# Proposal: Establishing the LLM Evaluation Framework (Foreman Framework)

**Prepared For:** Executive Steering Committee
**Prepared By:** AI Strategy & Research Division
**Date:** October 26, 2023
**Subject:** Formalization of the Continuous Large Language Model Capability Assessment System (Foreman Framework)

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## 1. Executive Summary: The Need for Foresight

The rapid maturation and diversification of Large Language Models (LLMs)--spanning GPT variants, Claude, open-source alternatives, and domain-specific foundation models--have created a significant technological opportunity coupled with an unprecedented risk of assumption failure. Current internal testing protocols are episodic, reactive, and lack the systematic rigor required to predict, benchmark, or reliably predict model drift or emergent failure modes across complex, multi-step enterprise workflows.

**This proposal establishes the Foreman Framework:** a continuous, systematic, and scientifically rigorous evaluation platform designed to move our LLM adoption lifecycle from *curiosity-driven experimentation* to *risk-managed, predictable capability deployment*. The framework will function as our central truth source for model performance, ensuring that every implemented AI feature is backed by quantifiable, comparative performance data.

**Key Deliverables:**
1. **Standardized Benchmarking Suite:** A library of domain-aware, failure-case test sets.
2. **Comparative Scoring Model:** A normalized, multi-axis metric system to compare disparate models objectively.
3. **Drift Detection Pipeline:** Automated monitoring to alert on degradation in performance over time.
4. **Recommendation Engine:** Outputting "Go/No-Go" recommendations for production readiness.

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## 2. The Problem: Limitations of Current Assessment

Our current assessment methodology suffers from three critical limitations:

| Limitation | Description | Business Impact / Risk |
| :--- | :--- | :--- |
| **1. Anecdotal Testing** | Reliance on subjective user feedback and limited scope tests rather than holistic failure space exploration. | Risk of "Shiny Object Syndrome"--over-investment in models that fail under real-world edge cases. |
| **2. Lack of Comparability** | Testing different models against different evaluation prompts, resulting in incomparable, siloed results. | Inability to definitively prove ROI or select the optimal model when budget cycles demand accountability. |
| **3. Ephemeral Evaluation** | Tests are conducted and filed away. There is no mechanism for re-testing the same model against newly discovered vulnerabilities or shifts in the model provider's underlying model. | Critical operational risk of **Model Drift**, leading to silent, unnoticeable degradation of accuracy in production systems. |

***

## 3. The Solution: The Foreman Framework Architecture

The Foreman Framework addresses the gaps above by operationalizing Model Evaluation as a core, measurable engineering discipline.

### 3.1. Core Components

1. **Curated Benchmarking Corpus (The Knowledge):**
    * **Structure:** Not merely question-answer pairs, but **Workflow Chains** (Input $A \to Model \to Contextual Output $B \to Downstream Action).
    * **Content:** Must cover **High-Value Domains** (Legal interpretation, financial risk assessment, complex compliance checks) and **Failure Domains** (Ambiguity handling, adversarial input detection, cultural nuance).
    * **Source:** Combination of historical production data (anonymized) and expert-curated adverse scenarios.

2. **The Comparative Scoring Engine (The Measurement):**
    * **Normalization:** Output results (e.g., relevance, hallucination rate, latency) are normalized against a multi-axis matrix.
    * **Weighting:** The matrix allows business units to assign measurable weights. *Example: For Legal Summarization, "Factual Accuracy" is weighted 60%; "Tone Consistency" is weighted 20%.*
    * **Output:** A **Model Capability Scorecard (MCS)**, providing an immediate, digestible ranking against baseline and peers.

3. **The Continuous Monitoring Loop (The Foresight):**
    * **Scheduled Retesting:** Automatically queues the top 3 performing models against the entire corpus on a defined cadence (e.g., bi-weekly).
    * **Drift Detection:** Statistical analysis monitors performance delta. If the average MCS score for a Model A drops by $2\sigma$ (two standard deviations) against the historical mean, the pipeline triggers a **Level 1 Alert**, pausing its operational deployment eligibility.

### 3.2. Operational Flow Diagram

*(Conceptual Diagram to be added upon approval, depicting data flow: Knowledge Corpus $\to$ Runner $\to$ Scoring Engine $\to$ MCS $\to$ Dashboard $\to$ Triage Action)*

***

## 4. Implementation Roadmap & Investment Needs

**Phasing Strategy:** We recommend a three-phase rollout over 9 months to minimize immediate disruption while maximizing learning velocity.

| Phase | Timeline | Focus Area | Key Milestones & Deliverables | Required Resources |
| :--- | :--- | :--- | :--- | :--- |
| **Phase 1: Foundation (Months 1-3)** | Immediate | Building the Benchmark Corpus. | Finalize 3 core industry test sets (e.g., Finance, HR, Compliance). Build the MCS skeletal structure. | Core Data Science Team; 1 FTE Subject Matter Expert (SME) allocation. |
| **Phase 2: Engine Build (Months 4-6)** | Mid-Term | Establishing comparative scoring and automated pipelines. | Implement automated scoring integration for 3 model types. Roll out internal Alpha Dashboard. | DevOps/MLOps Engineer; Cloud Compute Budget Allocation. |
| **Phase 3: Operationalization (Months 7-9)** | Full Integration | Continuous monitoring and corporate adoption. | Full Foreman Dashboard launch. Formalized "Go/No-Go" recommendation gates integrated into the CI/CD pipeline. | Select Business Unit Champions (Adoption); Governance Committee Oversight. |

### Required Investment Areas:

1. **Manpower:** Dedicated time commitment from 1-2 Senior Data Scientists to architect the framework and test sets.
2. **Computation:** Increased, governed cloud compute budget to handle the high volume of repetitive, comprehensive inference calls required for robust testing (This cost is significantly lower than the cost of a single critical failure).

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## 5. Conclusion and Recommendation

The Foreman Framework is not an IT project; **it is a foundational risk management layer for our core AI utility**. Continuing to treat LLM capability assessment as an ad-hoc activity exposes the firm to unacceptable, quantifiable risks related to failure, drift, and poor resource allocation.

**We formally request approval to allocate resources to initiate Phase 1 (Foundation) immediately.** This proactive investment will ensure that as our use of generative AI expands, our reliance is always built upon a foundation of proven, quantifiable performance metrics, granting us a decisive competitive advantage in reliability.