chore: sync content to repo (#9580)

Co-authored-by: kamranahmedse <4921183+kamranahmedse@users.noreply.github.com>

src/data/roadmaps/backend/content/ai-assisted-coding@fA3yi9puMbTFmbPpo6OjN.md

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+# AI-Assisted Coding
+
+AI-assisted coding involves using artificial intelligence tools to help developers write code more efficiently and effectively. These tools can provide real-time suggestions, automate repetitive tasks, identify potential errors, and even generate code snippets based on natural language descriptions, ultimately speeding up development and improving code quality.
+
+Visit the following resources to learn more:
+
+- [@article@How to Become an Expert in AI-Assisted Coding – A Handbook for Developers](https://www.freecodecamp.org/news/how-to-become-an-expert-in-ai-assisted-coding-a-handbook-for-developers/)
+- [@article@Best Practices I Learned for AI Assisted Coding](https://statistician-in-stilettos.medium.com/best-practices-i-learned-for-ai-assisted-coding-70ff7359d403)
+- [@book@AI-Assisted Programming](https://www.hkdca.com/wp-content/uploads/2025/06/ai-assisted-programming-oreilly.pdf)
+- [@video@How to elevate software development with AI-assisted coding](https://www.youtube.com/watch?v=S2GqQ4gJAH0)
+- [@video@Everything You Need to Know About Coding with AI // NOT vibe coding](https://www.youtube.com/watch?v=5fhcklZe-qE)
+- [@video@What Is Vibe Coding? Building Software with Agentic AI](https://www.youtube.com/watch?v=Y68FF_nUSWE)

src/data/roadmaps/backend/content/ai-vs-traditional-techniques@IZKl6PxbvgNkryAkdy3-p.md

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+# AI vs. Traditional Software Development
+
+Traditional software development relies on developers explicitly writing code to instruct computers on how to perform tasks. This involves defining every step and logic manually. AI-assisted coding, however, leverages machine learning models trained on vast amounts of code to automate parts of the development process. Instead of writing all the code from scratch, developers can use AI to generate code snippets, suggest improvements, and even debug errors, potentially leading to faster development cycles and reduced human error.
+
+Visit the following resources to learn more:
+
+- [@article@What is vibe coding?](https://www.ibm.com/think/topics/vibe-coding)
+- [@article@Unlocking the value of AI in software development](https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/unlocking-the-value-of-ai-in-software-development)
+- [@article@How AI is transforming work at Anthropic](https://www.anthropic.com/research/how-ai-is-transforming-work-at-anthropic)
+- [@video@AI Systems vs Traditional Coding](https://www.youtube.com/watch?v=P7lryCIvxgA)
+- [@video@Vibe Coding Fundamentals In 33 minutes](https://www.youtube.com/watch?v=iLCDSY2XX7E)
+- [@video@What is Vibe Coding?](https://www.youtube.com/watch?v=5OWurmg41tI)

src/data/roadmaps/backend/content/applications@Nx7mjvYgqLpmJ0_iSx5of.md

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+# AI Applications in Software Development
+
+AI is increasingly utilized to enhance various software development tasks, like automatically creating code snippets based on specifications. It also improves existing code by suggesting better ways to rewrite it and by generating helpful documentation from the code itself. These AI tools promise to increase efficiency and reduce errors.
+
+Visit the following resources to learn more:
+
+- [@article@AI in software development](https://www.ibm.com/think/topics/ai-in-software-development)
+- [@article@AI in software development: How to use it, benefits, and key trends](https://appfire.com/resources/blog/ai-in-software-development)
+- [@article@AI-Assisted Software Development: A Comprehensive Guide with Practical Prompts (Part 1/3)](https://aalapdavjekar.medium.com/ai-assisted-software-development-a-comprehensive-guide-with-practical-prompts-part-1-3-989a529908e0)

src/data/roadmaps/backend/content/code-reviews@0TMdly8yiqnNR8sx36iqc.md

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+# AI-Powered Code Reviews
+
+AI-powered code reviews leverage machine learning models to analyze source code and identify potential issues, such as bugs, security vulnerabilities, code style violations, and performance bottlenecks. These tools can automate parts of the code review process, freeing up developers to focus on more complex problems and improving the overall quality and maintainability of the codebase.
+
+Visit the following resources to learn more:
+
+- [@article@AI Code Reviews](https://github.com/resources/articles/ai-code-reviews)
+- [@article@AI Code Review: How to Make it Work for You](https://www.startearly.ai/post/ai-code-review-how-to-make-it-work-for-you)
+- [@video@You've Been Using AI the Hard Way (Use This Instead)](https://www.youtube.com/watch?v=MsQACpcuTkU)
+- [@video@Automatic code reviews with OpenAI Codex](https://www.youtube.com/watch?v=HwbSWVg5Ln4&t=83s)

src/data/roadmaps/backend/content/documentation-generation@q7NpwqQXUp4wt2to-yFiP.md

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+# Documentation Generation with AI
+
+AI-powered documentation generation leverages machine learning models to automatically create and maintain software documentation. These tools can analyze code, comments, and other project artifacts to produce API references, tutorials, and other types of documentation, reducing the manual effort required and ensuring accuracy and consistency. This helps developers by freeing them from manual tasks and ensuring that their APIs are well documented.
+
+Visit the following resources to learn more:
+
+- [@article@AI code documentation: Benefits and top tips](https://www.ibm.com/think/insights/ai-code-documentation-benefits-top-tips)
+- [@article@AI Code Documentation Generators: A Guide](https://overcast.blog/ai-code-documentation-generators-a-guide-b6cd72cd0ec4)
+- [@video@How I Built a Tool to Auto-Generate GitHub Documentation with LLMs](https://www.youtube.com/watch?v=QYchuz6nBR8)
+- [@video@How to Generate API Documentation Using AI](https://www.youtube.com/watch?v=1529XqH50Xs)

src/data/roadmaps/backend/content/embeddings@Ofv__kXuBk-Vv2jVhaFss.md

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+# Embeddings
+
+Embeddings are dense, continuous vector representations of data, such as words, sentences, or images, in a lower-dimensional space. They capture the semantic relationships and patterns in the data, where similar items are placed closer together in the vector space. In machine learning, embeddings are used to convert complex data into numerical form that models can process more easily. For example, word embeddings represent words based on their meanings and contexts, allowing models to understand relationships like synonyms or analogies. Embeddings are widely used in tasks like natural language processing, recommendation systems, and image recognition to improve model performance and efficiency.
+
+Visit the following resources to learn more:
+
+- [@article@What are Embeddings in Machine Learning?](https://www.cloudflare.com/en-gb/learning/ai/what-are-embeddings/)
+- [@article@What is Embedding?](https://www.ibm.com/topics/embedding)
+- [@article@Getting Started With Embeddings](https://huggingface.co/blog/getting-started-with-embeddings)
+- [@article@A Guide on Word Embeddings in NLP](https://www.turing.com/kb/guide-on-word-embeddings-in-nlp)
+- [@video@What are Word Embeddings?](https://www.youtube.com/watch?v=wgfSDrqYMJ4)

src/data/roadmaps/backend/content/how-llms-work@tUNP5VRKvrPcufPDPPEo8.md

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+# How LLMs Work
+
+LLMs, or Large Language Models, are advanced AI models trained on vast datasets to understand and generate human-like text. They can perform a wide range of natural language processing tasks, such as text generation, translation, summarization, and question answering. LLMs function as sophisticated prediction engines that process text sequentially, predicting the next token based on relationships between previous tokens and patterns from training data. They don't predict single tokens directly but generate probability distributions over possible next tokens, which are then sampled using parameters like temperature and top-K. The model repeatedly adds predicted tokens to the sequence, building responses iteratively. This token-by-token prediction process, combined with massive training datasets, enables LLMs to generate coherent, contextually relevant text across diverse applications and domains.
+
+Visit the following resources to learn more:
+
+- [@roadmap@Visit the Dedicated AI Engineer Roadmap](https://roadmap.sh/ai-engineer)
+- [@article@What is a large language model (LLM)?](https://www.cloudflare.com/en-gb/learning/ai/what-is-large-language-model/)
+- [@article@Understanding AI](https://leerob.com/ai)
+- [@article@New to LLMs? Start Here](https://towardsdatascience.com/new-to-llms-start-here/)
+- [@video@How Large Language Models Work](https://www.youtube.com/watch?v=5sLYAQS9sWQ)
+- [@video@Large Language Models Made Easy (LLMs)](https://www.youtube.com/watch?v=osKyvYJ3PRM)

src/data/roadmaps/backend/content/learn-the-basics@UTupdqjOyLh7-56_0SXJ8.md

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+# AI in Backend Development
+
+(Generative) AI can help backend developers automate tasks and write code more efficiently. It uses machine learning models to generate things like code snippets, documentation, and even test cases. By providing these tools with relevant information about your backend system, you can speed up the development process and reduce errors.

src/data/roadmaps/backend/content/prompting-techniques@xL8d-uHMpJKwUvT8z-Jia.md

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+# Prompt Engineering
+
+Prompt engineering is the process of crafting effective inputs (prompts) to guide AI models to generate desired outputs. It involves strategically designing prompts to optimize the model’s performance by providing clear instructions, context, and examples. Effective prompt engineering can improve the quality, relevance, and accuracy of responses, making it essential for applications like chatbots, content generation, and automated support. By refining prompts, developers can better control the model’s behavior, reduce ambiguity, and achieve more consistent results, enhancing the overall effectiveness of AI-driven systems.
+
+Visit the following resources to learn more:
+
+- [@roadmap@Visit Dedicated Prompt Engineering Roadmap](https://roadmap.sh/prompt-engineering)
+- [@article@What is Prompt Engineering? A Detailed Guide For 2026](https://www.datacamp.com/blog/what-is-prompt-engineering-the-future-of-ai-communication)
+- [@article@Prompting Techniques](https://www.promptingguide.ai/techniques)
+- [@article@Prompt engineering techniques](https://www.ibm.com/think/topics/prompt-engineering-techniques)
+- [@course@Introduction to Prompt Engineering](https://learnprompting.org/courses/intro-to-prompt-engineering)
+- [@video@What is Prompt Engineering?](https://www.youtube.com/watch?v=nf1e-55KKbg)

src/data/roadmaps/backend/content/rags@6PCOCzCbx7jo9wO2Hl2gy.md

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+# RAGs
+
+Retrieval-Augmented Generation (RAG) is an AI approach that combines information retrieval with language generation to create more accurate, contextually relevant outputs. It works by first retrieving relevant data from a knowledge base or external source, then using a language model to generate a response based on that information. This method enhances the accuracy of generative models by grounding their outputs in real-world data, making RAG ideal for tasks like question answering, summarization, and chatbots that require reliable, up-to-date information.
+
+Visit the following resources to learn more:
+
+- [@article@What is Retrieval Augmented Generation (RAG)? - Datacamp](https://www.datacamp.com/blog/what-is-retrieval-augmented-generation-rag)
+- [@article@What is Retrieval-Augmented Generation? - Google](https://cloud.google.com/use-cases/retrieval-augmented-generation)
+- [@article@The Ultimate Guide to RAGs – Each Component Dissected](https://towardsdatascience.com/the-ultimate-guide-to-rags-each-component-dissected-3cd51c4c0212/)
+- [@video@What is Retrieval-Augmented Generation? - IBM](https://www.youtube.com/watch?v=T-D1OfcDW1M)

src/data/roadmaps/backend/content/refactoring@EYT2rTLZ8tUW2u8DOnAWF.md

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+# Refactoring with AI
+
+Refactoring, in the context of software development, is the process of restructuring existing computer code—changing its internal structure—without changing its external behavior. AI tools can assist in this process by analyzing code for potential improvements in readability, performance, and maintainability. They can automatically suggest or even implement changes like simplifying complex logic, removing redundant code, and improving code style consistency, ultimately leading to a cleaner and more efficient codebase.
+
+Visit the following resources to learn more:
+
+- [@article@What is AI code refactoring? - IBM](https://www.ibm.com/think/topics/ai-code-refactoring)
+- [@article@What is AI code refactoring?](https://graphite.com/guides/what-is-ai-code-refactoring)
+- [@video@Using AI to Refactor Legacy Code: A Practical Guide with Scott Wierschem](https://www.youtube.com/watch?v=B7Yt-WmlW2I)
+- [@video@AI-Driven Code Refactoring: Improving Legacy Codebases Automatically - Jorrik Klijnsma](https://www.youtube.com/watch?v=u8tvVxUOwvY)

src/data/roadmaps/backend/content/vectors@yKNdBbahm_h81xdMDT-qx.md

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+# Vectors
+
+Vectors are mathematical objects that have both magnitude (length) and direction. They are often represented as ordered lists of numbers, called components. In computer science, and particularly within AI and machine learning, vectors are used to represent data points in a multi-dimensional space, allowing for calculations of similarity, distance, and direction between data points.
+
+Visit the following resources to learn more:
+
+- [@article@What is vector embedding?](https://www.ibm.com/think/topics/vector-embedding)
+- [@article@What are vectors and how do they apply to machine learning?](https://www.algolia.com/blog/ai/what-are-vectors-and-how-do-they-apply-to-machine-learning)
+- [@article@A Gentle Introduction to Vectors for Machine Learning](https://machinelearningmastery.com/gentle-introduction-vectors-machine-learning/)
+- [@article@Vector Databases](https://developers.cloudflare.com/vectorize/reference/what-is-a-vector-database/)
+- [@video@AI Foundations - What are Vectors?](https://www.youtube.com/watch?v=dvDmXTKFtgQ)