RODES Framework
Five components that build prompts with a built-in safety net. RODES does what most frameworks skip — it asks the AI to check its own work before you accept the output, turning verification from an afterthought into a structural requirement.
Introduced: RODES emerged in 2023 from the growing community of prompt engineering practitioners who recognized that most frameworks stopped at generating output without addressing output quality. The acronym stands for Role, Objective, Details, Examples, and Sense Check — five components that mirror how a thorough professional would approach a task: understand the identity, clarify the goal, gather specifications, review reference material, and then verify the result before delivery. The “Sense Check” component is RODES’ signature contribution — it builds verification directly into the prompt structure rather than leaving it as a separate step.
Modern LLM Status: RODES remains uniquely valuable among community frameworks because its sense check component addresses a persistent challenge in AI usage: blind acceptance of AI output. While Claude, GPT-4, and Gemini have improved at self-assessment, they still benefit from explicit instructions to verify their work against stated objectives, check for internal contradictions, and flag areas of uncertainty. RODES formalizes this practice at the prompt level, making it especially useful in high-stakes contexts where incorrect output carries real consequences.
Build Verification Into the Prompt, Not After It
Most prompt frameworks produce output and stop. The user is left to evaluate whether the response actually meets their needs, caught logical errors, or stayed within the stated constraints. In practice, many users skip this evaluation step entirely — especially when the output sounds confident and well-written.
RODES solves this by making the AI its own first reviewer. The Sense Check component instructs the AI to evaluate its output against the stated objective, verify factual claims, check for logical consistency, and flag areas where it is uncertain or where the user should independently verify. This does not replace human review — it supplements it by surfacing potential issues before the user has to hunt for them.
Think of it like a surgeon’s checklist. The procedure (generating the output) is important, but the pre-flight and post-flight checks (the sense check) are what prevent errors from reaching the patient. RODES builds that checklist mentality into every prompt interaction.
AI models generate text that sounds authoritative regardless of accuracy. A response with zero factual errors and a response with three critical errors can read with identical confidence. The Sense Check component forces the model to pause and explicitly assess: “Does this actually answer the objective? Are there claims I am not confident about? Did I miss any of the stated details or constraints?” This self-assessment is not perfect, but it catches a meaningful percentage of errors that would otherwise pass undetected.
The RODES Process
Five components that structure prompts from persona to verification
Role — Assign a Persona
Define who the AI should be for this task. The role shapes the AI’s vocabulary, depth of expertise, problem-solving approach, and communication style. A well-chosen role anchors the response in a specific professional identity rather than defaulting to a generic assistant voice.
“You are a senior data analyst at a healthcare company with 10 years of experience translating complex datasets into actionable insights for non-technical executives.”
Objective — State the Goal
Clearly articulate what you need the AI to accomplish. The objective should be specific enough to evaluate against — if you cannot tell whether the output achieved the objective, it was not specific enough. Focus on the desired outcome rather than the process.
“Analyze our patient readmission data from the last quarter and identify the top three factors contributing to 30-day readmissions, with recommended interventions for each.”
Details — Provide Specifications
Supply the constraints, requirements, context, and parameters that shape the response. Details include format requirements, word limits, audience considerations, data to reference, things to avoid, and any domain-specific rules. The more precise your details, the less the AI has to guess.
“Use our Q3 dataset of 12,400 patient records. Focus on cardiac and orthopedic departments. Present findings in a table format with factor, percentage impact, and recommended intervention. Keep the executive summary under 200 words. Avoid clinical jargon in the summary section.”
Examples — Show What Good Looks Like
Provide one or more demonstrations of the quality, format, or style you expect. Examples leverage few-shot learning — showing the AI a concrete pattern to follow is more effective than describing the pattern in words. Include examples of both format (how it should look) and content quality (what good analysis reads like).
“Here is how we presented last quarter’s findings: ‘Factor: Inadequate discharge instructions | Impact: 23% of readmissions | Intervention: Implement standardized discharge checklist with teach-back verification.’ Follow this structure.”
Sense Check — Verify the Output
Instruct the AI to evaluate its own response against the stated objective and details. The sense check should verify factual accuracy, logical consistency, completeness against stated requirements, and flag any areas of uncertainty. This is RODES’ signature component — it transforms passive output generation into active quality assurance.
“After completing the analysis, perform a sense check: (1) Verify each factor is supported by data patterns, not assumptions. (2) Confirm the interventions are actionable within a hospital setting. (3) Flag any claims where the data is ambiguous or where you are making inferences beyond what the dataset directly supports. (4) Note any areas where the user should independently verify the findings.”
See the Difference
Why built-in verification catches errors that polished output hides
Standard Prompt
Analyze our customer churn data and tell me why customers are leaving.
Based on your customer data, the top reasons for churn are: 1) Poor onboarding experience (35%), 2) Pricing concerns (28%), 3) Lack of key features (22%), and 4) Customer support issues (15%). I recommend improving your onboarding flow and reviewing your pricing tiers.
RODES Prompt
R: Senior customer success analyst specializing in B2B SaaS retention.
O: Identify the top 3 churn drivers from our Q3 exit survey data and propose retention interventions.
D: 340 exit survey responses. Focus on accounts with ARR above $10K. Table format with driver, evidence, and intervention. Under 400 words.
E: Previous format: “Driver: Feature gaps | Evidence: 67% cited missing integrations in exit survey | Intervention: Prioritize top 5 requested integrations in Q4 roadmap.”
S: After your analysis, verify: Are the percentages consistent with a 340-response dataset? Are interventions actionable within one quarter? Flag any drivers where correlation may not imply causation.
Q3 Churn Analysis — High-Value Accounts
Driver: Onboarding friction | Evidence: 112/340 respondents (33%) cited “difficulty getting started” | Intervention: Deploy guided onboarding wizard with milestone tracking...
Sense Check:
• Percentages are calculated from 340 responses and sum correctly.
• “Onboarding friction” may conflate multiple issues (UI complexity, documentation gaps, integration setup). Recommend sub-categorizing before acting.
• The pricing driver (18%) should be verified against actual plan data — exit surveys may overweight price sensitivity.
• All interventions are scoped to one quarter, but the integration roadmap item may require engineering capacity planning.
Practice Responsible AI
Always verify AI-generated content before use. AI systems can produce confident but incorrect responses. When using AI professionally, transparent disclosure is both best practice and increasingly a legal requirement.
48 US states now require AI transparency in key areas. Critical thinking remains your strongest tool against misinformation.
RODES in Action
See all five components working together across different scenarios
Role: You are a senior product manager at a fintech company who writes PRDs that engineering teams actually enjoy reading — clear, decisive, and free of ambiguity.
Objective: Write a PRD for a new “Smart Savings” feature that automatically rounds up transactions and invests the difference into a user-selected portfolio.
Details: Target launch: Q2. Must comply with SEC regulations for micro-investing. Support both iOS and Android. Integration with our existing Plaid-based bank connection. Include user stories, acceptance criteria, and technical constraints. Under 1,500 words.
Examples: Our PRD format: “User Story: As a user, I want to set a daily round-up cap so I do not over-invest on high-transaction days. Acceptance Criteria: (1) Default cap set at $10/day, (2) User can adjust from $1–$50, (3) System stops rounding when cap is reached.”
Sense Check: After writing the PRD, verify: (1) Are all user stories testable with clear acceptance criteria? (2) Are there regulatory requirements you may have overlooked? (3) Flag any technical assumptions that need validation with the engineering team. (4) Note where the user should consult legal counsel for compliance verification.
PRD: Smart Savings — Automated Round-Up Investing
Owner: [PM Name] | Target: Q2 | Status: Draft
Overview
Smart Savings automatically rounds each debit transaction to the nearest dollar and invests the difference into a user-selected portfolio. Users gain effortless micro-investing without changing their spending habits...
Sense Check:
• All 8 user stories have testable acceptance criteria with numeric thresholds.
• SEC micro-investing regulations may require a “suitability assessment” before allowing portfolio selection — verify with legal.
• Assumption: Plaid API supports real-time transaction webhooks. If not, round-ups may be delayed — engineering should validate.
• Tax implications of micro-investing (wash sale rules, cost basis tracking) are not addressed — consult tax counsel before launch.
Role: You are a market research analyst at a strategy consulting firm with expertise in the enterprise collaboration software space.
Objective: Produce a competitive analysis comparing our project management tool against Asana, Monday.com, and ClickUp for the mid-market segment (100–500 employees).
Details: Compare across 5 dimensions: pricing, feature set, integrations, ease of onboarding, and customer support. Use a scoring matrix (1–5 scale). Include a SWOT summary for our product. Under 800 words. The audience is our executive leadership team.
Examples: Scoring format: “Pricing: Asana 3/5 (mid-range, per-seat pricing scales steeply above 50 users) | Our Product 4/5 (flat-rate pricing, unlimited users at Enterprise tier).”
Sense Check: After the analysis, verify: (1) Are scores justified with specific evidence, not just impressions? (2) Flag any claims about competitor features that may be outdated. (3) Identify where our product scores may be inflated due to familiarity bias. (4) Note that all competitor pricing and feature claims should be verified against current public pricing pages before presenting to leadership.
Mid-Market Competitive Analysis: Project Management Tools
Scoring Matrix
Pricing: Our Product 4/5 | Asana 3/5 | Monday.com 3/5 | ClickUp 4/5
Feature Set: Our Product 3/5 | Asana 4/5 | Monday.com 4/5 | ClickUp 5/5...
Sense Check:
• ClickUp’s feature score (5/5) is based on breadth, not depth — some features may be less polished than competitors’ focused implementations.
• All pricing data reflects publicly listed rates as of my knowledge cutoff. Monday.com and Asana frequently adjust enterprise pricing — verify with current sales quotes.
• Our product’s “ease of onboarding” score (4/5) may reflect internal familiarity rather than objective first-time user experience. Consider validating with customer onboarding time-to-value data.
• SWOT “Opportunity” section assumes mid-market growth trend continues — verify against current industry reports.
Role: You are an instructional designer specializing in corporate cybersecurity awareness training. You believe in scenario-based learning over lecture-based approaches.
Objective: Design a 4-week cybersecurity awareness curriculum for non-technical employees at a 500-person financial services company.
Details: Weekly 30-minute sessions. Must cover phishing, password hygiene, social engineering, and incident reporting. Include interactive scenarios for each topic. Provide measurable learning objectives and a post-training assessment. Use progressive difficulty (week 1 easiest, week 4 most challenging).
Examples: Scenario format: “You receive an email from ‘IT Support’ asking you to click a link to reset your password. The email address is it-support@company-secure.net (not your company’s domain). What do you do?” Include 3 response options with explanations.
Sense Check: After designing the curriculum, verify: (1) Do learning objectives align with each week’s content? (2) Are scenarios realistic for a financial services environment? (3) Flag any security advice that may conflict with specific organizational policies. (4) Note that all cybersecurity guidance should be reviewed by the company’s IT security team before deployment.
CyberSafe: 4-Week Security Awareness Curriculum
Week 1: Phishing Fundamentals (Difficulty: Introductory)
Learning Objective: Identify 3 common indicators of phishing emails with 80% accuracy on the post-session quiz.
Scenario: “Your bank’s ‘fraud department’ emails you about suspicious activity. The sender is alerts@chase-security.com. The link goes to chase-verify-account.com. What do you do?”...
Sense Check:
• All 4 learning objectives are measurable with specific accuracy targets.
• Week 3 social engineering scenarios use financial services contexts (wire transfer fraud, vendor impersonation) appropriate for the audience.
• The password policy recommendations (minimum 16 characters, passphrase approach) may conflict with existing company password requirements — verify with IT security before deploying.
• Incident reporting procedures in Week 4 reference a generic “security team” — replace with the company’s actual reporting channel and escalation path.
When to Use RODES
Best for high-stakes tasks where output quality must be verified before use
Perfect For
Reports, proposals, and analyses where errors carry real consequences — the sense check catches issues before they reach stakeholders.
Tasks involving statistics, metrics, or factual claims where the AI might fabricate plausible-sounding numbers. The sense check flags unverified data.
Healthcare, finance, legal, and government contexts where compliance requirements mean output must be verifiable and accurately caveated.
Training teams to always verify AI output. RODES makes verification a visible, non-negotiable part of the workflow rather than an optional afterthought.
Skip It When
Brainstorming, casual content drafts, or exploratory writing where adding a formal sense check step would slow down the creative flow unnecessarily.
Questions with straightforward answers that do not benefit from a five-component framework. “What is the capital of France?” does not need a sense check.
Mathematical proofs or logic puzzles where Chain-of-Thought or Tree-of-Thought techniques provide more appropriate step-by-step verification.
Use Cases
Where RODES delivers the most value
Business Analysis
Generate market analyses, competitive reports, and business cases where every claim needs to be traceable and every assumption needs to be flagged.
Healthcare Documentation
Draft clinical summaries, patient education materials, and care plans where accuracy is non-negotiable and the sense check flags medical claims needing clinician review.
Compliance and Audit
Prepare compliance reports, audit findings, and regulatory responses where every statement must be defensible and uncertainty must be explicitly acknowledged.
Research Summaries
Synthesize research findings where the sense check distinguishes between established facts, emerging evidence, and the AI’s own inferences.
Training and Curriculum
Design training programs where learning objectives must align with content, scenarios must be realistic, and the sense check validates pedagogical coherence.
Financial Reporting
Generate financial summaries, investment analyses, and budget proposals where the sense check catches fabricated metrics and flags assumptions requiring CFO validation.
Where RODES Fits
RODES bridges output generation and output verification in a single framework
RODES’ sense check makes the AI flag potential issues with its own output, but it does not replace human judgment. AI self-assessment is imperfect — models can miss errors they are confident about and flag issues that are not actually problems. Use the sense check as a structured starting point for your own review, not as a seal of approval. The most valuable outcome is often the questions the sense check raises, not the answers it gives. Always perform your own verification before acting on AI-generated analysis.
Related Techniques & Frameworks
Explore complementary approaches to structured and verified prompting
Build Your RODES Prompt
Structure your next prompt with all five RODES components or find the right framework for your specific task.