AI Crucible's ensemble strategies now include advanced features that improve output quality and give you more control. This guide covers the new capabilities across all seven strategies, with practical examples showing when and how to use them.
Reading time: 8-10 minutes
AI Crucible has added confidence scoring, attack customization, diversity preservation, and bi-directional feedback to its ensemble strategies. These improvements can produce higher-quality outputs by reducing groupthink, surfacing uncertainties, and enabling targeted adversarial testing. Each strategy now includes features designed for its specific use case.
The updates fall into three categories:
The Red Team / Blue Team strategy now lets you select specific attack vectors for adversarial testing. Instead of generic attacks, you choose from seven specialized techniques based on your content type. This produces more relevant vulnerabilities and stronger final outputs.
| Technique | Icon | Best For |
|---|---|---|
| Social Engineering | 🎭 | User-facing content, processes |
| Prompt Injection | 💉 | AI system prompts, instructions |
| Logical Fallacies | 🔄 | Arguments, analyses, recommendations |
| Edge Cases | 📐 | Technical specifications, code |
| Security Exploits | 🔓 | System designs, authentication flows |
| Scalability Stress | 📈 | Architecture, performance-critical systems |
| Assumption Challenges | ❓ | Business plans, strategy documents |
Tests how content could be exploited through manipulation, deception, or trust abuse. The Red Team looks for ways bad actors could trick users or bypass human processes.
Attack examples:
Best for: Customer service scripts, onboarding processes, authentication procedures, user communications, policies that involve human judgment, support workflows.
Tests AI-related content for vulnerabilities where malicious inputs could alter behavior or extract sensitive information. Focuses on how prompts could be manipulated to override intended functionality.
Attack examples:
Best for: AI system prompts, chatbot instructions, content moderation rules, AI agent definitions, automated response templates, LLM-based workflows.
Identifies flawed reasoning, circular logic, false premises, and unsupported conclusions. Examines whether arguments actually support their stated conclusions and checks for common reasoning errors.
Attack examples:
Best for: Business cases, research conclusions, policy recommendations, strategic analyses, persuasive documents, investment theses, decision frameworks.
Tests boundary conditions, extreme values, and unusual but valid inputs that could break or expose weaknesses in the solution. Focuses on what happens at the limits of expected behavior.
Attack examples:
Best for: Technical specifications, API designs, data validation rules, form designs, algorithms, code logic, configuration schemas, input processing.
Identifies potential security vulnerabilities including injection attacks, authentication bypass, privilege escalation, and data exposure risks. Examines how malicious actors could compromise the system.
Attack examples:
Best for: System architectures, API designs, authentication flows, data handling procedures, access control policies, security documentation, infrastructure designs.
Tests how solutions perform under high load, with large datasets, or with many concurrent users. Identifies bottlenecks, resource exhaustion points, and cascade failure modes.
Attack examples:
Best for: System architectures, database designs, API specifications, infrastructure plans, performance-critical algorithms, capacity planning, distributed systems.
Questions implicit assumptions that the solution takes for granted. Examines what happens if those assumptions don't hold in real-world conditions.
Attack examples:
Best for: Business plans, strategy documents, project proposals, feasibility studies, risk assessments, planning documents, roadmaps, growth strategies.
Select attack techniques that match your content type. A security review needs different attacks than a marketing campaign stress test. Focused attacks find more relevant issues than broad approaches.
Example: API Design Review
Select these techniques:
Skip these:
Example: Business Proposal
Select these techniques:
Skip these:
The Red Team prompts automatically focus on your selected attack vectors.
Steelmanning requires each side to accurately summarize their opponent's strongest argument before rebutting. This prevents strawman attacks and ensures genuine engagement with opposing viewpoints. Judges evaluate steelmanning quality alongside argument strength.
Traditional debates often devolve into strawman attacks where sides misrepresent opponents. Steelmanning forces models to demonstrate understanding before criticism. This produces:
Without Steelmanning:
"The opposition claims X, but this is clearly wrong because..."
With Steelmanning:
"The opposition's strongest point is that X leads to Y, which addresses concern Z. However, this overlooks..."
Steelmanning works best for:
Steelmanning is enabled by default. You can disable it for simpler debates where speed matters more than depth.
After judges declare a winner, the winning side must argue the opposite position. This exposes blind spots and confirmation bias in the winning argument. The exercise reveals weaknesses that competitive pressure might have hidden.
The devil's advocate round often exposes:
Example Output:
## Devil's Advocate Round
**Original Position:** Proposition (won the debate arguing FOR remote work policies)
**Devil's Advocate Argument:**
Having argued for remote work, I must now present the strongest case against it:
1. **Collaboration suffers** - Spontaneous innovation happens in hallways
2. **Culture erodes** - New employees never absorb company values
3. **Management overhead** - Coordination costs exceed commute savings
These points reveal our original argument assumed existing team cohesion...
Chain-of-Thought and Collaborative Synthesis now extract confidence levels from model responses. Each reasoning step includes a confidence rating (1-5), and synthesis gives more weight to high-confidence contributions.
| Score | Level | Meaning |
|---|---|---|
| 5 | Certain | Mathematical fact, established principle |
| 4 | Very Confident | Strong logical inference, well-supported |
| 3 | Moderate | Reasonable assumption, likely correct |
| 2 | Uncertain | Multiple interpretations, needs verification |
| 1 | Speculative | Educated guess, limited evidence |
In Collaborative Synthesis, the arbiter model gives more weight to high-confidence responses:
Example:
Model A (85% confidence): "The primary cause is X because of evidence Y and Z."
Model B (45% confidence): "It might be X, but possibly W depending on factors."
Model C (70% confidence): "X is likely, though alternative Y deserves consideration."
Weighted Synthesis: "The primary cause is X (supported by Models A and C with
high confidence). Model B raises the possibility of W, which merits consideration
in edge cases."
Low confidence scores indicate areas needing verification. The system highlights steps with confidence ≤2 for priority review:
⚠️ LOW CONFIDENCE STEPS (Priority Review):
**Model A:**
- Step 3: "Assuming market conditions remain stable..." (Confidence: 2)
- Step 7: "Based on limited data from Q3..." (Confidence: 1)
**Model B:**
- Step 4: "This pattern typically indicates..." (Confidence: 2)
Focus your review on these flagged steps. They represent the weakest links in the reasoning chain.
Competitive Refinement now protects unique approaches from premature convergence. The system detects when responses become too similar and encourages alternative thinking. The final round requires proposing genuinely different approaches.
Without protection, Competitive Refinement can converge to mediocre consensus. Models copy successful patterns, losing innovative ideas that might be superior. Diversity preservation:
The system calculates diversity scores between responses. When similarity exceeds 70%, it triggers anti-groupthink measures:
⚠️ ANTI-GROUPTHINK ALERT:
The responses are converging significantly. Before finalizing, you MUST:
1. Propose at least ONE genuinely different alternative approach
2. Identify assumptions that ALL responses share but haven't examined
3. Challenge the emerging consensus with counter-arguments
Every final round now includes an Alternative Approach section:
## My Best Answer
[Main refined response based on competitive improvement]
## Alternative Approach
Even if confident in my main answer, here's a genuinely different approach:
**Different Method:** Instead of optimization-focused solution, consider a
simplicity-first approach that...
**Unique Advantages:** This approach requires less expertise to maintain,
has fewer failure points, and...
**When to Use:** Choose this approach when team expertise is limited or
when maintenance burden matters more than peak performance.
Implementers can now flag impractical strategic assumptions back to strategists. This creates a feedback loop where ground-level insight improves high-level planning. Quality gates ensure work meets explicit criteria before advancing.
Implementers fill out a structured feedback table:
| Strategic Point | Issue Type | Problem | Suggested Adjustment |
|---|---|---|---|
| "Scale to 10M users in month 1" | impractical | Current infrastructure supports 100K max | Phase rollout: 100K → 1M → 10M over 3 months |
| "Use microservices architecture" | unclear | No guidance on service boundaries | Specify domain boundaries or defer to implementation |
Quality gates define explicit pass/fail criteria between hierarchy levels:
Strategy → Implementation:
Implementation → Review:
If implementers flag significant issues, round 3 becomes a revision round:
This ensures practical concerns get addressed before final review.
The moderator now explicitly identifies missing perspectives and unchallenged assumptions. This surfaces blind spots that no assigned expert covers. The gap analysis appears in moderator summaries.
Each moderator summary now contains:
## Gap Analysis
### Missing Perspectives
What expertise or viewpoints are NOT represented in this panel?
- Legal/regulatory perspective not represented
- End-user voice missing from technical discussion
- Financial impact not addressed by any expert
### Unanswered Questions
What questions has no expert adequately addressed?
- How does this scale beyond initial deployment?
- What happens if the primary vendor fails?
### Unchallenged Assumptions
What assumptions have all experts shared but not examined?
- All experts assumed current market conditions continue
- No one questioned the 6-month timeline feasibility
Gap analysis helps you:
Each improved strategy excels at different tasks. Match your needs to the right approach.
| Task Type | Recommended Strategy | Key Feature to Use |
|---|---|---|
| Security review | Red Team / Blue Team | Custom attack techniques |
| Complex decision | Debate Tournament | Devil's advocate round |
| Multi-discipline analysis | Expert Panel | Gap analysis |
| Technical problem | Chain-of-Thought | Confidence scoring |
| Creative content | Competitive Refinement | Diversity preservation |
| Research synthesis | Collaborative Synthesis | Weighted aggregation |
| Project planning | Hierarchical | Bi-directional feedback |
AI Crucible provides a Strategy Options panel where you can enable or disable enhancement features for each strategy. All options are enabled by default for maximum output quality, but you can disable specific features when you need faster results or simpler outputs.
Look for the ⚙️ Settings button next to the strategy selector dropdown. The button shows a count of enabled options (e.g., "⚙️ 2/2" means 2 of 2 options are enabled).
Click the button to open a dropdown with toggle switches for each available enhancement for the selected strategy.
Your preferences are automatically saved! When you toggle any option:
This means you can set up your preferred configuration once and it will be remembered for future sessions.
Different strategies have different configurable options:
| Strategy | Options | What They Control |
|---|---|---|
| Debate Tournament | Steelmanning, Devil's Advocate | Argument quality requirements, extra round |
| Chain-of-Thought | Step Confidence, Error Categorization | Output verbosity, critique structure |
| Collaborative Synthesis | Weighted Aggregation, Disagreement Highlighting | Synthesis approach, conflict visibility |
| Competitive Refinement | Diversity Preservation, Anti-Groupthink | Innovation encouragement, final round behavior |
| Hierarchical | Bi-Directional Feedback, Quality Gates | Workflow complexity, checkpoint requirements |
Disable options when:
Keep options enabled when:
Toggle the Strategy Options panel with:
⌘ + Shift + OCtrl + Shift + OSpeed-focused configuration:
Disable these options for faster, simpler outputs:
Quality-focused configuration (default):
Keep all options enabled:
Most improvements are enabled by default. Here's how to access the configurable options:
For best results, use at least 3 rounds for strategies with advanced features.