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2 posts tagged with "LLM"

Large Language Models

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Getting Started with AI Governance in a Enterprise

· 5 min read
Dinesh Gopal
Dinesh Gopal
Technology Leader, AI Enthusiast and Practitioner

A Practical Guide Using a RAG Chatbot as a Case Study​

As AI becomes increasingly embedded into enterprise workflows, AI governance is no longer optional — it's essential. The risks of deploying unchecked AI include misinformation, privacy breaches, compliance violations, biased outcomes, and reputational damage.

This post explores how to implement AI governance in a practical and actionable way, using the example of a RAG (Retrieval-Augmented Generation) chatbot built to to assist users by retrieving and summarizing information from a large collection of domain-specific documents, such as policies, procedures, or technical manuals.

🚦 What is AI Governance?​

AI governance is the framework of policies, processes, roles, and tools that ensure AI systems are:

  • Ethical
  • Compliant with regulations
  • Reliable and transparent
  • Aligned with business and user expectations

It encompasses both technical (data security, evaluation, explainability) and organizational (roles, accountability, training) dimensions.

🧠 RAG Chatbot in the Enterprise: The Use Case​

Let’s say your enterprise is deploying a RAG chatbot for internal use. It pulls answers from internal documentation and returns concise responses using an LLM like OpenAI’s GPT or Anthropic’s Claude.

Your goals are:

  • Boost productivity by reducing time spent searching documents
  • Ensure responses are accurate, consistent, and traceable
  • Protect sensitive data from being leaked or mishandled
  • Maintain compliance with internal risk, privacy, and legal policies

This is where governance becomes critical.

Prompt Engineering Techniques

· 4 min read
Dinesh Gopal
Dinesh Gopal
Technology Leader, AI Enthusiast and Practitioner

📘 Introduction​

Prompt engineering is the art and science of crafting inputs that guide Large Language Models (LLMs) to produce desired outputs. While anyone can write a prompt, effective prompt engineering requires an understanding of LLM behavior, configuration tuning, and iterative testing.

Based on Google’s 2024 whitepaper, this guide breaks down strategies, parameters, and real-world examples to help you get the most from any LLM.