alivirgo Ask AI to List Its Hidden Assumptions (AI Skill)
Overview
Every time an AI answers a strategic, financial, or technical question, it silently fills undefined variables with default assumptions (e.g., assuming you have infinite bandwidth, a $50k monthly budget, standard US legal jurisdiction, or a team of 10 senior engineers).
The Assumption Surfacing Protocol forces the AI to declare all latent premises underneath its analysis - allowing you to adjust flawed presuppositions before making expensive decisions.
The Assumption Extraction Framework
┌─────────────────────────────────────────────────────────────┐
│ Assumption Surfacing Engine │
│ │
│ [ RECOMMENDATION / PLAN ] │
│ │ │
│ ▼ │
│ ┌───────────────────────────────────────────────────────┐ │
│ │ 1. INFRASTRUCTURE & TECH: Versions, tools assumed │ │
│ │ 2. TRAFFIC & SCALE: Requests/sec, database size │ │
│ │ 3. BUDGET & RESOURCES: Team headcount, licensing cost │ │
│ │ 4. USER BEHAVIOR: Conversion rates, latency tolerance │ │
│ └───────────────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ [ Sensitivity Check: If Assumption X changes $\rightarrow$ Impact ]│
└─────────────────────────────────────────────────────────────┘
Master Assumption-Audit Prompt Templates
Pattern 1: The Pre-Flight Assumption Audit
Use whenever receiving a complex technical, business, or marketing plan:
Review the plan you just proposed above.
List your **Top 4 Core Assumptions** across these categories:
1. **Technical / Environment Assumptions**: (e.g. cloud provider, SDK versions, OS)
2. **Scale & Traffic Assumptions**: (e.g. daily active users, requests per second)
3. **Resource & Team Assumptions**: (e.g. implementation hours, skillset required)
4. **Economic & Cost Assumptions**: (e.g. ongoing SaaS fees, egress costs)
For each assumption, answer:
*"If this assumption is false, what is the single biggest risk to the plan?"*
Pattern 2: The Sensitivity Inversion Test
Use when testing whether a plan holds up under extreme constraints:
What happens to your proposed architecture if:
- Our budget is reduced by 50%?
- Our traffic spikes by 10x overnight?
- We cannot use proprietary third-party APIs?
Provide the modified, fail-safe recommendations.
Real-World Case Study
Scenario: Recommending an Elasticsearch Cluster
Unstated Assumptions (The Hidden Trap)
User: "What search solution should we use for our e-commerce store with 500,000 products?"
AI Default Recommendation: "Set up an Elasticsearch cluster on AWS OpenSearch with 3 master nodes and 6 data nodes."
(Hidden Assumption: The team has a dedicated DevOps engineer to manage shard rebalancing, cluster memory heaps, and index lifecycle policies, and can afford $1,200/mo in AWS compute).
Surfaced Assumptions Output
User: "List the critical operational assumptions behind this recommendation."
AI Response:
Declared Assumptions:
- Budget: Assumes an infrastructure spend of $\ge $800/\text{month}$ for managed OpenSearch.
- Operations: Assumes your team has experience tuning JVM garbage collection and mapping analyzers.
- Query Complexity: Assumes you need complex fuzzy matching, vector semantic search, and multi-language stemmers.
Sensitivity Warning: "If you do not have dedicated DevOps staff or your monthly budget is $< $100$, Elasticsearch is an operational trap. In that scenario, use Meilisearch or Typesense on a single $20/mo VPS instead."
Summary Best Practices
- Never approve a plan without reading its assumptions: It is where 90% of real-world budget blowouts hide.
- Invert assumptions early: Testing "What if we have 1/5th the time?" immediately surfaces simpler, higher-ROI alternatives.