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Reinforcement Learning from Human Feedback (RLHF)

The Need for Alignment in LLMs

The RLHF Process Explained

Alignment Tax and Model Behavior

Instruct Models vs. Chat Models

The Architecture of Instruct Models

The Chat Model Paradigm

Comparing Chat with Completion Architectures

🎮 Crossword: AI Terminology Challenge

Mastering the Chat Completion API

Structure of a Chat Request

Managing Context and History

Tuning API Parameters

Prompt Engineering as Playwriting

The Playwright Metaphor

Defining Personas and Voice

Scripting Multi-Turn Interactions

Moving Beyond Chat to Tools

Limitations of Pure Text Generation

Introduction to Function Calling

The Tool Execution Workflow

🎮 Pixel Quest: The AI Architect's Journey

Quiz Section

Quiz: RLHF Fundamentals

Quiz: Alignment and Safety

Quiz: Instruct vs. Chat Basics

Quiz: Training Differences

Quiz: Legacy vs. Modern APIs

Quiz: API Roles

Quiz: Context Management

Quiz: API Hyperparameters

Quiz: System Prompting

Quiz: Persona Design

Quiz: Few-Shot Prompting

Quiz: Limits of Text Generation

Quiz: Function Calling Logic

Quiz: Tool Integration

Quiz: Final Comprehensive Review

Course Feedback and Survey

Survey: Course Pacing

Survey: RLHF Content Clarity

Survey: Instruct vs. Chat Relevance

Survey: API Technical Depth

Survey: Playwriting Metaphor Utility

Survey: Tool Use Complexity

Survey: Quiz Difficulty

Survey: Game Engagement

Survey: Visual Aids

Survey: Real-world Application

Survey: Platform Experience

Survey: Future Topics

Survey: Audio/Video Quality

Survey: Instructor Effectiveness

Survey: Overall Satisfaction

Mastering Chat-Based AI: RLHF Prompting and Tool Integration is a hands-on course for engineers, product builders, and technical leaders who want to design reliable, safe, and high-performance chat experiences. You’ll learn how Reinforcement Learning from Human Feedback (RLHF) shapes modern language models, why alignment matters, and how it affects behavior such as refusal patterns, creativity, and task focus. Then, you’ll dive into the practical differences between instruct and chat paradigms, exploring how multi-turn context, system messaging, and dialog history change how you prompt. From there, we get tactical with the Chat Completion API—message roles, context windows, and hyperparameters—so you can build chat flows that are both predictable and flexible. Finally, you’ll move beyond text generation by integrating tools via function calling, enabling capabilities like deterministic calculations, real-time data retrieval, and external actions. Along the way, games and quizzes reinforce core concepts, while practical exercises help you migrate legacy prompts, design effective system personas, and implement tool execution loops. By the end, you’ll be able to architect end-to-end conversational systems that are aligned, debuggable, and production-ready.

3. Mastering Chat-Based AI: RLHF Prompting and Tool Integration

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            <p>The landscape of Large Language Models (LLMs) has evolved rapidly from simple next-token prediction engines to sophisticated conversational agents capable of nuanced reasoning and task execution. This transformation is largely driven by Reinforcement Learning from Human Feedback (RLHF), a critical process that aligns raw base models with human values of helpfulness and harmlessness. Understanding the mechanics of RLHF—including Supervised Fine-Tuning, Reward Modeling, and Proximal Policy Optimization—is essential for any professional seeking to comprehend the behavioral constraints and capabilities of modern AI. This course provides a rigorous examination of these alignment processes, exploring the "alignment tax" and the fundamental shifts in statistical output that distinguish a raw model from a production-ready assistant.</p>

<p>Beyond the underlying training paradigms, mastering chat-based AI requires a deep technical proficiency in modern API architectures and interaction design. Participants will analyze the structural differences between Instruct and Chat models, learning to navigate the transition from legacy completion endpoints to structured message lists involving System, User, and Assistant roles. By adopting the metaphor of "Prompt Engineering as Playwriting," the curriculum guides developers and analysts through the creation of complex system personas and multi-turn interaction scripts. Furthermore, the course addresses the inherent limitations of pure text generation by introducing function calling and tool integration. This enables the development of autonomous workflows where AI can interact with external data and execute deterministic tasks, bridging the gap between static conversation and functional utility in a professional environment.</p>
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