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@@ -6,5 +6,5 @@ Visit the following resources to learn more:
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- [@article@Top 15 Use Cases Of AI Agents In Business](https://www.ampcome.com/post/15-use-cases-of-ai-agents-in-business)
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- [@article@A Brief Guide on AI Agents: Benefits and Use Cases](https://www.codica.com/blog/brief-guide-on-ai-agents/)
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- [@video@The Complete Guide to Building AI Agents for Beginners](https://youtu.be/MOyl58VF2ak?si=-QjRD_5y3iViprJX)
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- [@article@How to Build Effective AI Agents to Process Millions of Requests](https://towardsdatascience.com/how-to-build-effective-ai-agents-to-process-millions-of-requests/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration)
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- [@article@How to Build Effective AI Agents to Process Millions of Requests](https://towardsdatascience.com/how-to-build-effective-ai-agents-to-process-millions-of-requests/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration)
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- [@video@The Complete Guide to Building AI Agents for Beginners](https://youtu.be/MOyl58VF2ak?si=-QjRD_5y3iViprJX)
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@@ -1,6 +1,6 @@
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# AI vs AGI
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AI (Artificial Intelligence) refers to systems designed to perform specific tasks by mimicking aspects of human intelligence, such as pattern recognition, decision-making, and language processing. These systems, known as "narrow AI," are highly specialized, excelling in defined areas like image classification or recommendation algorithms but lacking broader cognitive abilities. In contrast, AGI (Artificial General Intelligence) represents a theoretical form of intelligence that possesses the ability to understand, learn, and apply knowledge across a wide range of tasks at a human-like level. AGI would have the capacity for abstract thinking, reasoning, and adaptability similar to human cognitive abilities, making it far more versatile than today’s AI systems. While current AI technology is powerful, AGI remains a distant goal and presents complex challenges in safety, ethics, and technical feasibility.
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AI (Artificial Intelligence) refers to systems designed to perform specific tasks by mimicking aspects of human intelligence, such as pattern recognition, decision-making, and language processing. These systems, known as "narrow AI," are highly specialized, excelling in specific areas such as image classification or recommender algorithms but lacking broader cognitive abilities. In contrast, AGI (Artificial General Intelligence) is a theoretical form of intelligence that can understand, learn, and apply knowledge across a wide range of tasks at a human-like level. AGI would have the capacity for abstract thinking, reasoning, and adaptability similar to human cognitive abilities, making it far more versatile than today’s AI systems. While current AI technology is powerful, AGI remains a distant goal and presents complex challenges in safety, ethics, and technical feasibility.
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Visit the following resources to learn more:
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@@ -7,5 +7,4 @@ Visit the following resources to learn more:
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- [@official@Claude](https://claude.ai)
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- [@course@Claude 101](https://anthropic.skilljar.com/claude-101)
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- [@video@How To Use Claude Pro For Beginners](https://www.youtube.com/watch?v=J3X_JWQkvo8)
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- [@article@How To Use Claude Pro For Beginners](https://www.youtube.com/watch?v=J3X_JWQkvo8)
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- [@video@Claude FULL COURSE 1 HOUR (Build & Automate Anything)](https://www.youtube.com/watch?v=KrKhfm2Xuho)
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@@ -1,3 +1,10 @@
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# Claude Code
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Claude Code refers to the code-generation capabilities of Anthropic's Claude AI model. It's designed to assist developers by understanding natural language prompts and translating them into functional code across various programming languages. This allows developers to automate repetitive coding tasks, generate code snippets, and even create entire functions or modules based on descriptive instructions.
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Claude Code refers to the code-generation capabilities of Anthropic's Claude AI model. It's designed to assist developers by understanding natural language prompts and translating them into functional code across various programming languages. This allows developers to automate repetitive coding tasks, generate code snippets, and even create entire functions or modules based on descriptive instructions.
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Visit the following resources to learn more:
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- [@roadmap@Visit the Dedicated Claude Code Roadmap](https://roadmap.sh/claude-code)
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- [@course@Claude Code in Action](https://anthropic.skilljar.com/claude-code-in-action)
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- [@official@Claude Code Overview](https://code.claude.com/docs/en/overview)
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- [@video@Introducing Claude Code](https://www.youtube.com/watch?v=AJpK3YTTKZ4)
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@@ -4,6 +4,6 @@ Open-source models are freely available for customization and collaboration, pro
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Visit the following resources to learn more:
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- [@article@Open-Source LLMs vs Closed: Unbiased Guide for Innovative Companies [2026](https://hatchworks.com/blog/gen-ai/open-source-vs-closed-llms-guide/)
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- [@article@https://hatchworks.com/blog/gen-ai/open-source-vs-closed-llms-guide/](https://hatchworks.com/blog/gen-ai/open-source-vs-closed-llms-guide/)
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- [@video@Open Source vs Closed AI: LLMs, Agents & the AI Stack Explained](https://www.youtube.com/watch?v=_QfxGZGITGw)
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- [@video@Open-Source vs Closed-Source LLMs](https://www.youtube.com/watch?v=710PDpuLwOc)
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@@ -1,3 +1,8 @@
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# Codex
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Codex is an AI model created by OpenAI that translates natural language into code. It's designed to understand and generate code in a variety of programming languages, including Python, JavaScript, and more. Codex is particularly adept at interpreting comments and instructions to produce functional code snippets, making it a powerful tool for automating and accelerating the software development process.
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Codex is an AI model created by OpenAI that translates natural language into code. It's designed to understand and generate code in a variety of programming languages, including Python, JavaScript, and more. Codex is particularly adept at interpreting comments and instructions to produce functional code snippets, making it a powerful tool for automating and accelerating the software development process.
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Visit the following resources to learn more:
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- [@official@Codex - Official Webste](https://chatgpt.com/codex)
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- [@video@Getting started with Codex](https://www.youtube.com/watch?v=px7XlbYgk7I)
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# Context Isolation
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Context isolation is about keeping different tasks or areas of knowledge separate when working with large language models (LLMs). Think of it like giving each task its own dedicated space. Instead of one big LLM trying to handle everything at once, you use multiple, smaller "agents" that are each focused on a specific job and trained on their own specific data. This prevents unrelated information from interfering with each other, leading to more accurate and reliable results.
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Context isolation is about keeping different tasks or areas of knowledge separate when working with large language models (LLMs). Think of it like giving each task its own dedicated space. Instead of one big LLM trying to handle everything at once, you use multiple, smaller "agents" that are each focused on a specific job and trained on their own specific data. This prevents unrelated information from interfering with each other, leading to more accurate and reliable results.
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Visit the following resources to learn more:
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# Cursor
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Cursor is an AI-powered code editor designed to enhance developer productivity. It leverages large language models to provide features such as code generation, intelligent autocompletion, and code refactoring suggestions, all within a familiar editor environment. Cursor aims to streamline the coding process and accelerate software development.
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Cursor is an AI-powered code editor designed to enhance developer productivity. It leverages large language models to provide features such as code generation, intelligent autocompletion, and code refactoring suggestions, all within a familiar editor environment. Cursor aims to streamline the coding process and accelerate software development.
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Visit the following resources to learn more:
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- [@official@Cursor Docs](https://cursor.com/docs)
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- [@official@Cursor Learn](https://cursor.com/learn)
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- [@video@Cursor AI Tutorial for Beginners](https://www.youtube.com/watch?v=3289vhOUdKA)
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# Development Tools
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AI has given rise to a collection of AI powered development tools of various different varieties. We have IDEs like Cursor that has AI baked into it, live context capturing tools such as Pieces and a variety of brower based tools like V0, Claude and more.
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Visit the following resources to learn more:
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- [@official@v0 Website](https://v0.dev)
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- [@official@Aider - AI Pair Programming in Terminal](https://aider.chat/)
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- [@official@Replit AI](https://replit.com/ai)
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- [@official@Pieces](https://pieces.app)
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AI has given rise to a collection of AI-powered development tools of various varieties. We have IDEs like Cursor that have AI baked into it, live context capturing tools such as Pieces, and a variety of browser-based tools like V0, Claude, and more.
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@@ -5,6 +5,5 @@ Few-shot prompting is a technique used with large language models (LLMs) where y
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Visit the following resources to learn more:
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- [@article@Few-Shot Prompting](https://www.promptingguide.ai/techniques/fewshot)
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- [@article@Technique #3: Examples in Prompts: From Zero-Shot to Few-Shot](https://learnprompting.org/docs/basics/few_shot?srsltid=AfmBOooXYnhXZxh3YDocIxmsft0KBwCcuKQjaU5gCnBxSJdSvjBgYDDR)
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- [@article@What is few shot prompting?](https://www.ibm.com/think/topics/few-shot-prompting)
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- [@video@Discover Few-Shot Prompting | Google AI Essentials](https://www.youtube.com/watch?v=9qdgEBVkWR4)
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@@ -5,6 +5,5 @@ Fine-tuning involves taking a pre-trained large language model (LLM) and further
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Visit the following resources to learn more:
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- [@article@What is fine-tuning?](https://www.ibm.com/think/topics/fine-tuning)
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- [@article@What is fine-tuning? A guide to fine-tuning LLMs](https://cohere.com/blog/fine-tuning)
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- [@article@How I Fine-Tuned Granite-Vision 2B to Beat a 90B Model — Insights and Lessons Learned](https://towardsdatascience.com/how-i-fine-tuned-granite-vision-2b-to-beat-a-90b-model-insights-and-lessons-learned/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration)
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- [@video@RAG vs Fine-Tuning vs Prompt Engineering: Optimizing AI Models](https://www.youtube.com/watch?v=zYGDpG-pTho)
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@@ -6,5 +6,4 @@ Visit the following resources to learn more:
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- [@article@A Comprehensive Guide to Function Calling in LLMs](https://thenewstack.io/a-comprehensive-guide-to-function-calling-in-llms/)
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- [@article@Function Calling with LLMs | Prompt Engineering Guide](https://www.promptingguide.ai/applications/function_calling)
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- [@article@Function Calling with Open-Source LLMs](https://medium.com/@rushing_andrei/function-calling-with-open-source-llms-594aa5b3a304)
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- [@video@LLM Function Calling - AI Tools Deep Dive](https://www.youtube.com/watch?v=gMeTK6zzaO4)
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# Gemini
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Gemini is a multimodal AI model developed by Google. It's designed to understand and reason across different types of information, including text, code, audio, images, and video. This allows Gemini to solve complex problems and potentially generate new types of content, offering a more holistic approach compared to models focused on a single modality.
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Gemini is a multimodal AI model developed by Google. It's designed to understand and reason across different types of information, including text, code, audio, images, and video. This allows Gemini to solve complex problems and potentially generate new types of content, offering a more holistic approach compared to models focused on a single modality.
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Visit the following resources to learn more:
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- [@official@Google Gemini](https://gemini.google.com/)
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- [@official@Helpful Tips for using Gemini at work](https://services.google.com/fh/files/misc/quick_start_guide_to_google_ai_at_work.pdf)
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- [@video@Welcome to the Gemini era](https://www.youtube.com/watch?v=_fuimO6ErKI)
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@@ -4,7 +4,7 @@ Google Gemini is an advanced AI model by Google DeepMind, designed to integrate
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Visit the following resources to learn more:
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- [@course@Google AI Training](https://grow.google/ai/)
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- [@official@Google Gemini](https://gemini.google.com/)
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- [@official@Google's Gemini Documentation](https://workspace.google.com/solutions/ai/)
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- [@course@Google AI Training](https://grow.google/ai/)
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- [@video@Welcome to the Gemini era](https://www.youtube.com/watch?v=_fuimO6ErKI)
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# Haystack
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# Langchain
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Langchain
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=========
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Haystack is an open-source Python framework that helps you build search and question-answering agents fast. You connect your data sources, pick a language model, and set up pipelines that find the best answer to a user’s query. Haystack handles tasks such as indexing documents, retrieving passages, running the model, and ranking results. It works with many back-ends like Elasticsearch, OpenSearch, FAISS, and Pinecone, so you can scale from a laptop to a cluster. You can add features like summarization, translation, and document chat by dropping extra nodes into the pipeline. The framework also offers REST APIs, a web UI, and clear tutorials, making it easy to test and deploy your agent in production.
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# OpenAI API
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# How LLMs Work
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The OpenAI API provides access to powerful AI models like GPT, Codex, DALL-E, and Whisper, enabling developers to integrate capabilities such as text generation, code assistance, image creation, and speech recognition into their applications via a simple, scalable interface.
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Large Language Models (LLMs) are sophisticated AI systems trained on vast amounts of text data to understand, generate, and manipulate human language. They operate by learning statistical relationships between words and phrases, enabling them to predict the next word in a sequence or generate coherent text based on a given prompt. This is achieved through deep neural networks, primarily using a transformer architecture, which allows them to capture long-range dependencies in text and produce contextually relevant outputs.
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Visit the following resources to learn more:
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- [@official@OpenAI API](https://openai.com/api/)
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- [@article@What is a large language model (LLM)?](https://www.cloudflare.com/en-gb/learning/ai/what-is-large-language-model/)
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- [@article@New to LLMs? Start Here](https://towardsdatascience.com/new-to-llms-start-here/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration)
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- [@video@How Large Language Models Work](https://www.youtube.com/watch?v=5sLYAQS9sWQ)
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@@ -4,6 +4,6 @@ Hugging Face is a leading AI company and open-source platform that provides tool
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Visit the following resources to learn more:
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- [@course@Hugging Face Official Video Course](https://www.youtube.com/watch?v=00GKzGyWFEs&list=PLo2EIpI_JMQvWfQndUesu0nPBAtZ9gP1o)
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- [@official@Hugging Face](https://huggingface.co)
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- [@course@Hugging Face Official Video Course](https://www.youtube.com/watch?v=00GKzGyWFEs&list=PLo2EIpI_JMQvWfQndUesu0nPBAtZ9gP1o)
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- [@video@What is Hugging Face? - Machine Learning Hub Explained](https://www.youtube.com/watch?v=1AUjKfpRZVo)
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@@ -5,7 +5,5 @@ LLMs, or Large Language Models, are advanced AI models trained on vast datasets
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Visit the following resources to learn more:
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- [@article@What is a large language model (LLM)?](https://www.cloudflare.com/en-gb/learning/ai/what-is-large-language-model/)
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- [@article@Understanding AI: Everything you need to know about language models](https://leerob.com/ai)
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- [@article@New to LLMs? Start Here](https://towardsdatascience.com/new-to-llms-start-here/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration)
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- [@video@How Large Language Models Work](https://www.youtube.com/watch?v=5sLYAQS9sWQ)
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- [@video@Large Language Models (LLMs) - Everything You NEED To Know](https://www.youtube.com/watch?v=osKyvYJ3PRM)
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- [@video@How Large Language Models Work](https://www.youtube.com/watch?v=5sLYAQS9sWQ)
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@@ -4,5 +4,5 @@ Meta Llama is a family of large language models (LLMs) developed by Meta AI. The
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Visit the following resources to learn more:
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- [@course@Building with Llama 4](https://www.deeplearning.ai/short-courses/building-with-llama-4/)
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- [@official@Llama](https://www.llama.com/)
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- [@official@Llama](https://www.llama.com/)
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- [@course@Building with Llama 4](https://www.deeplearning.ai/short-courses/building-with-llama-4/)
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@@ -5,6 +5,6 @@ Prompt engineering is the art and science of crafting effective prompts, which a
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Visit the following resources to learn more:
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- [@roadmap@Visit Dedicated Prompt Engineering Roadmap](https://roadmap.sh/prompt-engineering)
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- [@article@hat is Prompt Engineering? - AI Prompt Engineering Explained - AWS](https://aws.amazon.com/what-is/prompt-engineering/)
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- [@article@What is Prompt Engineering? - AI Prompt Engineering Explained - AWS](https://aws.amazon.com/what-is/prompt-engineering/)
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- [@article@Advanced Prompt Engineering for Data Science Projects](https://towardsdatascience.com/advanced-prompt-engineering-for-data-science-projects/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration)
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- [@video@What is Prompt Engineering?](https://www.youtube.com/watch?v=nf1e-55KKbg)
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- [@article@Context engineering vs. prompt engineering](https://www.elastic.co/search-labs/blog/context-engineering-vs-prompt-engineering)
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- [@article@Effective context engineering for AI agents](https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents)
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- [@article@Context Engineering vs Prompt Engineering](https://medium.com/data-science-in-your-pocket/context-engineering-vs-prompt-engineering-379e9622e19d)
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- [@article@Beyond Prompting: The Power of Context Engineering](https://towardsdatascience.com/beyond-prompting-the-power-of-context-engineering/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration)
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- [@video@Context Engineering vs. Prompt Engineering: Smarter AI with RAG & Agents](https://www.youtube.com/watch?v=vD0E3EUb8-8)
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- [@article@Retrieval augmented generation use cases: Transforming data into insights](https://www.glean.com/blog/retrieval-augmented-generation-use-cases)
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- [@article@Retrieval Augmented Generation (RAG) – 5 Use Cases](https://theblue.ai/blog/rag-news/)
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- [@video@Introduction to RAG](https://www.youtube.com/watch?v=LmiFeXH-kq8&list=PL-pTHQz4RcBbz78Z5QXsZhe9rHuCs1Jw-)
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- [@article@How to Train a Chatbot Using RAG and Custom Data](https://towardsdatascience.com/how-to-train-a-chatbot-using-rag-and-custom-data/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration)
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- [@article@How to Train a Chatbot Using RAG and Custom Data](https://towardsdatascience.com/how-to-train-a-chatbot-using-rag-and-custom-data/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration)
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- [@video@Introduction to RAG](https://www.youtube.com/watch?v=LmiFeXH-kq8&list=PL-pTHQz4RcBbz78Z5QXsZhe9rHuCs1Jw-)
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# Replit
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Replit is an online integrated development environment (IDE) that allows users to write and run code in various programming languages directly in their web browser. It provides a collaborative coding environment with features like real-time collaboration, version control, and package management, making it easy to build and deploy projects without needing to install software locally. Replit also incorporates AI features like code completion and generation to help streamline the coding process.
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Replit is an online integrated development environment (IDE) that allows users to write and run code in various programming languages directly in their web browser. It provides a collaborative coding environment with features like real-time collaboration, version control, and package management, making it easy to build and deploy projects without needing to install software locally. Replit also incorporates AI features like code completion and generation to help streamline the coding process.
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Visit the following resources to learn more:
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- [@official@Replit](https://replit.com/)
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- [@article@What is Replit? An honest look at the AI app builder in 2025](https://www.eesel.ai/blog/replit)
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- [@video@Getting Started with Replit](https://www.youtube.com/watch?v=St95nPOwsa8&list=PLto9KpJAqHMTzEMDAFT4r5LlI4NByngyT)
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# Role & Behavior in System Prompting
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# Role & Behavior
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System prompting involves crafting instructions that define the AI model's role, personality, and overall behavior when interacting with users. This allows you to shape the AI's responses, ensuring they are consistent with desired guidelines, such as adopting a specific persona (e.g., a helpful assistant, an expert) or adhering to constraints on tone and style. By carefully defining these aspects, you can significantly influence the AI's output and guide it towards more relevant and effective interactions.
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System prompting involves crafting instructions that define the AI model's role, personality, and overall behavior when interacting with users. This allows you to shape the AI's responses, ensuring they are consistent with desired guidelines, such as adopting a specific persona (e.g., a helpful assistant, an expert) or adhering to constraints on tone and style. By carefully defining these aspects, you can significantly influence the AI's output and guide it towards more relevant and effective interactions.
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Visit the following resources to learn more:
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- [@article@Beyond Basics: Contextual & Role Prompting That Actually Works](https://medium.com/@the_manoj_desai/beyond-basics-contextual-role-prompting-that-actually-works-bd75a0c5086b)
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# Structured Output in System Prompting
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# Structured Outputs
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Structured output in system prompting refers to designing prompts that guide a Large Language Model (LLM) to generate responses in a predefined format, such as JSON, XML, or a specific text-based structure. This approach focuses on crafting system prompts to elicit predictable and parseable outputs, making it easier to integrate LLM responses into downstream applications and workflows. By providing clear instructions and examples of the desired output structure, we can reliably extract information and automate processes.
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@@ -5,6 +5,5 @@ Temperature is a parameter used in language models that controls the randomness
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Visit the following resources to learn more:
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- [@article@What Temperature Means in Natural Language Processing and AI](https://thenewstack.io/what-temperature-means-in-natural-language-processing-and-ai/)
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- [@article@LLM Temperature: How It Works and When You Should Use It](https://www.vellum.ai/llm-parameters/temperature)
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- [@article@hat is LLM Temperature? - IBM](https://www.ibm.com/think/topics/llm-temperature)
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- [@article@What is LLM Temperature? - IBM](https://www.ibm.com/think/topics/llm-temperature)
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- [@article@How Temperature Settings Transform Your AI Agent's Responses](https://docsbot.ai/article/how-temperature-settings-transform-your-ai-agents-responses)
|
||||
@@ -1,3 +1,9 @@
|
||||
# Windsurf
|
||||
|
||||
Windsurf is a tool specifically designed to enhance code navigation and understanding within large codebases. It leverages AI to provide intelligent code search, relationship discovery between different code elements, and code completion suggestions that are contextually aware. It's intended to reduce the time developers spend exploring and understanding code, enabling them to write more efficient and accurate code.
|
||||
Windsurf is a tool specifically designed to enhance code navigation and understanding within large codebases. It leverages AI to provide intelligent code search, relationship discovery between different code elements, and code completion suggestions that are contextually aware. It's intended to reduce the time developers spend exploring and understanding code, enabling them to write more efficient and accurate code.
|
||||
|
||||
Visit the following resources to learn more:
|
||||
|
||||
- [@official@Windsurf Docs](https://docs.windsurf.com/windsurf/getting-started)
|
||||
- [@video@Windsurf Tutorial for Beginners (AI Code Editor) - Better than Cursor??](https://www.youtube.com/watch?v=8TcWGk1DJVs)
|
||||
- [@video@Windsurf AI Tutorial for Beginners](https://www.youtube.com/watch?v=x1VCmB__TDo)
|
||||
Reference in New Issue
Block a user