Foundations of RAG: Context Engineering and Reranking — WalkSelf
4.4 (9) ⏱ 2h 48m 📚 28 lessons

Foundations of RAG: Context Engineering and Reranking

Understand the basics of Retrieval-Augmented Generation, context engineering, and reranking to build accurate AI applications and reduce hallucinations.

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About this course

Large Language Models are powerful, but they often struggle with accuracy and generate false information. Retrieval-Augmented Generation (RAG) solves this by providing relevant context, making AI applications reliable and factual. This text-based course guides you through the core concepts of building effective RAG pipelines. You will start with the fundamental terminology of vector databases and embedding models, then progress to practical techniques for context engineering and reranking. By the end of the course, you will know how to structure context effectively to prevent AI hallucinations and improve response quality. What you will learn: Understand the foundational concepts of Retrieval-Augmented Generation and vector search. Design effective context pipelines to provide accurate data to Large Language Models. Apply reranking techniques to prioritize the most relevant information for your prompts. Practice prompt engineering basics to guide models and reduce hallucinations. Implement modern retrieval patterns to improve the reliability of AI applications. The course flows logically from basic AI terminology and vector database concepts to hands-on written exercises in context structuring. You will read through clear explanations and code snippets that illustrate modern RAG architecture. This course is designed for beginners, aspiring developers, and tech enthusiasts with no prior machine learning experience required. Start reading today to build a strong foundation in modern AI development and context engineering.

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  • Short & focused
    2h 48m of practical content

Reviews (9)

Ximena Orozco CR
★ 5 · July 17, 2026

Llevaba meses leyendo artículos sueltos sobre RAG sin lograr conectar los conceptos, y este curso por fin los ordenó todos en mi cabeza. Empiezan explicando qué es Retrieval-Augmented Generation desde cero, para luego entrar en context engineering, es decir, cómo decidir qué fragmentos de información realmente le sirven al modelo y cuáles solo añaden ruido. La parte sobre reranking me pareció la más valiosa, porque muestra con ejemplos claros cómo reordenar los resultados de una búsqueda antes de pasarlos al modelo mejora muchísimo la calidad de las respuestas. El instructor explica todo con calma, sin dar cosas por sentado, y cada concepto viene acompañado de un ejemplo práctico que se puede reproducir. Terminé el curso con un pipeline de RAG funcional y, más importante, entendiendo por qué cada pieza está ahí.

Clara Laurent FR Verified learner
★ 4 · July 3, 2026

Ce cours couvre bien les bases du RAG, en particulier la partie sur le context engineering où l'on apprend à choisir intelligemment quels morceaux de texte envoyer au modèle plutôt que de tout lui donner en vrac. La section sur le reranking est celle qui m'a le plus servi, avec des exemples concrets de comment reclasser les résultats de recherche pour améliorer la pertinence des réponses générées. Le rythme est agréable et progressif, on ne se sent jamais perdu même sans grande expérience préalable en recherche d'information. Mon seul regret, c'est que certains exercices pratiques auraient mérité un peu plus de temps d'explication avant qu'on nous laisse les faire seul. Globalement une bonne base solide pour continuer à approfondir le sujet par soi-même.

Henrique Santos BR Verified learner
★ 5 · June 24, 2026

Sempre tive dificuldade em entender reranking de verdade, e esse curso finalmente deixou claro por que reordenar os resultados antes de enviar ao modelo faz tanta diferença na qualidade das respostas. A explicação sobre context engineering também é muito boa, mostrando como escolher os trechos certos em vez de simplesmente jogar tudo no prompt. Recomendo bastante para quem está começando a construir sistemas de RAG do zero.

Mariana Almeida PT Verified learner
★ 4 · June 20, 2026

A parte de reranking ajudou bastante a reduzir respostas inventadas; queria mais exemplos, mas valeu muito.

Maximilian Schmidt DE Verified learner
★ 4 · June 12, 2026

Solide Einführung in RAG und Context Engineering, auch wenn der Abschnitt zum Reranking gerne etwas ausführlicher hätte sein können.

Fernando Ferreira BR Verified learner
★ 4 · June 12, 2026

Gostei bastante da forma como o curso apresenta os fundamentos de RAG antes de entrar em context engineering e reranking, sem pular etapas. As explicações sobre como escolher os chunks certos para o contexto são bem práticas e fáceis de acompanhar. Só achei que a parte de reranking poderia ter mais exemplos comparando diferentes abordagens.

Leonardo De Luca IT Verified learner
★ 4 · June 4, 2026

Ottima introduzione ai concetti base del RAG, spiegati con un linguaggio semplice anche per chi non ha esperienza precedente. La parte su context engineering mi ha aperto gli occhi su quanto conti selezionare bene i chunk prima di mandarli al modello. L'unico punto debole è che il reranking viene trattato un po' velocemente rispetto al resto del corso.

Carlos Aguilar PE Verified learner
★ 5 · May 29, 2026

Explicación de RAG muy clara y práctica.

নাসরিন সুলতানা BD Verified learner
★ 5 · May 27, 2026

রির্যাঙ্কিং আর কনটেক্সট ইঞ্জিনিয়ারিং কীভাবে হ্যালুসিনেশন কমায়, সেটা এই কোর্সে দারুণভাবে বুঝলাম। উদাহরণগুলো হাতেকলমে করা যায়, তাই RAG নিয়ে আমার ভিত্তিটা এখন অনেক শক্ত।

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