A Practical and Pragmatic Approach for Responsible AI Usage and AI Governance for GenAI for Small and Medium Businesses, Communities and Individuals

Authors: Koustav Bhar

DOI: https://doi.org/10.37082/IJIRMPS.v14.i1.232973

Short DOI: https://doi.org/hbttzb

Country: United States

Full-text Research PDF File:   View   |   Download

Abstract: As GenAI becomes common-place, usage of Artificial Intelligence (AI) is becoming increasingly crucial for small and medium businesses, communities, and individuals. This paper proposes a practical, costefficient, and privacy-conscious framework for evaluating application systems built on Large Language Models (LLMs). Traditional machine learning models are typically assessed using standardized quantitative metrics such as accuracy, precision, recall, F1-score, RMSE, and R², where outputs are structured and easily comparable to labeled ground truth. In contrast, LLMbased systems generate open-ended, context-sensitive responses, making their evaluation significantly more complex. Measuring performance in such systems must go beyond correctness alone and include dimensions such as factual grounding, consistency, bias control, safety behavior, resistance to malicious prompts, and ethical alignment. The growing enterprise adoption of GenAI solutions creates an urgent need for an evaluation methodology that is reliable, explainable, and economically sustainable.
The framework described in this work introduces a structured evaluation approach designed specifically for LLM-powered applications that perform extraction, reasoning, and question answering over documents. Rather than depending on complex and expensive multi-model evaluation architectures — especially those that rely on using another LLM as an automated judge — this method emphasizes controlled testing, deterministic prompt design, and ground-truth-based validation. This avoids the governance and reliability concerns associated with “who evaluates the evaluator,” while also significantly reducing operational costs and architectural complexity.
The methodology begins with cross-domain document preparation, privacy sanitization, and detailed manual analysis to identify verifiable data points. From these, validated ground truth datasets and repeatable prompts are created. Documents are then segmented using an optimized chunking strategy with contextual overlap and embedded using an enterprise-approved embedding model. These embeddings are stored in a vector database to enable semantic retrieval. At runtime, user queries are matched to the most relevant content segments through similarity search, and only a small number of top-ranked chunks are supplied to the LLM along with a strict system prompt. This constrained-context design improves response relevance, reduces hallucinations, and controls token usage and latency.
Evaluation is performed by systematically comparing model outputs with predefined ground truth answers to measure accuracy and detect drift. Additional monitoring layers analyze user interactions and model responses to identify bias indicators, malicious or unethical intent, and appropriate refusal behavior. These checks are implemented through rules, controlled prompts, and scoring logic rather than secondary judging models. All queries, retrieved contexts, prompts, responses, and evaluation results are logged to support traceability, audit readiness, and stakeholderreportingonperformance andresponsibleusage. Overall, the proposed framework demonstrates that robust LLM evaluation can be achieved through a transparent, lightweight, and enterprise-aligned architecture. It balances accuracy, safety, privacy, and cost, while remaining extensible for future enhancements such as newer models, improved embeddings, refined prompts, and multi-layer evaluation strategies. By addressing these critical components, organizations and individuals can navigate the complexities of AI implementation while ensuring ethical and accountable AI practices.

Keywords: LLM Bias and Safety Evaluation, GenAI Evaluation Framework, LLM Accuracy Evaluation, Malicious Intent Detection, Responsible AI Usage Monitoring.

Paper Id: 232973

Published On: 2026-02-05

Published In: Volume 14, Issue 1, January-February 2026

Share this