> ## Documentation Index > Fetch the complete documentation index at: https://suprmind.ai/docs/llms.txt > Use this file to discover all available pages before exploring further. # Prompt Adjutant Examples # Prompt Adjutant Examples > **Quick Answer:** See real before/after examples across business, research, strategy, and technical use cases. ## What You'll Learn * How the Adjutant transforms different types of questions * Patterns you can apply to your own prompts * The difference good structure makes ## Business Strategy ### Before ``` we need to grow faster but don't have much budget and the team is already stretched thin not sure whether to focus on marketing or sales or product improvements ``` ### After ``` Evaluate three growth strategies for a resource-constrained SaaS startup: Context: - Team at capacity (no new hires planned for 6 months) - Limited marketing budget ($5K/month) - Current growth: 8% MoM (target: 15%) Compare: 1. Double down on marketing (content, SEO, community) 2. Build a sales motion (outbound, partnerships) 3. Improve product (retention, expansion revenue, referrals) For each strategy: - Expected ROI timeline - Resource requirements vs. our constraints - Risks if this doesn't work - Quick wins possible in first 30 days Recommend one primary strategy with supporting rationale. ``` ## Market Research ### Before ``` what do I need to know about the AI market for my pitch deck investors want to see market size and trends ``` ### After ``` Provide investor-grade market analysis for the AI productivity tools market: Required data points: 1. Total Addressable Market (TAM) with methodology 2. Serviceable Addressable Market (SAM) for multi-AI tools specifically 3. YoY growth rate (2023-2026) 4. Key market drivers and tailwinds 5. Notable funding rounds in this category (last 12 months) Format: Statistics with sources where available. Distinguish between verified data and estimates. Context: This is for a Series A pitch deck. Investors expect credible, sourced numbers - not aspirational projections. ``` ## Product Decision ### Before ``` should we build a mobile app users keep asking for one but it seems like a lot of work and we're not sure if mobile usage will be high enough to justify it ``` ### After ``` Evaluate the build/defer decision for a mobile app: Current state: - Web-only SaaS product - Users requesting mobile (frequency: ~5 requests/week) - Unknown actual mobile usage potential Analyze: 1. Demand validation: How to test mobile demand without building a full app (MVPs, responsive web, PWA options) 2. Resource investment: Engineering time for native vs. cross-platform vs. PWA approaches 3. Usage patterns: For our product type [describe product], what does mobile usage typically look like? Primary vs. supplementary use? 4. Competitive pressure: Do competitors have mobile? Does it matter? 5. Timing: Build now vs. defer 6 months - what do we gain/lose? Decision framework: Under what conditions should we definitely build, definitely defer, or test further? ``` ## Technical Architecture ### Before ``` our database is getting slow we have like 2 million rows and queries are taking forever need to figure out what to do ``` ### After ``` Diagnose and resolve database performance issues: Current state: - PostgreSQL database - ~2 million rows in primary table - Query times degrading (specifics needed: which queries?) - Application: [SaaS product type] Investigate: 1. Common causes of degradation at this scale (indexing, query patterns, connection pooling, hardware) 2. Diagnostic steps: What to check first (EXPLAIN ANALYZE patterns, pg_stat_statements, index usage) 3. Quick wins: Optimizations achievable without schema changes 4. Medium-term: Schema/architecture improvements if needed 5. When to consider: Read replicas, caching layer, or different DB entirely Prioritize solutions by: impact, implementation effort, and risk. At 2M rows, this shouldn't be slow - focus on identifying the root cause before jumping to solutions. ``` ## Content Creation ### Before ``` need to write something about our product launch for linkedin want to get engagement from potential customers who are decision makers ``` ### After ``` Develop a LinkedIn content strategy for an upcoming product launch: Target audience: Decision-makers (Directors+) at companies with 100-500 employees who currently use single-AI tools Goals: 1. Build awareness pre-launch (what's coming) 2. Drive sign-up interest 3. Position as thought leader in multi-AI space Provide: - 3 post concepts with hooks (first 2 lines visible in feed) - Recommended posting cadence (pre/during/post launch) - Content angles that resonate with this audience level - CTA approach that doesn't feel salesy - Engagement tactics specific to LinkedIn algorithm Tone: Authoritative but approachable. Not corporate, not casual. Think "experienced founder sharing insight" not "company making announcements." ``` ## Hiring ### Before ``` need to hire someone for marketing but not sure exactly what role or what seniority and we can't pay a ton ``` ### After ``` Define the right first marketing hire for an early-stage SaaS company: Constraints: - Budget: $80-120K total comp - Stage: Pre-Series A, 15 employees - Current marketing: Founder-led, ad hoc - Product: B2B, technical audience - Goal: Build repeatable acquisition engine Determine: 1. Role definition: Generalist vs. specialist? What specialization if specialist (content, growth, demand gen, product marketing)? 2. Seniority: Senior IC vs. mid-level? Pros/cons at our stage. 3. Key skills: What's non-negotiable vs. nice-to-have? 4. Profile: What does this person's resume look like? Where did they work before? What did they accomplish? 5. Assessment: 3 interview questions that reveal if they can operate independently at a startup (not just execute playbooks) Important: This person will work alone for 6+ months. Self-direction is critical. ``` ## Auto-Generated Prompts The examples above show the manual flow - you write raw thoughts, the Adjutant structures them. But the Adjutant can also **auto-generate** prompts based on your project context and conversation history. When you're not sure what to ask next, the system suggests a ready-to-use prompt tailored to your current work. Auto-generated prompts follow the same patterns shown above. You can use them as-is or open the **Refine panel** to adjust scope, format, or constraints before sending. ## The Patterns Looking across these examples, the Adjutant consistently: 1. **Adds context** - Industry, stage, constraints, current state 2. **Structures the ask** - Numbered points, clear categories 3. **Specifies format** - How to present the answer 4. **Sets constraints** - Budget, timeline, team size 5. **Asks for trade-offs** - Not just "what" but "vs. what alternative" 6. **Anticipates follow-ups** - Includes dimensions you'd ask about next ## Tips * Study these patterns. Over time, you'll naturally write prompts closer to the "After" versions. * The Adjutant isn't magic - it's structured thinking. You're teaching yourself to think more clearly about what you need. * If the Adjutant's output doesn't match your intent, use the **Refine panel** to adjust before sending - or add more specifics to your raw input and try again. * Auto-generated prompts are a good starting point when you're unsure where to begin a new conversation. ## Related Articles * [The Prompt Adjutant: Your Prompt Engineer](/docs/prompt-adjutant/what-is-prompt-adjutant/) * [When to Use the Prompt Adjutant](/docs/prompt-adjutant/when-to-use-it/) * [Conversation Best Practices](/docs/conversation-control/conversation-best-practices/) ## Still Need Help? Reach out to us at [support@suprmind.ai](mailto:support@suprmind.ai) or use the feedback button in the app.