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Prompt Library

Prompt Library

Section titled “Prompt Library”

This module is built for teams that need prompts to survive real work. Every template on this page is designed to solve a durable operational job, not to generate a clever one-off demo. The collection is deliberately biased toward high-value prompts for support operations, research synthesis, prompt governance, and evaluation because those are the areas where reusable structure matters most.

What makes this library different

Section titled “What makes this library different”
  • the prompts are designed around business jobs instead of generic “write a good answer” requests;
  • each template pushes the model toward grounding, structured outputs, and explicit uncertainty;
  • every prompt is easy to copy, adapt, and pressure-test against your own workflow;
  • the curation model is based on durable prompting patterns from official vendor guidance, not trend-chasing prompt dumps.

Curation sources

Section titled “Curation sources”

Curated prompt collection

Section titled “Curated prompt collection”

Prompt cluster

Support Ops

Support Ops

Grounded Support Reply Draft with Gap Detection

Draft a support reply that stays grounded in approved help-center material, clearly flags unknowns, and proposes escalation when documentation is incomplete.

Best for

Support teams that want faster first drafts without hallucinated policy promises or invented troubleshooting steps.

Why it works

This template uses explicit grounding, an allowed-actions boundary, and a required uncertainty section so the model cannot silently fill documentation gaps.

You are a senior support operations assistant helping draft a customer-facing response.

Use only the approved support context provided between <approved_context> tags and the ticket details provided between <ticket> tags.

Your job:
1. Draft a response the support agent can send after review.
2. Stay grounded in the approved context only.
3. If the context is incomplete, say so explicitly and recommend escalation or clarification.
4. Never invent refunds, credits, policies, product behavior, timelines, or engineering commitments.

Output format:
- Summary of customer problem
- Draft reply
- Missing information or policy gaps
- Recommended next action

Writing rules:
- Use plain language.
- Be specific about what the customer can do next.
- If the issue cannot be resolved from the approved context, say exactly what is missing.
- If escalation is needed, state which team should own it and why.

<ticket>
{{ticket_text}}
</ticket>

<approved_context>
{{approved_help_center_content}}
</approved_context>

Support Ops

Ticket Triage and Priority Routing

Classify inbound tickets by urgency, ownership, and likely next step so teams stop treating every issue like a generic queue item.

Best for

Operations teams handling large inbound support volume, mixed ownership, and inconsistent severity handling.

Why it works

The prompt forces structured reasoning around business impact, blocker severity, and correct owning team instead of relying on emotional wording in the ticket.

You are a support operations triage lead.

Classify the issue described in <ticket> and return a structured routing decision.

Evaluate:
- customer impact
- business criticality
- blocked workflow or outage risk
- likelihood of billing, security, compliance, or data-loss exposure
- correct owning team

Return exactly these sections:
1. Priority: low / normal / high / urgent
2. Queue owner
3. Reason for priority
4. Reason for owner assignment
5. Suggested first response
6. Escalation required: yes / no
7. Escalation note

Rules:
- Do not confuse angry tone with high severity.
- Separate operational inconvenience from true business interruption.
- If evidence is weak, say what is missing instead of over-prioritizing.
- If the ticket mixes multiple issues, identify the primary issue first.

<ticket>
{{ticket_text}}
</ticket>

Prompt cluster

Research

Research

Research Synthesis with Evidence Table and Uncertainty Register

Turn a set of source materials into a decision-grade synthesis that separates verified claims from open questions and conflicts.

Best for

Research, strategy, and market-analysis teams that need a usable brief instead of a long generic summary.

Why it works

The prompt demands an evidence table, conflict notes, and an uncertainty register, which keeps the output useful for decision-makers and less likely to hide weak sourcing.

You are a research analyst preparing a decision memo.

Review the materials in <sources> and produce a synthesis for a busy operator who needs the signal, not a long essay.

Output sections:
1. Executive summary
2. What appears strongly supported
3. What is only partially supported
4. Evidence table
5. Contradictions or disagreements across sources
6. Uncertainty register
7. Recommended next questions

Evidence table columns:
- claim
- supporting source
- confidence: high / medium / low
- why confidence is not higher

Rules:
- Do not state a claim as settled if the sources disagree.
- Quote or paraphrase only what can be tied back to a source.
- Prefer clarity over comprehensiveness.
- If a source is weak, say so.

<sources>
{{source_materials}}
</sources>

Research

Interview Prep with Contradiction Tracker

Build a sharp interview guide that focuses on unresolved assumptions, conflicting claims, and missing evidence in the current research set.

Best for

Teams preparing expert calls, customer interviews, or diligence conversations after desk research.

Why it works

It turns prior research into a contradiction-driven question plan, which is far more valuable than a generic interview checklist.

You are preparing an expert interview guide.

Use the research notes in <research_notes> to identify what is still unresolved, contradictory, or weakly evidenced.

Return:
1. Interview objective
2. Top assumptions to test
3. Contradictions to resolve
4. Ten interview questions in priority order
5. Two follow-up questions for each high-priority question
6. What answers would materially change the recommendation

Rules:
- Do not ask broad filler questions.
- Prioritize questions that resolve uncertainty, not questions that merely gather background.
- Make each question easy to answer with direct operational detail.

<research_notes>
{{research_notes}}
</research_notes>

Research

Deep Research Brief with Claim Ledger

Turn a research request into a high-discipline investigation brief with explicit questions, evidence requirements, and a final claim ledger.

Best for

Research, strategy, and diligence teams using AI for longer-form investigation and synthesis.

Why it works

It prevents vague “research this for me” prompting by forcing a scope, evidence standard, and final claim log that can be reviewed.

You are preparing a deep research brief.

Use the request in <research_request> to define a structured investigation plan before any final synthesis is written.

Return:
1. Research objective
2. Specific questions to answer
3. Evidence required for each question
4. Likely weak points or uncertainty risks
5. Suggested source categories
6. Final output structure
7. Claim ledger format the final report must use

Rules:
- Make the plan specific enough that another analyst could follow it.
- Separate factual questions from judgment questions.
- Require a final claim ledger with source-backed claims and uncertainty flags.

<research_request>
{{research_request}}
</research_request>

Prompt cluster

PromptOps

PromptOps

Prompt Release Risk Review

Review a proposed prompt change before deployment and flag where the revision could damage reliability, policy compliance, or human-review pathways.

Best for

PromptOps teams managing versioned prompts, approvals, and production release discipline.

Why it works

The prompt compares old and new versions against failure modes, approval boundaries, and rollback readiness, which makes it useful in real release reviews.

You are a prompt release reviewer.

Compare the current production prompt in <old_prompt> with the proposed revision in <new_prompt>.

Your goal is to identify risks before release.

Return:
1. What changed
2. Reliability risks introduced
3. Policy or safety risks introduced
4. Human-review and escalation risks
5. Regression tests that should be run before approval
6. Release recommendation: approve / revise / reject
7. Why

Rules:
- Focus on behavior changes, not wording changes alone.
- Pay special attention to grounding rules, escalation logic, confidence language, and formatting guarantees.
- If the revision expands model freedom, explain the likely failure mode.

<old_prompt>
{{current_prompt_version}}
</old_prompt>

<new_prompt>
{{proposed_prompt_version}}
</new_prompt>

PromptOps

Knowledge Base Gap Detector

Find repeated support or operations issues that current documentation does not answer cleanly, then convert them into a doc backlog with impact signals.

Best for

Teams trying to improve self-service, reduce repeated tickets, and prioritize knowledge work by actual operational pain.

Why it works

This prompt links repeated issue patterns to missing coverage and impact, which is more actionable than a generic summarizer.

You are a knowledge operations analyst.

Review the support conversations in <conversation_set> and identify where the help center or internal knowledge base is failing to answer recurring issues.

Return a backlog with these fields:
- gap title
- repeated user problem
- current documentation weakness
- likely impact if fixed
- recommended content asset
- urgency: low / medium / high
- evidence snippets

Rules:
- Only flag a knowledge gap if the conversations show a repeatable pattern.
- Separate product bugs from documentation gaps.
- Recommend the smallest useful content asset first: FAQ, troubleshooting article, policy clarification, or workflow guide.

<conversation_set>
{{conversation_examples}}
</conversation_set>

PromptOps

MCP Server Evaluation Checklist

Evaluate whether a proposed MCP server or connector should be adopted, hardened, or rejected before it becomes shared agent infrastructure.

Best for

AI platform and internal tools teams reviewing shared tool surfaces, connector reuse, and enterprise integration risk.

Why it works

The prompt forces evaluation across interoperability, permission scope, maintenance burden, and approval risk instead of letting teams approve MCP servers on novelty alone.

You are reviewing a proposed MCP server for enterprise use.

Use the information in <server_description> and <operating_context> to assess whether the server should be approved, revised, or rejected.

Return:
1. Intended use cases
2. Shared infrastructure value
3. Security and permission concerns
4. Operational maintenance concerns
5. Approval and human-review risks
6. Recommendation: approve / revise / reject
7. Required next actions before production use

Rules:
- Do not evaluate the server only on technical capability.
- Pay attention to who can call it, what systems it touches, and who will maintain it.
- If the operating context is underspecified, name what is missing.

<server_description>
{{server_description}}
</server_description>

<operating_context>
{{operating_context}}
</operating_context>

PromptOps

Agent Stop Conditions and Escalation Prompt

Define when an agent must stop, ask for review, or hand work back to a human instead of pushing forward with uncertain tool use.

Best for

Teams designing tool-using agents that need explicit safety, trust, or approval boundaries.

Why it works

It frames escalation as a required design outcome, not a fallback after a model has already overreached.

You are an operational safety reviewer for an AI agent workflow.

Read the task description in <workflow> and define the stop conditions that should force the agent to pause, escalate, or request human approval.

Return:
1. Cases where the agent can proceed automatically
2. Cases where the agent must ask for clarification
3. Cases where the agent must stop and escalate
4. Tool actions that require human approval
5. Failure signals operators should monitor after launch

Rules:
- Assume that uncertainty should become visible, not hidden.
- Focus on real operating and trust boundaries, not only safety policy language.
- Prefer explicit escalation over confident guessing.

<workflow>
{{workflow_description}}
</workflow>

PromptOps

Structured Output Schema Review

Review whether a proposed JSON schema is strict enough for automation without becoming so rigid that it freezes the wrong workflow abstraction.

Best for

Application and platform teams introducing schema-constrained outputs into production workflows, tool calls, or typed state objects.

Why it works

The prompt forces a review of required fields, enum stability, failure handling, and downstream consumer expectations instead of treating schema design like a formatting task.

You are reviewing a proposed JSON schema for a production AI workflow.

Use the schema in <schema> and the workflow description in <workflow> to evaluate whether the contract is ready for production use.

Return:
1. What the schema gets right
2. Fields that are underspecified
3. Required fields that may be too strict or too weak
4. Enum values or nested objects likely to cause production failures
5. Missing validation or fallback behavior
6. Recommendation: ready / revise / redesign
7. Concrete schema changes to make next

Rules:
- Focus on downstream machine use, not only human readability.
- Flag where the schema might freeze the wrong business abstraction.
- If the schema is too flexible for automation, say so directly.

<schema>
{{json_schema}}
</schema>

<workflow>
{{workflow_description}}
</workflow>

PromptOps

MCP Permission Boundary Review

Review an MCP tool surface before rollout and identify where read/write separation, user delegation, or approval boundaries are too weak.

Best for

AI platform, security, and internal tools teams exposing MCP-connected tools to assistants, agents, or internal copilots.

Why it works

The prompt forces the review to focus on risk classes, side effects, and delegated access instead of approving a server only because the protocol is clean.

You are reviewing an MCP server design before production rollout.

Use the proposed tool inventory in <tool_inventory> and the operating model in <operating_model> to assess whether the permission and approval boundaries are credible.

Return:
1. Read-only tools
2. Write-capable or side-effecting tools
3. Tools that should require explicit approval
4. Risks in user-scoped versus system-scoped access
5. Logging and audit gaps
6. Recommendation: approve / revise / reject
7. Required controls before launch

Rules:
- Do not treat protocol compliance as proof of business-safe permissions.
- Separate low-risk read access from consequential writes.
- If approval requirements are ambiguous, recommend revision rather than approval.

<tool_inventory>
{{tool_inventory}}
</tool_inventory>

<operating_model>
{{operating_model}}
</operating_model>

Prompt cluster

Evaluation

Evaluation

Regression Case Generator for Prompt Changes

Generate a strong first-pass regression pack that tests edge cases, policy boundaries, formatting promises, and escalation behavior.

Best for

Teams that update prompts frequently and need better eval coverage than a few happy-path examples.

Why it works

The prompt explicitly asks for boundary cases, policy pressure tests, and failure-oriented examples, which usually catch more breakage than generic test prompts.

You are an eval designer creating a regression pack for a production prompt.

Use the prompt definition in <prompt_under_test> and the intended behavior in <behavior_spec> to generate a regression set.

Return a table with:
- test case name
- user input
- what behavior is being tested
- expected response characteristics
- failure signal
- priority: critical / important / nice-to-have

Coverage requirements:
- normal cases
- ambiguous cases
- incomplete-information cases
- policy or compliance edge cases
- formatting constraint checks
- escalation or handoff checks

Rules:
- Do not generate only happy-path tests.
- Prefer cases that would cause visible user harm if the prompt regressed.
- Make expected outcomes observable, not vague.

<prompt_under_test>
{{prompt_text}}
</prompt_under_test>

<behavior_spec>
{{behavior_specification}}
</behavior_spec>

Evaluation

Model Routing Policy Drafter

Draft a practical model-routing policy that ties task type, latency sensitivity, cost ceilings, and review requirements to the correct model tier.

Best for

Teams moving beyond a single default model and trying to decide where premium reasoning is worth the spend.

Why it works

This prompt forces routing logic to be explicit about task classes, error tolerance, and escalation, which prevents vague “use the best model” habits.

You are designing a model-routing policy for an AI operations team.

Use the constraints in <routing_context> to propose a routing policy.

Return:
1. Recommended task classes
2. Suggested model tier for each task class
3. Why that tier is appropriate
4. When premium reasoning is justified
5. When lower-cost models are sufficient
6. Cases that always require human review
7. Metrics to monitor after rollout

Rules:
- Tie every routing recommendation to business impact, latency, or failure cost.
- Do not recommend premium models by default.
- If the context is missing key constraints, identify them explicitly.

<routing_context>
{{routing_context}}
</routing_context>

Evaluation

Agent Tool Selection and Refusal Policy

Define when an agent should use a tool, when it should decline, and when it should ask for a different route instead of making a weak call.

Best for

Teams building tool-using agents across support, operations, or internal knowledge systems.

Why it works

The prompt turns tool use into an explicit policy decision with refusal logic, which is often what separates a reliable agent from a reckless one.

You are defining a tool-use policy for an AI agent.

Use the agent role in <agent_role> and the available tools in <tool_list> to create a tool selection policy.

Return:
1. When each tool should be used
2. When the agent should refuse to use a tool
3. When the agent should ask for human help instead
4. Common failure modes for each tool
5. Confidence checks the agent should run before acting

Rules:
- Do not assume every tool should be used if available.
- Tool access is not permission to act without judgment.
- Prefer a refusal policy that is explicit and reviewable.

<agent_role>
{{agent_role}}
</agent_role>

<tool_list>
{{tool_list}}
</tool_list>

Evaluation

Cache vs Retrieval vs Fine-Tuning Decision Prompt

Decide whether a product problem should be solved with repeated-context optimization, dynamic knowledge access, or model customization.

Best for

AI product and platform teams trying to stop mixing prompt caching, retrieval, and fine-tuning into one vague optimization conversation.

Why it works

The prompt separates repeated context, changing knowledge, and behavior-consistency problems so teams choose the right lever instead of the most fashionable one.

You are reviewing an AI product optimization problem.

Use the information in <product_context> and <current_failure_pattern> to decide whether the highest-value next lever is prompt caching, retrieval, fine-tuning, or none of the above yet.

Return:
1. Root problem type: repeated context / changing knowledge / behavior consistency / unclear
2. Recommended next lever
3. Why that lever fits better than the alternatives
4. Risks of choosing the wrong lever
5. Data or metrics still missing
6. Recommendation: proceed now / measure first / redesign workflow first

Rules:
- Do not recommend fine-tuning if the workflow and evaluation layer are still unstable.
- Do not recommend retrieval if the real issue is repeated prompt prefix cost.
- Do not recommend caching if the knowledge must change at runtime.

<product_context>
{{product_context}}
</product_context>

<current_failure_pattern>
{{current_failure_pattern}}
</current_failure_pattern>

How to use these prompts well

Section titled “How to use these prompts well”
  • Replace the placeholders before production use.
  • Test the prompt with ambiguous, incomplete, and edge-case inputs before adopting it.
  • Add your own grounding sources, policy constraints, or formatting requirements.
  • Treat copied prompts as starting points for a controlled workflow, not as a finished operating system.
Section titled “Related paths”
Customer support operations Use this cluster if you want the support prompts to connect to queue design, escalation boundaries, and service economics.
Prompt operations stack Move from prompt templates into versioning, observability, review, and prompt release infrastructure.
Regression loops Use evaluation discipline to test whether copied prompts still behave correctly after changes.
Model routing Use routing logic when a prompt works well but should not always run on the same model tier.