prompt-engineer

name	prompt-engineer
description	Review, generate, optimize, and look up prompt engineering techniques for LLM systems.

Prompt Engineer Skill

Systematic prompt engineering using established techniques. Four modes: review, generate, lookup, optimize.

When to trigger

• User asks: "review this prompt", "critique my prompt", "what's wrong with this prompt"
• User asks: "write a prompt for", "generate a prompt", "prompt for X"
• User asks: "what technique", "how does CoT work", "explain ReAct"
• User asks: "optimize this prompt", "fix this prompt", "the model keeps doing X"
• User invokes /prompt-engineer

Modes

Detect mode from context. If ambiguous, ask.

Mode 1: Review (/prompt-engineer review)

Analyze an existing prompt for weaknesses and improvements.

Process:

1. Get the prompt (user pastes it, or read from file)

2. Identify the four elements present or missing:

   • Instruction -- specific task or command
   • Context -- background information, constraints, domain
   • Input Data -- the data to process
   • Output Indicator -- format, type, or structure of expected output

3. Evaluate against these criteria:

   Structure:

   • Is the instruction at the beginning, clearly separated?
   • Are delimiters used to separate instruction/context/input (###, ```, XML tags)?
   • Is the output format specified?

   Specificity:

   • Are instructions precise or vague? ("Write something about X" vs "Write a 2-paragraph summary of X for a technical audience")
   • Does it use "do" framing instead of "don't" framing?
   • Are constraints explicit (length, format, tone, audience)?

   Technique gaps:

   • Would few-shot examples improve consistency?
   • Would CoT improve reasoning? ("Let's think step by step")
   • Is the model asked to do too much in one pass? (needs prompt chaining)
   • Is external knowledge needed? (needs RAG or generated knowledge)
   • For tool-use prompts: are tool descriptions clear, with examples?

   Small model considerations:

   • For models under 8B: is the prompt too complex? Small models need simpler, more explicit instructions.
   • Are output rules repeated/reinforced? Small models forget rules mid-generation.
   • Are negative examples provided alongside positive? Small models follow "do X" better than "never do Y".

   Adversarial robustness:

   • Can user input override instructions? (prompt injection risk)
   • Are system/user boundaries clear?
   • Is the prompt leakable?

4. Output findings as prioritized list (P0-P3) with specific rewrites.

Mode 2: Generate (/prompt-engineer generate)

Create a prompt from a task description.

Process:

1. Clarify: what model? what task? what input/output? what failure modes to avoid?

2. Select technique based on task type:

   Task Type	Recommended Technique
   Simple classification/extraction	Zero-shot with output format
   Tasks needing consistent format	Few-shot (1-5 examples)
   Math, logic, multi-step reasoning	Chain-of-Thought or PAL
   Complex reasoning with exploration	Tree of Thoughts
   Knowledge-intensive QA	RAG or Generated Knowledge
   Multi-step with tools	ReAct (Thought/Action/Observation)
   Long complex workflows	Prompt Chaining (subtask pipeline)
   Tasks needing self-correction	Reflexion (actor/evaluator/reflect loop)
   Noisy reasoning tasks	Self-Consistency (sample k, majority vote)

3. Build the prompt using this structure:

   [System context / role]
   [Constraints and rules]
   [Output format specification]
   [Few-shot examples if needed]
   [Input delimiter]
   [Input data]
   [Output delimiter / indicator]
   

4. For small models (under 8B):

   • Keep system prompt under 500 tokens
   • Use explicit section headers (## Rules, ## Output Format)
   • Repeat critical rules near the output indicator
   • Prefer positive instructions over prohibitions
   • Use structured output (JSON, markdown) over free-form

5. Output the complete prompt with annotations explaining technique choices.

Mode 3: Lookup (/prompt-engineer lookup <technique>)

Explain a prompting technique with actionable pattern.

Technique Reference:

Zero-Shot Prompting

• What: Direct instruction with no examples. Relies on model's training.
• When: Simple tasks (classification, extraction, translation). Try first.
• Pattern: [Instruction]\n[Input]\n[Output indicator]

Few-Shot Prompting

• What: Provide 1-5 labeled demonstrations before the query.
• When: Zero-shot output format is inconsistent. Need to steer style/format.
• Pattern: Example 1: [input] -> [output]\nExample 2: [input] -> [output]\n[query] ->

Chain-of-Thought (CoT)

• What: Elicit intermediate reasoning steps before the final answer.
• When: Math, logic, multi-step reasoning. Model jumps to wrong conclusions.
• Pattern: Add "Let's think step by step." or show worked examples.

Zero-Shot CoT

• What: Append "Let's think step by step" without demonstrations.
• When: No examples available but reasoning is needed.
• Pattern: [Question]\n\nLet's think step by step.

Self-Consistency

• What: Sample k diverse reasoning paths, take majority vote on answer.
• When: Arithmetic, commonsense reasoning. Single CoT path may be wrong.
• Pattern: Generate 5-10 CoT outputs at temperature > 0, aggregate answers.

Tree of Thoughts (ToT)

• What: Explore reasoning as a tree with BFS/DFS and self-evaluation.
• When: Complex problems needing backtracking (puzzles, planning, math proofs).
• Pattern: Generate candidates, evaluate as sure/maybe/impossible, search.

RAG (Retrieval-Augmented Generation)

• What: Retrieve external documents, concatenate with prompt before generation.
• When: Factual QA, evolving knowledge, need to ground outputs.
• Pattern: [Retrieved context]\n[Instruction]\n[Query]

ReAct (Reasoning + Acting)

• What: Interleave reasoning traces with tool actions.
• When: Multi-step tasks needing external data (search, API calls, DB queries).
• Pattern: Thought: [reasoning]\nAction: [tool call]\nObservation: [result]\n...repeat

Prompt Chaining

• What: Pipeline subtasks, feeding each output as next input.
• When: Complex tasks with multiple transformations. Single prompt too complex.
• Pattern: Prompt 1 -> Output 1 -> Prompt 2 (with Output 1) -> Final

PAL (Program-Aided Language)

• What: LLM generates executable code as reasoning; run code for answer.
• When: Math, date calculation, logic where exact computation matters.
• Pattern: Prompt for Python code, execute, read result.

Generated Knowledge

• What: First generate relevant knowledge, then use it to answer.
• When: Commonsense reasoning, domain tasks where model lacks context.
• Pattern: Step 1: "Generate facts about X." Step 2: "Given these facts, answer Y."

Reflexion

• What: Agent generates attempt, evaluates it, reflects on mistakes, retries.
• When: Trial-and-error tasks (coding, decision-making, iterative improvement).
• Pattern: Act -> Evaluate -> Reflect (verbal feedback to memory) -> Retry.

Active-Prompt

• What: Identify high-uncertainty examples, get human annotations, use as exemplars.
• When: Need task-specific few-shot examples but unsure which ones help most.
• Pattern: Sample k outputs -> measure disagreement -> annotate uncertain cases.

APE (Automatic Prompt Engineer)

• What: LLM generates and evaluates instruction candidates; select best performer.
• When: Optimizing prompts at scale. Manual iteration too slow.
• Pattern: Generate N instructions -> score each -> select top performer.

ART (Automatic Reasoning and Tool-use)

• What: Auto-select reasoning and tool-use demonstrations from a library.
• When: New tasks needing tool integration without manual prompt engineering.
• Pattern: Retrieve relevant demos -> interleave generation with tool calls.

Meta Prompting

• What: Use abstract structural templates instead of specific content examples.
• When: Token-efficient prompting. Structure matters more than content.
• Pattern: Show structural skeleton, not filled-in examples.

Directional Stimulus Prompting (DSP)

• What: Small policy model generates hints to steer a larger frozen model.
• When: Fine-grained control of black-box LLM behavior. Requires training budget.
• Pattern: Policy LM hint -> main LLM generation guided by hint.

Multimodal CoT

• What: Combine text and vision inputs for two-stage reasoning.
• When: Tasks requiring both text and image understanding.
• Pattern: Stage 1: generate rationale from text+image. Stage 2: answer from rationale.

Mode 4: Optimize (/prompt-engineer optimize)

Fix a prompt based on observed failure mode.

Process:

1. Get the prompt and the failure description (what the model does wrong)

2. Diagnose using this failure-to-technique map:

   Failure Mode	Likely Cause	Fix
   Model hallucinates facts	No grounding, no constraints	Add "Only state facts present in [context]." Add RAG.
   Model ignores instructions	Instructions buried, too many rules	Move instructions to top. Use delimiters. Reduce rule count.
   Output format inconsistent	No format spec, no examples	Add output indicator. Add 1-2 few-shot examples.
   Model can't reason through problem	No reasoning elicitation	Add CoT ("Let's think step by step") or few-shot with worked examples.
   Model does too much / rambles	No length/scope constraints	Add explicit length ("2-3 sentences"), scope ("only X, not Y").
   Model leaks internal data	System/user boundary unclear, raw IDs in context	Sanitize context before delivery. Reinforce output rules.
   Model answers "I don't know" when data exists	Context trimmed, budget too tight, data not reaching model	Check context pipeline. Increase budget. Verify data delivery.
   Small model forgets rules mid-output	System prompt too long, rules only stated once	Shorten prompt. Repeat critical rules near output. Use structured output.
   Model parrots input verbatim	No transformation instruction	Add explicit transformation: "Summarize", "Rewrite as", "Extract only".
   Tool use syntax wrong	Tool descriptions unclear	Add concrete tool call examples. Show expected input/output format.
   Model calls tools when it shouldn't	No "when to use tools" guidance	Add decision criteria: "Only call tools when [condition]. Otherwise answer from context."

3. Apply fix. Show before/after with explanation of which technique was applied and why.

Principles

These apply across all modes:

1. Instruction first. Place the task instruction at the top, separated from context.
2. Be specific, not clever. "Use 2-3 sentences to explain X to a high school student" beats "Keep it short and simple."
3. Do, not don't. "Respond with: Sorry, I can't help with that." beats "Don't answer questions about X."
4. Delimiters matter. Use ###, ```, XML tags, or markdown headers to separate sections.
5. Output format is an instruction. Always specify what the output should look like.
6. Few-shot is format control. Examples steer output structure more than instructions alone.
7. Small models need repetition. Repeat critical rules near the output point.
8. Context is finite. Every token in the prompt competes with output tokens. Be economical.
9. Test adversarially. Try inputs that break the prompt before shipping it.
10. Iterate. Prompts are code. Version them, test them, diff them.

Tool access

allowed-tools:

• Read
• Write
• Grep
• Glob
• Bash

Output method

1. For review and optimize: output findings inline (no file needed unless user asks).
2. For generate: output the complete prompt in a code block. If user asks, write to file.
3. For lookup: output the technique reference inline.
4. For all modes: if the prompt is for a specific project, read existing prompts/system prompts from codebase for context.