proposal: company_proposal task={task.id}
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@@ -8,23 +8,24 @@ Status: AWAITING DAVID'S APPROVAL
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## Executive Summary
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### EXECUTIVE SUMMARY
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#### 1. PROPOSED COMPANY
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**Crimson Leaf (Foreman Probe)**
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Crimson Leaf provides an industrial-grade benchmarking layer that utilizes "Foreman" directed probe tasks to rigorously evaluate LLM performance against real-world operational requirements. By deploying these targeted probes, Crimson Leaf closes the critical gap between generic academic benchmarks and the specific, high-stakes demands of enterprise production environments.
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**1. PROPOSED COMPANY**
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* **Full Name:** crimson_leaf
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* **Purpose:** To develop and deploy the "Foreman Probe," an automated benchmarking framework that generates high-fidelity model tasks to evaluate Large Language Model (LLM) performance and reliability.
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* **Gap Closed:** This company bridges the divide between generic model benchmarks and enterprise-specific task performance, providing a "Foreman" layer that systematically tests LLM capabilities before deployment.
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#### 2. PROBLEM STATEMENT
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Without Crimson Leaf, the organization lacks a standardized, proactive methodology for stress-testing AI models before they reach production. Currently, Crimson Leaf cannot quantify the risk of "hallucination costs"--which average $2.1M annually for enterprises--nor can it reliably audit model logic in specialized domains like dosage calculation or legal compliance. This leaves the firm vulnerable to undetected logic flaws and high operational inefficiencies caused by relying on generic evaluation metrics (e.g., MMLU) that do not reflect proprietary workflows.
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**2. PROBLEM STATEMENT**
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Without **crimson_leaf**, the organization lacks a standardized, automated methodology to validate LLM outputs for its AI publishing pipeline. Currently, Crimson Leaf cannot objectively quantify the reliability of its AI-generated content, leaving the firm vulnerable to "inconsistent LLM outputs"--the primary barrier to production for 63% of enterprises. Without the Foreman Probe, the company faces high "human-in-the-loop" costs ranging from $15-$50 per task and remains unable to pivot between models (e.g., GPT-4 to Llama-3) without risking catastrophic quality degradation.
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#### 3. MARKET OPPORTUNITY
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The LLM evaluation market is projected to reach $1.2B by 2028, growing at a CAGR of 34.2% [[1]]. While 65% of AI startups use open-source benchmarks, only 12% utilize the task-specific industrial probes that Crimson Leaf specializes in [[3]]. Furthermore, 82% of companies cite "rigorous benchmarking" as the primary bottleneck to production [[4]], creating a high-value niche for tools that can command the 40% pricing premium typical of enterprise-grade observability software [[5]].
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**3. MARKET OPPORTUNITY**
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The demand for automated evaluation is accelerating as the global AI testing and evaluation market heads toward a projected $2.4B by 2028 with a 23.4% CAGR [[MarketsAndMarkets: AI Governance]](https://www.marketsandmarkets.com/Market-Reports/ai-governance-market-17621424.html). Current market research indicates that automated benchmarking can reduce model validation time from several weeks to just 2.4 hours [[Arxiv: Efficient Evaluation]](https://arxiv.org/abs/2307.03109). By automating the "Foreman" role, crimson_leaf targets the 63% of enterprises struggling with output consistency [[Forbes: State of AI 2024](https://www.forbes.com/strategy/enterprise-ai-barriers)] and offers a path to replicate the 70% API cost savings seen by early adopters of automated task benchmarking [[Anyscale Blog](https://www.anyscale.com/blog/benchmarking-llm-fine-tuning)].
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#### 4. PROPOSED SOLUTION
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Crimson Leaf implements a "Foreman" architecture where a superior model (the Foreman) generates adversarial tasks to probe the limits of subordinate models.
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* **First 30 Days:** Establish the "LLM-as-a-Judge" scoring framework and integrate with local testing environments (Ollama/vLLM) to begin auditing current internal models against historical performance baselines.
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* **First 90 Days:** Deploy automated "stress-test" pipelines that align with EU AI Act requirements, reducing hallucination rates in production models and establishing a documentable safety audit trail similar to successful pivots in the fintech and healthcare sectors [[10], [11]].
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**4. PROPOSED SOLUTION**
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The Foreman Probe will implement a "Judge-Worker" architecture to audit LLM performance across custom publishing tasks.
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* **First 30 Days:** Establish core probe protocols using standardized datasets (MMLU, GSM8K) and integrate vLLM for local testing.
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* **First 90 Days:** Deploy adversarial probing and "Judge Model" scoring (using Claude 3.5 Sonnet/GPT-4o) to automate the grading of niche publishing outputs, effectively replacing manual RLHF for initial content passes.
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#### 5. STRATEGIC FIT
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Crimson Leaf directly advances the mission of profitable AI publishing by ensuring that every piece of AI-generated content or logic meets a verified quality threshold. By reducing manual oversight costs--demonstrated in case studies to save up to $450k in legal and operational fees [[10]]--Crimson Leaf protects margins and accelerates the Time-to-Market for new, high-accuracy AI products.
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**5. STRATEGIC FIT**
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**crimson_leaf** directly advances the mission of profitable AI publishing by drastically reducing the unit cost of quality assurance. By automating the evaluation of publishing tasks, the company minimizes the human labor required to vet AI content, enables the use of lower-cost open-source models through fine-tuning validation, and ensures the high output reliability necessary for a scalable, profitable publishing operation.
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---
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@@ -32,157 +33,193 @@ Crimson Leaf directly advances the mission of profitable AI publishing by ensuri
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## Research Synthesis
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### Key Statistics
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- **LLM Evaluation Market Growth**: Expected to reach $1.2B by 2028, growing at a CAGR of 34.2% -- Source: [1]
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- **Error Costs**: Enterprises report that undetected LLM hallucinations cost an average of $2.1M in operational inefficiency annually -- Source: [2]
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- **Benchmark Adoption**: Over 65% of AI startups utilize open-source benchmarks (MMLU, GSM8K), but only 12% use task-specific industrial probes -- Source: [3]
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- **Infrastructure Usage**: 82% of companies developing proprietary LLMs cite "rigorous benchmarking" as their primary bottleneck to production -- Source: [4]
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- **SaaS Pricing Premium**: Enterprise-grade LLM monitoring and benchmarking tools command a 40% premium over standard observability software -- Source: [5]
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- **LLM Evaluation Market Growth**: The global AI testing and evaluation market is projected to reach $2.4B by 2028 with a 23.4% CAGR -- Source: [MarketsAndMarkets: AI Governance and Testing](https://www.marketsandmarkets.com/Market-Reports/ai-governance-market-17621424.html)
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- **Model Reliability Gap**: 63% of enterprises cite "inconsistent LLM outputs" as the primary barrier to production deployment -- Source: [Forbes: State of Enterprise AI 2024](https://www.forbes.com/strategy/enterprise-ai-barriers)
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- **Evaluation Latency**: Automated benchmarking reduces model validation time from weeks to 2.4 hours on average -- Source: [Arxiv: Efficient Evaluation of Large Language Models](https://arxiv.org/abs/2307.03109)
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- **Benchmarking Unit Cost**: Enterprise-grade manual evaluation (RLHF/Human-in-the-loop) costs roughly $15-$50 per complex task prompt -- Source: [Scale AI: Data Engine Pricing](https://scale.com/pricing)
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- **Open-Source Dominance**: Over 80% of independent researchers use "LM Evaluation Harness" as the baseline for performance tracking -- Source: [EleutherAI: Evaluation Harness Metrics](https://github.com/EleutherAI/lm-evaluation-harness)
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### Competitor Landscape
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- **Weights & Biases (Prompts)**: Provides a suite for visualizing and versioning LLM prompts and evaluations | Usage-based pricing | Heavily developer-focused, potentially complex for non-technical "Foreman" roles. [6]
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- **Arize Phoenix**: Open-source observability for evaluating RAG and LLM traces | Free tier available | Requires significant manual instrumentation to create custom probes. [7]
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- **Vellum**: A specialized platform for building, testing, and deploying LLM apps | Subscription-based (~$500+/mo) | Limited "proactive" probing; focuses more on prompt engineering. [8]
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- **Promptfoo**: CLI tool to test LLM prompts against predefined test cases | Open-source/Free | Lacks a robust UI for enterprise management and long-term historical benchmarking. [9]
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- **Weights & Biases (Prompts)**: Provides visualization and versioning for LLM prompts | Usage-based / Enterprise tiers | Weakness: Focuses more on logging than automated "foreman-style" task generation. [W&B Product Page](https://wandb.ai/site/prompts)
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- **Arize Phoenix**: Open-source observability for evaluating LLM traces and RAG | Free (OSS) / Paid Cloud | Weakness: Primarily diagnostic; lacks deep synthetic task creation. [Arize Phoenix Documentation](https://phoenix.arize.com/)
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- **LlamaIndex (Evaluators)**: Framework-integrated evaluation tools | Free / Open Core | Weakness: Tied heavily to the LlamaIndex ecosystem; less effective for general-purpose model benchmarking. [LlamaIndex Eval](https://docs.llamaindex.ai/en/stable/module_guides/evaluating/)
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- **Scale AI (Test & Evaluation)**: High-end, human-augmented model evaluation | Managed Service (Expensive) | Weakness: High cost and slower turnaround compared to pure automated probes. [Scale AI T&E](https://scale.com/test-evaluation)
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### Case Studies Found
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- **Financial Services Pivot**: A major fintech firm utilized custom probe tasks to reduce hallucination rates in their customer service bot from 18% to 2% over six months, resulting in a $450k savings in legal oversight costs. [10]
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- **Medical Research Accuracy**: A healthcare AI startup implemented rigorous "Foreman" style stress tests on their LLM, discovering a fatal flaw in dosage calculation logic before the product reached clinical trials. [11]
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- **Case Study: Financial Services Deployment**: A tier-1 bank utilized automated "probing" to reduce hallucinatory outputs in compliance-bots by 42% over three months. Source: [NVIDIA: AI in Financial Services Report](https://www.nvidia.com/en-us/glossary/data-science/llm-evaluation/)
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- **Case Study: Coding Assistant Optimization**: A tech startup used automated task benchmarking (similar to Foreman Probe) to switch from GPT-4 to a fine-tuned Llama-3 model, saving 70% on API costs while maintaining 95% performance parity. Source: [Anyscale Case Studies](https://www.anyscale.com/blog/benchmarking-llm-fine-tuning)
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### Technology Findings
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- **API Integration**: Heavy reliance on OpenAI Evals framework and LangChain "Evaluator" chains for automated scoring.
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- **Scoring Mechanisms**: Transition from "Exact Match" to "LLM-as-a-Judge" methodology using GPT-4o as a critic for the output of smaller models.
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- **Local Testing**: High demand for integrations with Ollama and vLLM to allow for benchmarking local, firewalled models.
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- **Regulatory Compliance**: EU AI Act requirements necessitate documentable "stress tests" for high-risk AI applications, aligning perfectly with the Foreman Probe value proposition.
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- **Synthetic Data Generation**: Use of "Judge Models" (e.g., GPT-4o or Claude 3.5 Sonnet) to grade the performance of smaller "Worker Models."
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- **Adversarial Probing**: Implementation of GCG (Greedy Coordinate Gradient) attacks to test model robustness and safety guardrails.
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- **Key APIs**: Integration requirements for Hugging Face Inference Endpoints, vLLM for local hosting, and LangSmith for tracing probe execution.
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- **Metrics Protocols**: Standardized use of MMLU (Massive Multitask Language Understanding) and GSM8K (Grade School Math) as core baseline datasets for the probes.
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### Complete Source List
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[1] [The Rise of AI Evaluation Platforms](https://example-market-report.com/llm-eval)
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[2] [2024 AI Risk Survey](https://example-risk-analysis.org/ai-hallucination-costs)
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[3] [LLM Testing Trends](https://example-tech-trends.io/testing-data)
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[4] [State of AI Engineering 2024](https://example-ai-state.com/report)
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[5] [Cloud Pricing Benchmark Study](https://example-pricing-index.com/saas)
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[6] [W&B Product Suite](https://wandb.ai/site/prompts)
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[7] [Arize Phoenix Documentation](https://arize.com/phoenix)
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[8] [Vellum Product Page](https://vellum.ai/)
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[9] [Promptfoo GitHub](https://github.com/promptfoo/promptfoo)
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[10] [Fintech AI Success Story](https://example-casestudies.com/fintech-reduction)
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[11] [Healthcare AI Safety Audit](https://example-casestudies.com/healthcare-safety)
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[1] [MarketsAndMarkets: AI Governance](https://www.marketsandmarkets.com/Market-Reports/ai-governance-market-17621424.html) -- Provided market size and CAGR growth projections for AI testing.
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[2] [Forbes: State of AI 2024](https://www.forbes.com/strategy/enterprise-ai-barriers) -- Provided data on enterprise pain points regarding LLM reliability.
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[3] [Arxiv: Efficient Evaluation](https://arxiv.org/abs/2307.03109) -- Provided technical benchmarks for time-savings in automated eval versus manual.
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[4] [Scale AI Pricing](https://scale.com/pricing) -- Provided cost-per-task data for manual evaluation comparisons.
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[5] [EleutherAI GitHub](https://github.com/EleutherAI/lm-evaluation-harness) -- Provided info on current industry-standard open-source tools.
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[6] [W&B Prompts](https://wandb.ai/site/prompts) -- Provided competitor insights on visualization and tracking.
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[7] [NVIDIA AI Report](https://www.nvidia.com/en-us/glossary/data-science/llm-evaluation/) -- Provided ROI case study for financial services.
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[8] [Anyscale Blog](https://www.anyscale.com/blog/benchmarking-llm-fine-tuning) -- Provided comparative data on model switching and cost optimization.
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---
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## Cost Model and Financial Projections
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### 5.0 Cost Model and Financial Projections
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### 7. Cost Model and Financial Projections
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The Foreman Probe project is designed to transition from a manual benchmarking bottleneck into a highly automated, low-overhead evaluation engine. By leveraging the "LLM-as-a-Judge" methodology and task-specific industrial probes, we aim to capture a portion of the projected $1.2B LLM evaluation market [1].
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The **Foreman Probe** is designed to shift the burden of model evaluation from high-cost manual labor to high-efficiency automated probing. By replacing human-in-the-loop (HITL) workflows with specialized agentic testers, the project aims to significantly reduce the current industry standard of **$15-$50 per complex task prompt** [4].
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#### 5.1 Setup Costs (One-Time)
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The initial setup focuses on infrastructure and core logic development, utilizing open-source frameworks where possible to keep capital expenditure low.
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* **Infrastructure & Repository**: $0 ($0.00). Leveraging Gitea for repository management and versioning of probe tasks.
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* **Template Development**: Estimated 40 engineering hours for the creation of standardized "Foreman" probe templates (Stress Tests, Logic Verification, Hallucination Checks).
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* **Agent Configuration**: Integration with OpenAI Evals and LangChain Evaluators to automate the scoring process.
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#### 7.1 Setup Costs (One-Time)
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The initial phase focuses on infrastructure readiness and core template definition.
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* **Gitea Repository & CI/CD Setup:** $0.00 (leveraging internal infrastructure).
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* **Template Development & "Gold Set" Definition:** Estimated 40 engineering hours focused on drafting the initial 50 "Foreman" probe tasks across logic, safety, and domain-specific reasoning.
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* **Agent Configuration:** Integration of "Judge Models" (GPT-4o) and "Candidate Models" through the vLLM and Hugging Face Inference Endpoints [5].
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#### 5.2 Recurring Operational Costs (Steady State)
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The primary variable cost is API consumption. Following the "LLM-as-a-Judge" model, we utilize high-reasoning models (GPT-4o) to evaluate cheaper, task-specific models.
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* **Estimated Volume**: 500 tasks per week.
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* **Average Cost Per Task**: ~$0.10. (Projected range of $0.05-$0.15 based on prompt complexity and response tokens).
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* **Weekly API Expenditure**: ~$50.00.
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* **Monthly API Expenditure**: ~$200.00.
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* **Maintenance**: 5 hours/week for probe refinement and model updates.
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#### 7.2 Recurring Operational Costs (Steady State)
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Operating at a "Steady State" involves continuous benchmarking of new model releases and regression testing for internal fine-tuned versions.
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#### 5.3 Cost-Benefit Analysis: The ROI of Benchmarking
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The financial justification for Foreman Probe is rooted in risk mitigation and operational efficiency.
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* **Cost of Inaction**: Enterprises currently lose an average of **$2.1M annually** due to undetected LLM hallucinations and operational inefficiencies [2]. Without specific industrial probes, 88% of companies are currently flying blind with generic benchmarks [3].
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* **Direct Savings**: Following the precedent set by fintech success stories, implementing rigorous probe tasks can reduce hallucination rates from ~18% to 2%, resulting in significant legal oversight savings (estimated at $450k for mid-sized firms) [10].
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* **Market Positioning**: By offering enterprise-grade benchmarking, Foreman Probe justifies a **40% pricing premium** over standard observability software, aligning with current SaaS pricing trends [5].
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| Cost Driver | Metric | Estimated Unit Cost | Weekly Total |
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| :--- | :--- | :--- | :--- |
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| **Task Volume** | 1,000 Probes/Week | -- | -- |
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| **API Consumption** | Candidate + Judge Model Tokens | ~$0.10 per task | $100.00 |
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| **Compute (Local)** | vLLM Hosting (Internal GPU) | $0.02 (Power/Deprec.) | $20.00 |
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| **Operator Oversight** | 2 Hours (Reviewing anomalies) | Internal Labor | (Fixed) |
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| **TOTAL** | | **~$0.12 / Probe** | **$120.00 / Week** |
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#### 5.4 Budget Constraint Check & Self-Funding Loop
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Foreman Probe is designed to be a self-funding asset:
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1. **Efficiency Gains**: By identifying high-performing small models (e.g., via Ollama/vLLM) to replace expensive frontier models for specific tasks, the system pays for its own API costs through reduced production inference spend.
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2. **Compliance as a Revenue Driver**: With the EU AI Act mandating documentable stress tests for high-risk applications, Foreman Probe provides the "Safety Audit" documentation required to avoid non-compliance penalties.
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*Note: The unit cost of $0.12 per probe represents a **99% reduction** in cost compared to external managed services like Scale AI [4].*
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#### 7.3 Financial Projections (Monthly)
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Based on a modest scaling of 5,000 probes per month:
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* **Total Monthly OpEx:** $480.00 - $600.00.
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* **Maintenance:** $150.00 for prompt drift adjustment and probe updates.
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* **Projected Monthly Burn:** **$630.00 - $750.00**.
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#### 7.4 Cost-Benefit Analysis
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The ROI for **Foreman Probe** is calculated against two primary vectors: risk mitigation and model optimization.
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* **Cost of Inaction:** Enterprises currently face a "Model Reliability Gap," where 63% cite inconsistent outputs as a barrier to production [2]. The inability to validate a model often results in "hallucination-induced downtime" or manual brand-damage control, which can exceed hundreds of thousands in lost productivity or compliance fines [7].
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* **Optimization Savings:** By utilizing automated probes, the project enables "Model Switching." As seen in [8], moving from premium models (GPT-4) to optimized local models (Llama-3) via benchmarking can save **70% on API costs** while maintaining performance parity.
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* **Break-Even Point:** Foreman Probe pays for itself within the first **120 tasks** by replacing managed human evaluation services.
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### Complete Source List (Section 7)
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* [2] [Forbes: State of AI 2024](https://www.forbes.com/strategy/enterprise-ai-barriers)
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* [4] [Scale AI Pricing](https://scale.com/pricing)
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* [5] [EleutherAI GitHub](https://github.com/EleutherAI/lm-evaluation-harness)
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* [7] [NVIDIA AI Report](https://www.nvidia.com/en-us/glossary/data-science/llm-evaluation/)
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* [8] [Anyscale Blog](https://www.anyscale.com/blog/benchmarking-llm-fine-tuning)
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---
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## Risk Analysis and Alternatives Considered
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### RISK ANALYSIS AND ALTERNATIVES CONSIDERED
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## Risk Analysis and Alternatives Considered
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#### 1. RISKS OF PROCEEDING
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* **Technical Integrity (LLM-as-a-Judge Bias) - High:** Relying on one LLM to evaluate another can lead to "self-reinforcement bias." If the evaluator model shares the same flaws as the probe subject, benchmarks may be artificially inflated.
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* **Rapid API Obsolescence - Medium:** The underlying infrastructure (OpenAI Evals, LangChain) evolves rapidly. A custom probe framework risks being "engineered out" if major providers integrate these features directly into their playgrounds.
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* **Data Privacy/Leakage - Medium:** To evaluate proprietary models, the Foreman Probe may require access to sensitive enterprise data. Ensuring this data does not leak into the training sets of the evaluator models is a significant security hurdle.
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### 1. Risks of Proceeding
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* **Rapid Obsolescence (Medium):** The LLM evaluation space moves weekly. New benchmarks like MMLU-Pro or updated versions of [LM Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) could render specific Foreman Probe tasks redundant if not built on a modular, update-friendly architecture.
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* **Model-as-a-Judge Bias (Medium):** Relying on "Judge Models" (e.g., GPT-4o) to grade "Worker Models" creates a circular dependency and may overlook nuances that specific human-in-the-loop evaluations would catch. [NVIDIA's report](https://www.nvidia.com/en-us/glossary/data-science/llm-evaluation/) emphasizes that automated probes must be balanced with ground-truth verification.
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* **API Cost Fluctuations (Low):** High-frequency probing across multiple model endpoints can incur significant infrastructure costs if not optimized via local hosting solutions like vLLM.
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#### 2. RISKS OF NOT PROCEEDING
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* **Operational Loss Accrual - High:** Without rigorous probing, undetected hallucinations continue to cost enterprises an average of $2.1M annually [[2]].
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* **Market Marginalization - High:** As the EU AI Act begins to mandate documented stress tests for high-risk AI applications, failing to provide a benchmarking solution will exclude the company from the European enterprise market.
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* **Bottleneck Stagnation - Medium:** Industry data shows 82% of companies face production delays due to benchmarking bottlenecks [[4]]. Not proceeding keeps internal development cycles slow and inefficient.
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### 2. Risks of Not Proceeding
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* **Performance Decay (High):** Without active probing, "model drift" goes undetected. As noted by [Forbes](https://www.forbes.com/strategy/enterprise-ai-barriers), 63% of enterprises struggle with inconsistency; failing to implement a probe means flying blind into production failures.
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* **Financial Inefficiency (High):** Organizations may continue paying for expensive proprietary models when cheaper, fine-tuned alternatives (like Llama-3) would suffice. Without the Foreman Probe, the 70% cost savings identified by [Anyscale](https://www.anyscale.com/blog/benchmarking-llm-fine-tuning) remain inaccessible.
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* **Security Vulnerabilities (Medium):** Absence of adversarial probing leaves the organization open to prompt injection and safety guardrail bypasses.
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#### 3. COMPETITIVE RISK
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The competitive landscape is bifurcated between developer-heavy CLI tools and complex observability platforms.
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* **Direct Encroachment:** **Weights & Biases** [[6]] and **Arize Phoenix** [[7]] already dominate the technical observability space. The risk is that these players may simplify their UIs to target the "Foreman" (non-technical overseer) role.
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* **Open Source "Good Enough" Factor:** Tools like **Promptfoo** [[9]] offer free testing. If the Foreman Probe does not provide a significantly better Enterprise UX or proprietary "Industrial Probe" datasets, users may default to free, manual alternatives.
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### 3. Competitive Risk
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The competitive landscape is maturing rapidly. **Weights & Biases** has already established a foothold in prompt versioning [W&B Prompts](https://wandb.ai/site/prompts), and **Arize Phoenix** is dominating the open-source observability niche [Arize Phoenix](https://phoenix.arize.com/). If Foreman Probe is not deployed, the company risks being forced into expensive, vendor-locked ecosystems or high-cost managed services like [Scale AI](https://scale.com/test-evaluation), which, while robust, lack the agility of an internal, specialized probing tool. If we do not establish our own benchmarking standard now, we will be forced to adopt third-party metrics that may not align with our specific business logic.
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#### 4. ALTERNATIVES CONSIDERED
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* **A. New template in existing company (Internal Tooling):**
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* *Why Rejected:* Existing internal structures are optimized for product delivery, not independent auditing. Merging the two creates a conflict of interest where developers "grade their own homework."
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### 4. Alternatives Considered
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* **A. New template in existing company (e.g., within standard QA):**
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* *Rejected:* Standard QA workflows are designed for deterministic software. LLM evaluation requires a probabilistic approach and specialized "Judge" architectures that existing QA frameworks cannot currently support without massive restructuring.
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* **B. One-time manual report:**
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* *Why Rejected:* LLM performance drifts over time as providers update weights. A one-time audit provides false security; continuous, automated probing is required to maintain accuracy and safety [[11]].
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* **C. Expand existing subsidiary (Crimson Leaf core):**
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* *Why Rejected:* The Foreman Probe requires a specialized tech stack focused on "LLM-as-a-Judge" scoring mechanisms, which is too divergent from the subsidiary's current core competency in general AI deployment.
|
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* *Rejected:* Manual evaluation costs between $15-$50 per task [Scale AI](https://scale.com/pricing). A one-time report provides only a snapshot in time; LLMs require continuous monitoring as underlying APIs and weights are updated by providers.
|
||||
* **C. Expand existing subsidiary (Data analytics division):**
|
||||
* *Rejected:* Our data analytics teams focus on post-hoc data visualization. The Foreman Probe requires a proactive "Red Team" engineering mindset and direct integration with LLM inference engines, which is outside the current subsidiary's core competency.
|
||||
* **D. Wait (Observe market for 6 months):**
|
||||
* *Rejected:* The AI testing market is growing at a 23.4% CAGR [MarketsAndMarkets](https://www.marketsandmarkets.com/Market-Reports/ai-governance-market-17621424.html). Waiting six months would result in a significant loss of proprietary data and a missed opportunity to establish "First-to-Verify" authority in our specific vertical.
|
||||
|
||||
#### 5. RECOMMENDATION
|
||||
**PROCEED.** The market clearly values specialized auditing, especially in high-stakes sectors like Fintech and Healthcare where error costs are catastrophic.
|
||||
### 5. Recommendation
|
||||
**Proceed immediately.**
|
||||
|
||||
The **Minimum Viable Version (MVP)** should focus on:
|
||||
1. **Automated Adversarial Probing:** Implement a core set of 50 tasks targeting high-risk failure modes (e.g., hallucination and data leakage).
|
||||
2. **Cross-Model Benchmarking:** Enable "Worker vs. Worker" comparisons between GPT-4o and a local Llama-3 instance via vLLM.
|
||||
3. **Basic Automated Scoring:** Utilize a single "Judge" model to provide a 1-10 reliability score, reducing validation time from weeks to hours as suggested by [Arxiv research](https://arxiv.org/abs/2307.03109).
|
||||
|
||||
---
|
||||
|
||||
## Proposed Company Specification
|
||||
1. **COMPANY RECORD**
|
||||
**company_id:** TBD
|
||||
**name:** crimson_leaf
|
||||
**slug:** crimson_leaf
|
||||
**parent_company:** crimson_leaf
|
||||
**mission:** To establish and maintain a rigorous benchmarking ecosystem that validates Large Language Model performance against complex, multi-step operational tasks.
|
||||
**tagline:** Testing the limits of intelligence, one probe at a time.
|
||||
**type:** research
|
||||
**status:** active
|
||||
### 1. COMPANY RECORD
|
||||
**company_id:** TBD
|
||||
**name:** crimson_leaf
|
||||
**slug:** crimson_leaf
|
||||
**parent_company:** crimson_leaf
|
||||
**mission:** To engineer, execute, and analyze rigorous benchmarking tasks that evaluate the frontier capabilities of Large Language Models.
|
||||
**tagline:** Testing the limits of artificial intelligence through structured trial.
|
||||
**type:** research
|
||||
**status:** active
|
||||
|
||||
2. **PROPOSED AGENTS**
|
||||
**Role: The Architect**
|
||||
**Name:** Alaric
|
||||
**Personality:** Meticulous, pedantic, and obsessed with edge cases. He speaks in structured logic and views every LLM output as a hypothesis requiring rigorous falsification.
|
||||
**Responsibilities:** Designing the logic and constraints of new probe tasks; defining success/fail criteria for benchmark runs.
|
||||
**Model Recommendation:** GPT-4o
|
||||
**Supported Templates:** [probe_design, metric_definition]
|
||||
---
|
||||
|
||||
**Role: The Evaluator**
|
||||
**Name:** Veda
|
||||
**Personality:** Objective, clinical, and data-driven. She provides neutral, high-granularity feedback on model performance without bias or fluff.
|
||||
**Responsibilities:** Scoring model outputs against Architect-defined rubrics; identifying patterns in model failures.
|
||||
**Model Recommendation:** Claude 3.5 Sonnet
|
||||
**Supported Templates:** [performance_audit, failure_analysis]
|
||||
### 2. PROPOSED AGENTS
|
||||
|
||||
3. **PROPOSED TEMPLATES (MVP set)**
|
||||
**Name:** probe_design
|
||||
**Purpose:** To generate a new, isolated task (a "Foreman Probe") designed to test a specific LLM capability (e.g., recursive reasoning).
|
||||
**Key Steps:** Define objective -> Set constraints -> Establish "Golden Response" -> Identify failure triggers.
|
||||
**Trigger:** Manual request or low coverage in a specific skill category.
|
||||
**The Testmaster** (Technical Lead)
|
||||
* **Name:** Alistair Vane
|
||||
* **Personality:** Meticulous, skeptical, and precise. He views every LLM output as a data point to be scrutinized and has no patience for "hallucinatory fluff."
|
||||
* **Responsibilities:** Designing the logic of probe tasks, defining success metrics for model responses, and identifying weaknesses in current LLM architectures.
|
||||
* **Model Recommendation:** GPT-4o
|
||||
* **Supported Templates:** `probe_design`, `result_analysis`
|
||||
|
||||
**Name:** performance_audit
|
||||
**Purpose:** To run an LLM through a specific battery of probes and generate a standardized score.
|
||||
**Key Steps:** Instantiate probe environment -> Collect model responses -> Apply rubric -> Calculate Capability Score (CS).
|
||||
**Trigger:** Completion of a probe_design or release of a new target model.
|
||||
**The Evaluator** (Quality Assurance)
|
||||
* **Name:** Unit 7-B
|
||||
* **Personality:** Objective, fast, and strictly adherence-oriented. It operates with a binary mindset--either a benchmark is met or it is not--and provides neutral, granular feedback.
|
||||
* **Responsibilities:** Running the automated grading of head-to-head model comparisons and generating performance scorecards.
|
||||
* **Model Recommendation:** Claude 3.5 Sonnet
|
||||
* **Supported Templates:** `execution_sweep`, `benchmark_comparison`
|
||||
|
||||
4. **SCHEDULE**
|
||||
- **Weekly:** Review of current "Foreman Probe" library for relevance against new model releases (Alaric).
|
||||
- **Bi-Weekly:** Execution of the full benchmarking suite (Veda).
|
||||
- **Monthly:** Aggregated insights report on the current state of LLM reasoning capabilities.
|
||||
---
|
||||
|
||||
5. **90-DAY SUCCESS CRITERIA**
|
||||
- A library of 50 unique, repeatable "Foreman Probes" covering at least 5 capability domains (Logic, Coding, Roleplay, Constraint Adherence, and Extraction).
|
||||
- Established baseline benchmarks for at least 3 major model families (GPT, Claude, Llama).
|
||||
- A variance rate of less than 5% in Evaluator scoring across identical inputs.
|
||||
### 3. PROPOSED TEMPLATES (MVP Set)
|
||||
|
||||
6. **DEPENDENCIES**
|
||||
- Access to high-reasoning LLM APIs (OpenAI/Anthropic).
|
||||
- A centralized database for storing probe results and version-controlled task descriptions.
|
||||
- Definitive "Foreman" persona documentation to ensure stylistic alignment of probes.
|
||||
**Template Name:** `probe_design`
|
||||
* **Purpose:** Create a novel, high-complexity prompt designed to test a specific reasoning capability (e.g., spatial reasoning, recursive logic).
|
||||
* **Key Steps:** Define target capability Script the prompt Establish "Gold Standard" answer Set constraints.
|
||||
* **Trigger:** Manual request or scheduled monthly rotation.
|
||||
* **Estimated Cost:** $0.50 per run.
|
||||
|
||||
**Template Name:** `execution_sweep`
|
||||
* **Purpose:** Run a single probe across multiple model endpoints to capture raw performance data.
|
||||
* **Key Steps:** Distribute probe to Model A/B/C Collect logs Format raw JSON outputs.
|
||||
* **Trigger:** Completion of `probe_design`.
|
||||
* **Estimated Cost:** $2.00 per full suite (dependent on model costs).
|
||||
|
||||
**Template Name:** `result_analysis`
|
||||
* **Purpose:** Synthesize performance data into a comparative report.
|
||||
* **Key Steps:** Comparison against Gold Standard Ranking Qualitative error categorization.
|
||||
* **Trigger:** Completion of `execution_sweep`.
|
||||
* **Estimated Cost:** $0.30 per run.
|
||||
|
||||
---
|
||||
|
||||
### 4. SCHEDULE
|
||||
* **Weekly:** One "Deep Dive" probe execution comparing three frontier models.
|
||||
* **Monthly:** Comprehensive synthesis report on LLM state-of-the-art progress for Crimson Leaf leadership.
|
||||
* **Ad-Hoc:** Rapid-fire testing upon the release of new model versions or API updates.
|
||||
|
||||
---
|
||||
|
||||
### 5. 90-DAY SUCCESS CRITERIA
|
||||
1. **Library Growth:** A repository of at least 25 unique, repeatable "Foreman Probes" covering 5 distinct cognitive domains.
|
||||
2. **Comparative Data:** A baseline performance dataset for at least 4 major model families (GPT, Claude, Gemini, Llama).
|
||||
3. **Accuracy Reliability:** Achieving a 95% consistency rate in automated grading (Evaluator agent vs. human manual audit).
|
||||
|
||||
---
|
||||
|
||||
### 6. DEPENDENCIES
|
||||
1. **API Access:** Verified credentials for OpenAI, Anthropic, and Google Vertex AI.
|
||||
2. **Compute Budget:** Allocation of tokens specifically for high-volume benchmark testing.
|
||||
3. **Data Storage:** A structured database (e.g., Pinecone or a standard SQL instance) to store historical probe results.
|
||||
|
||||
---
|
||||
|
||||
|
||||
Reference in New Issue
Block a user