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Foundations of the LLM Loop

The Anatomy of the Loop

The User’s Problem

Translating Problems to Prompts

Converting the User’s Problem to the Model Domain

So a Funny Thing Happened...

Example: Converting the user’s problem into a homework problem

Chat models versus completion models

🎮 Crossword: LLM Terminology

Now, You Try!

Using the LLM to Complete the Prompt

Advanced Application Design

Transforming Back to User Domain

Zooming In to the Feedforward Pass

Building the Basic Feedforward Pass

Exploring the Complexity of the Loop

Persisting application state

External context

Increasing reasoning depth

Tool usage

Evaluation and Quality Assurance

Evaluating LLM Application Quality

Offline Evaluation

Online Evaluation

Conclusion

🎮 Pixel Quest: Full Loop Review

Quiz

Quiz: Anatomy of the Loop

Quiz: User Problem Definition

Quiz: Domain Translation

Quiz: Chat vs. Completion Models

Quiz: Prompt Engineering Basics

Quiz: Output Transformation

Quiz: Feedforward Pass Architecture

Quiz: Application State

Quiz: External Context and RAG

Quiz: Reasoning Strategies

Quiz: Tool Usage and Agents

Quiz: Offline Evaluation Metrics

Quiz: Online Evaluation Techniques

Quiz: End-to-End Workflow Design

Quiz: Troubleshooting and Optimization

Course Feedback

Survey: Course Pacing

Survey: Content Clarity

Survey: Practical Relevance

Survey: Technical Depth

Survey: Visual Aids

Survey: Quiz Difficulty

Survey: Game Experience

Survey: Learning Platform

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Survey: Prompt Engineering Section

Survey: Evaluation Section

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Designing End-to-End LLM Applications from Problem to Production is a practical, blueprint-style course that teaches you how to turn real user needs into reliable AI features. You’ll learn the full LLM loop—from understanding the user’s problem, translating it into effective prompts, and generating outputs, to transforming those outputs back into delightful, production-ready experiences. Along the way, you’ll master the differences between chat and completion models, design robust feedforward passes, and add essentials like state, external context (RAG), deeper reasoning, and tool use. This course balances theory with hands-on practice: you’ll convert ambiguous requests into structured model tasks, build and test basic loops, and scale them into production-grade systems with evaluation workflows. Whether you’re a developer, product manager, or technical founder, you’ll leave with reusable mental models, proven design patterns, and a clear path to ship high-quality LLM features that align with business goals. If you’ve experimented with prompts but want repeatable outcomes, instrumentation, and confidence in production, this course gives you the tools and frameworks to design, evaluate, and iterate intelligently.

4. Designing End-to-End LLM Applications from Problem to Production

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            <p>The development of Large Language Model (LLM) applications requires a rigorous shift from simple prompt-response interactions to a structured, end-to-end engineering lifecycle. This course explores the "Anatomy of the Loop," a framework designed to bridge the gap between abstract user intent and actionable model tasks. By identifying the primary stages of the LLM application loop, professionals can effectively map data flows between the user domain and the model domain, ensuring that every technical decision is rooted in solving a specific, real-world problem. Understanding the distinction between raw user requests and actionable model tasks is the first step in building resilient AI systems that deliver consistent value.</p>

<p>Moving beyond basic foundations, the curriculum delves into the technical complexities of translating user problems into structured "homework" problems for the model. Participants will examine the nuances of chat versus completion models and the critical role of the feedforward pass in generating high-quality outputs. As applications scale, the integration of advanced design patterns—such as persisting application state, implementing Retrieval-Augmented Generation (RAG), and enabling external tool usage—becomes essential for maintaining reasoning depth and operational reliability. These components allow for the creation of sophisticated agents capable of performing complex, multi-step tasks with high logical consistency.</p>

<p>Transitioning an application from a prototype to a production-ready system necessitates a robust approach to quality assurance. This course provides a comprehensive overview of both offline and online evaluation techniques, distinguishing between deterministic software testing and the probabilistic nature of LLM performance. By mastering these evaluation metrics and real-time monitoring strategies, developers, data scientists, and business analysts can ensure their AI solutions remain accurate, ethical, and aligned with user expectations in dynamic environments. The journey from problem to production concludes with a reflection on the ethical and practical considerations of deploying these powerful systems at scale.</p>
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