops, data pipelines, model optimization with torch.compile, distributed training, or deploying PyTorch models.
Memory-efficient fine-tuning with 4-bit quantization and LoRA adapters. Use when fine-tuning large models (7B+) on consumer GPUs, when VRAM is limited, or when standard LoRA still exceeds memory. Builds on the lora skill.