I am writing to propose a mission-critical feature to address severe hallucination and performance degradation when processing complex, multi-project prompts with nested constraints.

Problem Statement

Our system currently exhibits fundamental breakdowns when handling prompts containing multiple distinct projects with interdependent constraints and domain-specific requirements:

1. Critical Performance Drop: Benchmarks show accuracy falling below 10% compared to single-task prompts. When processing 32+ nested hierarchical tasks (e.g., "Project Z's Project B's Project D"), performance degrades to GPT-3 equivalence levels.

2. Constraint Isolation Failure: The system cannot reconcile project-specific constraints with global constraints simultaneously. Complex nesting ("Project A's Project B's Project 9") causes complete context loss from parent-level backgrounds and requirements.

3. Format Contamination: Long-format examples (e.g., Q Project's 10,000-character engineering specs) incorrectly propagate to unrelated projects, overriding their designated output formats.

4. Cross-Domain Interference: Performance degrades exponentially when tasks span disparate domains (mathematics, image generation, genetics, history). Similar projects (G/J) get merged despite explicit separation instructions.

5. Execution Planning Deficit: No mechanism exists for task weighting, dependency mapping, or sequential vs. parallel execution determination across multiple projects.

Specific Failure Scenarios

• Multi-tier References: When Project Z's Project B's Project D requires referencing constraints from Project B's Project A background, the system fails to trace and apply hierarchical dependencies.
• Background-Only Tasks: Projects like P (life philosophy) that provide only contextual information contaminate active task execution.
• Unprecedented Generation: Z Project's requirement to generate entirely novel combinations (e.g., merging A's B's 9) triggers hallucination cascades.

Proposed Feature: Hierarchical Multi-Task Constraint Optimization Engine

This architectural enhancement would deliver:

1. Constraint Inheritance & Isolation: Maintain separate constraint namespaces per task/node with explicit inheritance rules from parent levels
2. Format Locking: Enforce project-specific output schemas without cross-contamination, regardless of example length
3. Dynamic Weighting & Dependency Graph: Allow user-defined priorities, execution sequences, and dependency mapping across task trees
4. Domain Siloing: Prevent knowledge transfer between disparate fields (e.g., D Project's history exams vs. Q Project's engineering specs)
5. Reference Resolution Engine: Intelligent handling of inter-project citations without context merging
6. Performance Scaling: Maintain GPT-4-level quality for up to 50+ hierarchical tasks

High-Value Application Domains

• Mathematical/Genetic Optimization: Simultaneous constraint satisfaction across multiple algorithmic problems
• Technical Documentation: Assembling complex specs from modular components while preserving format integrity
• Academic Assessment: Processing multi-domain exam hierarchies (e.g., Project D's fill-in-the-blank history tests) without domain bleeding
• Creative Synthesis: Executing Z Project's "unprecedented generation" requirements through controlled combinatorial logic

Business Impact

• Restore enterprise-grade reliability for complex workflows
• Enable new revenue streams in technical fields (biotech, engineering, finance)
• Reduce API costs from hallucination-induced retry loops
• Establish competitive moat in compound AI task handling

I have detailed benchmark data and failure pattern analysis available for review. I would appreciate the opportunity to discuss this proposal in our next roadmap planning session.

Best regards,