 <h1>Congratulations! 🎉 </h1>
    <p style="color:#637381;">All done! You’ve completed another milestone.</p>

To continue, enter your details to log in.

We’ve sent a 6‑digit code to #MAIL_ADDRESS#\nPlease check your inbox or spam folder and enter the code below to continue.

Verification code sent

Thank you for the payment. You can now access {collectionName} by simply clicking the button below.

Yes, you’ve successfully ordered! 🎉

Unlock This Exclusive Course

It seems that your email address is not currently on the authorized access list. Please ensure you are using the email address that was invited, or contact the content owner for assistance.

Verification Required for Access

Foundations of Prompt Content

Sources of Content

Crossword: Terminology Review

Static Content

Clarifying Your Question

Few-Shot Prompting

Dynamic Content Strategies

Dynamic Content

Finding Dynamic Context

Retrieval-Augmented Generation

Implementing Vector Search

# get the embeddings for the reviews

Pixel Quest: RAG Pipeline Challenge

# create the index

# reshape vectors into 2D array and then add to the index

# get the embedding for the query

# reshape vector into 2D array and then search the index

Optimization and Summary

Summarization

Conclusion

Knowledge Check

Quiz 1: Static vs Dynamic Content

Quiz 2: Prompt Engineering Basics

Quiz 3: Few-Shot Prompting

Quiz 4: Dynamic Context Needs

Quiz 5: Search Strategies

Quiz 6: RAG Workflow

Quiz 7: Embeddings Concepts

Quiz 8: Creating Embeddings

Quiz 9: Vector Indexing

Quiz 10: Data Reshaping

Quiz 11: Query Processing

Quiz 12: Searching the Index

Quiz 13: Summarization Techniques

Quiz 14: Managing Token Limits

Quiz 15: Best Practices Review

Course Feedback

Course Relevance Survey

Clarity of Instruction Survey

Pacing Survey

Technical Depth Survey

RAG Concept Utility Survey

Examples Effectiveness Survey

Quiz Difficulty Survey

Game Engagement Survey

Platform Experience Survey

Knowledge Application Survey

Summarization Utility Survey

Vector Search Clarity Survey

Overall Satisfaction Survey

Recommendation Likelihood Survey

Future Topics Survey

Build production-ready, knowledge-aware LLM applications that scale. In this hands-on course, you’ll learn how to combine Retrieval-Augmented Generation (RAG) and summarization to deliver accurate, current, and cost-efficient AI experiences. We start with the foundations of prompt content—when static model knowledge is enough and when you must inject dynamic context. You’ll practice clarifying intent, crafting effective few-shot prompts, and reducing hallucinations through grounding. Then we dive into dynamic content strategies and vector search: generating embeddings, creating and populating indexes, querying with semantic search, and interpreting nearest-neighbor results. You’ll design end-to-end RAG pipelines that connect external data sources to LLMs, and you’ll apply summarization patterns to fit rich evidence into tight context windows without losing critical information. By the end, you’ll be able to choose the right approach for each task, balance freshness with latency and cost, and implement robust guardrails for production. Whether you’re enhancing chatbots, analytics copilots, or documentation assistants, this course gives you the practical blueprint to ship scalable, trustworthy LLM systems.

6. Building Scalable Knowledge-Aware LLM Systems with RAG and Summarization

<div class="simple-box-wrapper">
    <div class="simple-box">
        <div class="column">
            <p>Large Language Models (LLMs) have revolutionized natural language processing, yet their utility in production environments often depends on their ability to access and process information beyond their initial training data. This course explores the critical distinction between static and dynamic content, providing a framework for determining when internal model knowledge suffices and when external data injection is mandatory. By analyzing the impact of data freshness and the risks of hallucinations, participants will learn to engineer prompts that clarify user intent and leverage few-shot prompting to enhance response accuracy. This foundational understanding is essential for any professional seeking to move beyond basic AI interactions toward the development of sophisticated, context-aware applications.</p>

<p>Moving into advanced implementation, the curriculum delves into the architecture of Retrieval-Augmented Generation (RAG) and the technical nuances of vector search. Technical professionals will gain insights into creating text embeddings, managing vector indices, and executing high-speed similarity searches to retrieve relevant context from vast datasets. Furthermore, the course addresses the operational challenges of scalability, specifically focusing on summarization techniques to manage token limits and optimize the efficiency of the context window. Through a structured workflow—spanning from data embedding to final context injection—this program equips developers, data scientists, and researchers with the best practices necessary to build robust, knowledge-aware AI systems that maintain high-quality performance in real-world scenarios.</p>
        </div>
    </div>
</div>
