3 posts tagged with "Chatbot"

Over the past two years, I’ve been working on AI applications 🤖, guiding organizations to build AI governance frameworks, responsible AI policies, and deploying production-ready systems.

From this experience, I can confidently say: figuring out the technical part is fun 🎉 and often the easier part. The bigger challenge—and where most time is spent—is building responsible AI practices and governance frameworks that scale across the enterprise.

In my previous post, I discussed how to approach AI governance and frameworks at the enterprise level. In this post, let’s go through a quick 101 on designing AI application architectures responsibly.

📖 Reference: OWASP Top 10 for LLM Applications

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🏗️ Why Architecture Matters in AI Applications​

The AI landscape changes daily ⚡, making it difficult to lock down a future-proof architecture. A good starting point is defining:

• 🎯 The objective of the AI application
• 🖥️ The platform on which it will be built

These early decisions shape the system design and architecture.

For this discussion, let’s use an example: a domain-specific chatbot 💬 that uses customer data and a foundational model to generate responses. To make it more complex, we’ll add tool calling 🛠️ and agents 🕹️ for real-time, domain-specific functions.

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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.

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🚦 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.

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🧠 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.

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📘 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.

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