From b0878c3481aab39a31d95fc9dad0bfb2bfd1be0c Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" <41898282+github-actions[bot]@users.noreply.github.com> Date: Tue, 3 Mar 2026 14:20:36 +0100 Subject: [PATCH] chore: sync content to repo (#9677) Co-authored-by: kamranahmedse <4921183+kamranahmedse@users.noreply.github.com> --- .../content/agents-usecases@778HsQzTuJ_3c9OSn5DmH.md | 3 ++- .../ai-engineer/content/ai-agents@4_ap0rD9Gl6Ep_4jMfPpG.md | 1 + .../ai-engineer-vs-ml-engineer@jSZ1LhPdhlkW-9QJhIvFs.md | 1 + .../ai-engineer/content/ai-vs-agi@5QdihE1lLpMc3DFrGy46M.md | 3 ++- .../content/anomaly-detection@AglWJ7gb9rTT2rMkstxtk.md | 3 ++- .../content/anthropic-claude@hy6EyKiNxk1x84J63dhez.md | 1 + .../closed-vs-open-source-models@RBwGsq9DngUsl8PrrCbqx.md | 2 +- .../content/context-compaction@9XCxilAQ7FRet7lHQr1gE.md | 2 +- .../content/context-engineering@kCiHNaZ9CgnS9uksIQ_SY.md | 1 + .../content/external-memory@KWjD4xEPhOOYS51dvRLd2.md | 7 +++---- .../content/fine-tuning@zTvsCNS3ucsZmvy1tHyeI.md | 3 ++- .../content/google-adk@mbp2NoL-VZ5hZIIblNBXt.md | 4 ++-- .../content/google-gemini@oe8E6ZIQWuYvHVbYJHUc1.md | 4 ++-- .../ai-engineer/content/haystack@ebXXEhNRROjbbof-Gym4p.md | 3 ++- .../content/hugging-face-hub@YLOdOvLXa5Fa7_mmuvKEi.md | 4 ++-- .../content/hugging-face@v99C5Bml2a6148LCJ9gy9.md | 2 +- .../large-language-model-llm@wf2BSyUekr1S1q6l8kyq6.md | 1 + .../content/meta-llama@OkYO-aSPiuVYuLXHswBCn.md | 4 ++-- .../content/prompt-engineering@VjXmSCdzi2ACv-W85Sy9D.md | 1 + .../prompt-vs-context-engineering@ozrR8IvjNFbHd44kZrExX.md | 1 + .../purpose-and-functionality@WcjX6p-V-Rdd77EL8Ega9.md | 3 ++- .../rag-and-dynamic-filters@LnQ2AatMWpExUHcZhDIPd.md | 3 ++- .../content/rag-usecases@GCn4LGNEtPI0NWYAZCRE-.md | 3 ++- .../ai-engineer/content/rag@IX1BJWGwGmB4L063g0Frf.md | 1 + .../ai-engineer/content/tokens@2WbVpRLqwi3Oeqk1JPui4.md | 7 +++---- .../tools--function-calling@eOqCBgBTKM8CmY3nsWjre.md | 4 ++-- .../what-is-an-ai-engineer@GN6SnI7RXIeW8JeD-qORW.md | 3 ++- .../ai-engineer/content/zero-shot@15XOFdVp0IC-kLYPXUJWh.md | 5 ++--- 28 files changed, 47 insertions(+), 33 deletions(-) diff --git a/src/data/roadmaps/ai-engineer/content/agents-usecases@778HsQzTuJ_3c9OSn5DmH.md b/src/data/roadmaps/ai-engineer/content/agents-usecases@778HsQzTuJ_3c9OSn5DmH.md index 030ae1200..5feaa7dc4 100644 --- a/src/data/roadmaps/ai-engineer/content/agents-usecases@778HsQzTuJ_3c9OSn5DmH.md +++ b/src/data/roadmaps/ai-engineer/content/agents-usecases@778HsQzTuJ_3c9OSn5DmH.md @@ -6,4 +6,5 @@ Visit the following resources to learn more: - [@article@Top 15 Use Cases Of AI Agents In Business](https://www.ampcome.com/post/15-use-cases-of-ai-agents-in-business) - [@article@A Brief Guide on AI Agents: Benefits and Use Cases](https://www.codica.com/blog/brief-guide-on-ai-agents/) -- [@video@The Complete Guide to Building AI Agents for Beginners](https://youtu.be/MOyl58VF2ak?si=-QjRD_5y3iViprJX) \ No newline at end of file +- [@video@The Complete Guide to Building AI Agents for Beginners](https://youtu.be/MOyl58VF2ak?si=-QjRD_5y3iViprJX) +- [@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) \ No newline at end of file diff --git a/src/data/roadmaps/ai-engineer/content/ai-agents@4_ap0rD9Gl6Ep_4jMfPpG.md b/src/data/roadmaps/ai-engineer/content/ai-agents@4_ap0rD9Gl6Ep_4jMfPpG.md index b549e9e30..ed2e8afe8 100644 --- a/src/data/roadmaps/ai-engineer/content/ai-agents@4_ap0rD9Gl6Ep_4jMfPpG.md +++ b/src/data/roadmaps/ai-engineer/content/ai-agents@4_ap0rD9Gl6Ep_4jMfPpG.md @@ -6,4 +6,5 @@ Visit the following resources to learn more: - [@article@Building an AI Agent Tutorial - LangChain](https://python.langchain.com/docs/tutorials/agents/) - [@article@AI Agents and Their Types](https://play.ht/blog/ai-agents-use-cases/) +- [@article@How to Design My First AI Agent](https://towardsdatascience.com/how-to-design-my-first-ai-agent/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration) - [@video@The Complete Guide to Building AI Agents for Beginners](https://youtu.be/MOyl58VF2ak?si=-QjRD_5y3iViprJX) \ No newline at end of file diff --git a/src/data/roadmaps/ai-engineer/content/ai-engineer-vs-ml-engineer@jSZ1LhPdhlkW-9QJhIvFs.md b/src/data/roadmaps/ai-engineer/content/ai-engineer-vs-ml-engineer@jSZ1LhPdhlkW-9QJhIvFs.md index 5a0fbb8ad..89842131d 100644 --- a/src/data/roadmaps/ai-engineer/content/ai-engineer-vs-ml-engineer@jSZ1LhPdhlkW-9QJhIvFs.md +++ b/src/data/roadmaps/ai-engineer/content/ai-engineer-vs-ml-engineer@jSZ1LhPdhlkW-9QJhIvFs.md @@ -6,4 +6,5 @@ Visit the following resources to learn more: - [@article@What does an AI Engineer do?](https://www.codecademy.com/resources/blog/what-does-an-ai-engineer-do/) - [@article@What is an ML Engineer?](https://www.coursera.org/articles/what-is-machine-learning-engineer) +- [@article@Machine Learning vs AI Engineer: What Are the Differences?](https://towardsdatascience.com/machine-learning-vs-ai-engineer-no-confusing-jargon/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration) - [@video@AI vs ML](https://www.youtube.com/watch?v=4RixMPF4xis) \ No newline at end of file diff --git a/src/data/roadmaps/ai-engineer/content/ai-vs-agi@5QdihE1lLpMc3DFrGy46M.md b/src/data/roadmaps/ai-engineer/content/ai-vs-agi@5QdihE1lLpMc3DFrGy46M.md index 5a6f86611..0251daa65 100644 --- a/src/data/roadmaps/ai-engineer/content/ai-vs-agi@5QdihE1lLpMc3DFrGy46M.md +++ b/src/data/roadmaps/ai-engineer/content/ai-vs-agi@5QdihE1lLpMc3DFrGy46M.md @@ -5,4 +5,5 @@ AI (Artificial Intelligence) refers to systems designed to perform specific task Visit the following resources to learn more: - [@article@What is AGI?](https://aws.amazon.com/what-is/artificial-general-intelligence/) -- [@article@The crucial difference between AI and AGI](https://www.forbes.com/sites/bernardmarr/2024/05/20/the-crucial-difference-between-ai-and-agi/) \ No newline at end of file +- [@article@The crucial difference between AI and AGI](https://www.forbes.com/sites/bernardmarr/2024/05/20/the-crucial-difference-between-ai-and-agi/) +- [@article@Stop Worrying about AGI: The Immediate Danger is Reduced General Intelligence (RGI)](https://towardsdatascience.com/stop-worrying-about-agi-the-immediate-danger-is-reduced-general-intelligence-rgi/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration) \ No newline at end of file diff --git a/src/data/roadmaps/ai-engineer/content/anomaly-detection@AglWJ7gb9rTT2rMkstxtk.md b/src/data/roadmaps/ai-engineer/content/anomaly-detection@AglWJ7gb9rTT2rMkstxtk.md index 8a8f3baf3..b03ec0eab 100644 --- a/src/data/roadmaps/ai-engineer/content/anomaly-detection@AglWJ7gb9rTT2rMkstxtk.md +++ b/src/data/roadmaps/ai-engineer/content/anomaly-detection@AglWJ7gb9rTT2rMkstxtk.md @@ -4,4 +4,5 @@ Anomaly detection with embeddings works by transforming data, such as text, imag Visit the following resources to learn more: -- [@article@Anomaly in Embeddings](https://ai.google.dev/gemini-api/tutorials/anomaly_detection) \ No newline at end of file +- [@article@Anomaly in Embeddings](https://ai.google.dev/gemini-api/tutorials/anomaly_detection) +- [@article@Boosting Your Anomaly Detection With LLMs](https://towardsdatascience.com/boosting-your-anomaly-detection-with-llms/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration) \ No newline at end of file diff --git a/src/data/roadmaps/ai-engineer/content/anthropic-claude@hy6EyKiNxk1x84J63dhez.md b/src/data/roadmaps/ai-engineer/content/anthropic-claude@hy6EyKiNxk1x84J63dhez.md index 8d9c6327d..038106ec4 100644 --- a/src/data/roadmaps/ai-engineer/content/anthropic-claude@hy6EyKiNxk1x84J63dhez.md +++ b/src/data/roadmaps/ai-engineer/content/anthropic-claude@hy6EyKiNxk1x84J63dhez.md @@ -7,4 +7,5 @@ Visit the following resources to learn more: - [@official@Claude](https://claude.ai) - [@course@Claude 101](https://anthropic.skilljar.com/claude-101) - [@video@How To Use Claude Pro For Beginners](https://www.youtube.com/watch?v=J3X_JWQkvo8) +- [@article@How To Use Claude Pro For Beginners](https://www.youtube.com/watch?v=J3X_JWQkvo8) - [@video@Claude FULL COURSE 1 HOUR (Build & Automate Anything)](https://www.youtube.com/watch?v=KrKhfm2Xuho) \ No newline at end of file diff --git a/src/data/roadmaps/ai-engineer/content/closed-vs-open-source-models@RBwGsq9DngUsl8PrrCbqx.md b/src/data/roadmaps/ai-engineer/content/closed-vs-open-source-models@RBwGsq9DngUsl8PrrCbqx.md index af74f72c6..53dd4c0fd 100644 --- a/src/data/roadmaps/ai-engineer/content/closed-vs-open-source-models@RBwGsq9DngUsl8PrrCbqx.md +++ b/src/data/roadmaps/ai-engineer/content/closed-vs-open-source-models@RBwGsq9DngUsl8PrrCbqx.md @@ -4,6 +4,6 @@ Open-source models are freely available for customization and collaboration, pro Visit the following resources to learn more: -- [@article@Open-Source LLMs vs Closed: Unbiased Guide for Innovative Companies [2026]](https://hatchworks.com/blog/gen-ai/open-source-vs-closed-llms-guide/) +- [@article@Open-Source LLMs vs Closed: Unbiased Guide for Innovative Companies [2026](https://hatchworks.com/blog/gen-ai/open-source-vs-closed-llms-guide/) - [@video@Open Source vs Closed AI: LLMs, Agents & the AI Stack Explained](https://www.youtube.com/watch?v=_QfxGZGITGw) - [@video@Open-Source vs Closed-Source LLMs](https://www.youtube.com/watch?v=710PDpuLwOc) \ No newline at end of file diff --git a/src/data/roadmaps/ai-engineer/content/context-compaction@9XCxilAQ7FRet7lHQr1gE.md b/src/data/roadmaps/ai-engineer/content/context-compaction@9XCxilAQ7FRet7lHQr1gE.md index 08a969e00..e8293b4a6 100644 --- a/src/data/roadmaps/ai-engineer/content/context-compaction@9XCxilAQ7FRet7lHQr1gE.md +++ b/src/data/roadmaps/ai-engineer/content/context-compaction@9XCxilAQ7FRet7lHQr1gE.md @@ -5,4 +5,4 @@ Context compaction is a technique used to reduce the length of the context provi Visit the following resources to learn more: - [@article@Context Engineering](https://blog.langchain.com/context-engineering-for-agents/) -- [@opensource@Context Compaction](https://gist.github.com/badlogic/cd2ef65b0697c4dbe2d13fbecb0a0a5f) \ No newline at end of file +- [@article@Context Compaction](https://gist.github.com/badlogic/cd2ef65b0697c4dbe2d13fbecb0a0a5f) \ No newline at end of file diff --git a/src/data/roadmaps/ai-engineer/content/context-engineering@kCiHNaZ9CgnS9uksIQ_SY.md b/src/data/roadmaps/ai-engineer/content/context-engineering@kCiHNaZ9CgnS9uksIQ_SY.md index aecac8810..7ab48be19 100644 --- a/src/data/roadmaps/ai-engineer/content/context-engineering@kCiHNaZ9CgnS9uksIQ_SY.md +++ b/src/data/roadmaps/ai-engineer/content/context-engineering@kCiHNaZ9CgnS9uksIQ_SY.md @@ -6,4 +6,5 @@ Visit the following resources to learn more: - [@article@Context Engineering Guide](https://www.promptingguide.ai/guides/context-engineering-guide) - [@article@Effective context engineering for AI agents](https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents) +- [@article@How to Perform Effective Agentic Context Engineering](https://towardsdatascience.com/how-to-perform-effective-agentic-context-engineering/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration) - [@video@Context Engineering vs. Prompt Engineering: Smarter AI with RAG & Agents](https://www.youtube.com/watch?v=vD0E3EUb8-8) \ No newline at end of file diff --git a/src/data/roadmaps/ai-engineer/content/external-memory@KWjD4xEPhOOYS51dvRLd2.md b/src/data/roadmaps/ai-engineer/content/external-memory@KWjD4xEPhOOYS51dvRLd2.md index 7b9095a19..0970f1b02 100644 --- a/src/data/roadmaps/ai-engineer/content/external-memory@KWjD4xEPhOOYS51dvRLd2.md +++ b/src/data/roadmaps/ai-engineer/content/external-memory@KWjD4xEPhOOYS51dvRLd2.md @@ -1,8 +1,7 @@ -# External Memory for LLMs +# External Memory -External memory refers to the techniques used to provide Large Language Models (LLMs) with access to information that is not stored directly within their parameters. This allows LLMs to access and utilize a much broader and more up-to-date knowledge base than what was available during their training. By using external memory, LLMs can overcome limitations related to knowledge cut-off, hallucination, and the inability to incorporate new information, leading to more accurate, reliable, and contextually relevant respons +External memory, in the context of large language models (LLMs), refers to mechanisms that allow these models to access and utilize information stored outside of their internal parameters. This can involve retrieving relevant data from databases, knowledge graphs, or other external sources during the prompt processing or generation phases to augment the model's knowledge and improve its performance on specific tasks. This enhances the LLM's ability to handle complex queries and generate more accurate and contextually relevant responses. Visit the following resources to learn more: -- [@article@Context Engineering - LLM Memory and Retrieval for AI Agents](https://weaviate.io/blog/context-engineering) -- [@article@4 context engineering strategies every AI engineer needs to know](https://newsletter.owainlewis.com/i/180013006/1-write-external-memory) \ No newline at end of file +- [@article@How to Maximize Agentic Memory for Continual Learning](https://towardsdatascience.com/how-to-maximize-agentic-memory-for-continual-learning/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration) \ No newline at end of file diff --git a/src/data/roadmaps/ai-engineer/content/fine-tuning@zTvsCNS3ucsZmvy1tHyeI.md b/src/data/roadmaps/ai-engineer/content/fine-tuning@zTvsCNS3ucsZmvy1tHyeI.md index 78b26f143..b868011c2 100644 --- a/src/data/roadmaps/ai-engineer/content/fine-tuning@zTvsCNS3ucsZmvy1tHyeI.md +++ b/src/data/roadmaps/ai-engineer/content/fine-tuning@zTvsCNS3ucsZmvy1tHyeI.md @@ -1,9 +1,10 @@ # Fine-tuning -Fine-tuning involves taking a pre-trained large language model (LLM) and further training it on a smaller, task-specific dataset. This adapts the LLM to perform better on a particular task or domain. However, fine-tuning can be resource-intensive and may not always be the most efficient approach. Prompt engineering, retrieval-augmented generation (RAG), or using smaller, specialized models can sometimes achieve comparable or even better results with less computational overhead and data requirements. +Fine-tuning involves taking a pre-trained large language model (LLM) and further training it on a smaller, task-specific dataset. This adapts the LLM to perform better on a particular task or domain. However, fine-tuning can be resource-intensive and may not always be the most efficient approach. Prompt engineering, retrieval-augmented generation (RAG), or using smaller, specialized models can sometimes achieve comparable or even better results with less computational overhead and data requirements. Visit the following resources to learn more: - [@article@What is fine-tuning?](https://www.ibm.com/think/topics/fine-tuning) - [@article@What is fine-tuning? A guide to fine-tuning LLMs](https://cohere.com/blog/fine-tuning) +- [@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) - [@video@RAG vs Fine-Tuning vs Prompt Engineering: Optimizing AI Models](https://www.youtube.com/watch?v=zYGDpG-pTho) \ No newline at end of file diff --git a/src/data/roadmaps/ai-engineer/content/google-adk@mbp2NoL-VZ5hZIIblNBXt.md b/src/data/roadmaps/ai-engineer/content/google-adk@mbp2NoL-VZ5hZIIblNBXt.md index 401669d06..a5040b41a 100644 --- a/src/data/roadmaps/ai-engineer/content/google-adk@mbp2NoL-VZ5hZIIblNBXt.md +++ b/src/data/roadmaps/ai-engineer/content/google-adk@mbp2NoL-VZ5hZIIblNBXt.md @@ -4,6 +4,6 @@ The Google Agent Development Kit (ADK) is a framework designed to help developer Visit the following resources to learn more: +- [@course@ADK Crash Course - From Beginner To Expert](https://codelabs.developers.google.com/onramp/instructions#0) - [@official@Agent Development Kit](https://google.github.io/adk-docs/) -- [@official@Overview of Agent Development Kit](https://docs.cloud.google.com/agent-builder/agent-development-kit/overview) -- [@course@ADK Crash Course - From Beginner To Expert](https://codelabs.developers.google.com/onramp/instructions#0) \ No newline at end of file +- [@official@Overview of Agent Development Kit](https://docs.cloud.google.com/agent-builder/agent-development-kit/overview) \ No newline at end of file diff --git a/src/data/roadmaps/ai-engineer/content/google-gemini@oe8E6ZIQWuYvHVbYJHUc1.md b/src/data/roadmaps/ai-engineer/content/google-gemini@oe8E6ZIQWuYvHVbYJHUc1.md index 1aafc8887..084fcc039 100644 --- a/src/data/roadmaps/ai-engineer/content/google-gemini@oe8E6ZIQWuYvHVbYJHUc1.md +++ b/src/data/roadmaps/ai-engineer/content/google-gemini@oe8E6ZIQWuYvHVbYJHUc1.md @@ -1,6 +1,6 @@ -# Google Gemini +# Google's Gemini -Google Gemini is a family of multimodal large language models (LLMs) developed by Google AI. It's designed to understand and generate content across various modalities, including text, images, audio, and video. Gemini comes in different sizes and capabilities, allowing developers to choose the best model for their specific needs and resource constraints. +Google Gemini is an advanced AI model by Google DeepMind, designed to integrate natural language processing with multimodal capabilities, enabling it to understand and generate not just text but also images, videos, and other data types. It combines generative AI with reasoning skills, making it effective for complex tasks requiring logical analysis and contextual understanding. Visit the following resources to learn more: diff --git a/src/data/roadmaps/ai-engineer/content/haystack@ebXXEhNRROjbbof-Gym4p.md b/src/data/roadmaps/ai-engineer/content/haystack@ebXXEhNRROjbbof-Gym4p.md index 32fccfd57..f10aa60a6 100644 --- a/src/data/roadmaps/ai-engineer/content/haystack@ebXXEhNRROjbbof-Gym4p.md +++ b/src/data/roadmaps/ai-engineer/content/haystack@ebXXEhNRROjbbof-Gym4p.md @@ -1,9 +1,10 @@ # Haystack +# Langchain 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. Visit the following resources to learn more: - [@official@Haystack](https://haystack.deepset.ai/) -- [@official@@Haystack Overview](https://docs.haystack.deepset.ai/docs/intro) +- [@official@Haystack Overview](https://docs.haystack.deepset.ai/docs/intro) - [@opensource@deepset-ai/haystack](https://github.com/deepset-ai/haystack) \ No newline at end of file diff --git a/src/data/roadmaps/ai-engineer/content/hugging-face-hub@YLOdOvLXa5Fa7_mmuvKEi.md b/src/data/roadmaps/ai-engineer/content/hugging-face-hub@YLOdOvLXa5Fa7_mmuvKEi.md index a6f46f398..de9a1e10d 100644 --- a/src/data/roadmaps/ai-engineer/content/hugging-face-hub@YLOdOvLXa5Fa7_mmuvKEi.md +++ b/src/data/roadmaps/ai-engineer/content/hugging-face-hub@YLOdOvLXa5Fa7_mmuvKEi.md @@ -4,5 +4,5 @@ The Hugging Face Hub is a central platform where users can discover, share, and Visit the following resources to learn more: -- [@official@Hugging Face Documentation](https://huggingface.co/docs/hub/en/index) -- [@course@The Hugging Face Hub (LLM Course)](https://huggingface.co/learn/nlp-course/en/chapter4/1) \ No newline at end of file +- [@course@The Hugging Face Hub (LLM Course)](https://huggingface.co/learn/nlp-course/en/chapter4/1) +- [@official@Hugging Face Documentation](https://huggingface.co/docs/hub/en/index) \ No newline at end of file diff --git a/src/data/roadmaps/ai-engineer/content/hugging-face@v99C5Bml2a6148LCJ9gy9.md b/src/data/roadmaps/ai-engineer/content/hugging-face@v99C5Bml2a6148LCJ9gy9.md index 7a8f184ed..7781bd257 100644 --- a/src/data/roadmaps/ai-engineer/content/hugging-face@v99C5Bml2a6148LCJ9gy9.md +++ b/src/data/roadmaps/ai-engineer/content/hugging-face@v99C5Bml2a6148LCJ9gy9.md @@ -4,6 +4,6 @@ Hugging Face is a leading AI company and open-source platform that provides tool Visit the following resources to learn more: -- [@official@Hugging Face](https://huggingface.co) - [@course@Hugging Face Official Video Course](https://www.youtube.com/watch?v=00GKzGyWFEs&list=PLo2EIpI_JMQvWfQndUesu0nPBAtZ9gP1o) +- [@official@Hugging Face](https://huggingface.co) - [@video@What is Hugging Face? - Machine Learning Hub Explained](https://www.youtube.com/watch?v=1AUjKfpRZVo) \ No newline at end of file diff --git a/src/data/roadmaps/ai-engineer/content/large-language-model-llm@wf2BSyUekr1S1q6l8kyq6.md b/src/data/roadmaps/ai-engineer/content/large-language-model-llm@wf2BSyUekr1S1q6l8kyq6.md index 226679800..d763420db 100644 --- a/src/data/roadmaps/ai-engineer/content/large-language-model-llm@wf2BSyUekr1S1q6l8kyq6.md +++ b/src/data/roadmaps/ai-engineer/content/large-language-model-llm@wf2BSyUekr1S1q6l8kyq6.md @@ -6,5 +6,6 @@ Visit the following resources to learn more: - [@article@What is a large language model (LLM)?](https://www.cloudflare.com/en-gb/learning/ai/what-is-large-language-model/) - [@article@Understanding AI: Everything you need to know about language models](https://leerob.com/ai) +- [@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) - [@video@How Large Language Models Work](https://www.youtube.com/watch?v=5sLYAQS9sWQ) - [@video@Large Language Models (LLMs) - Everything You NEED To Know](https://www.youtube.com/watch?v=osKyvYJ3PRM) \ No newline at end of file diff --git a/src/data/roadmaps/ai-engineer/content/meta-llama@OkYO-aSPiuVYuLXHswBCn.md b/src/data/roadmaps/ai-engineer/content/meta-llama@OkYO-aSPiuVYuLXHswBCn.md index e425cba29..ee37ff96a 100644 --- a/src/data/roadmaps/ai-engineer/content/meta-llama@OkYO-aSPiuVYuLXHswBCn.md +++ b/src/data/roadmaps/ai-engineer/content/meta-llama@OkYO-aSPiuVYuLXHswBCn.md @@ -4,5 +4,5 @@ Meta Llama is a family of large language models (LLMs) developed by Meta AI. The Visit the following resources to learn more: -- [@official@Llama](https://www.llama.com/) -- [@course@Building with Llama 4](https://www.deeplearning.ai/short-courses/building-with-llama-4/) \ No newline at end of file +- [@course@Building with Llama 4](https://www.deeplearning.ai/short-courses/building-with-llama-4/) +- [@official@Llama](https://www.llama.com/) \ No newline at end of file diff --git a/src/data/roadmaps/ai-engineer/content/prompt-engineering@VjXmSCdzi2ACv-W85Sy9D.md b/src/data/roadmaps/ai-engineer/content/prompt-engineering@VjXmSCdzi2ACv-W85Sy9D.md index 246189414..ca4f32484 100644 --- a/src/data/roadmaps/ai-engineer/content/prompt-engineering@VjXmSCdzi2ACv-W85Sy9D.md +++ b/src/data/roadmaps/ai-engineer/content/prompt-engineering@VjXmSCdzi2ACv-W85Sy9D.md @@ -6,4 +6,5 @@ Visit the following resources to learn more: - [@roadmap@Visit Dedicated Prompt Engineering Roadmap](https://roadmap.sh/prompt-engineering) - [@article@hat is Prompt Engineering? - AI Prompt Engineering Explained - AWS](https://aws.amazon.com/what-is/prompt-engineering/) +- [@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) - [@video@What is Prompt Engineering?](https://www.youtube.com/watch?v=nf1e-55KKbg) \ No newline at end of file diff --git a/src/data/roadmaps/ai-engineer/content/prompt-vs-context-engineering@ozrR8IvjNFbHd44kZrExX.md b/src/data/roadmaps/ai-engineer/content/prompt-vs-context-engineering@ozrR8IvjNFbHd44kZrExX.md index 38705043d..fd8db46e9 100644 --- a/src/data/roadmaps/ai-engineer/content/prompt-vs-context-engineering@ozrR8IvjNFbHd44kZrExX.md +++ b/src/data/roadmaps/ai-engineer/content/prompt-vs-context-engineering@ozrR8IvjNFbHd44kZrExX.md @@ -7,4 +7,5 @@ Visit the following resources to learn more: - [@article@Context engineering vs. prompt engineering](https://www.elastic.co/search-labs/blog/context-engineering-vs-prompt-engineering) - [@article@Effective context engineering for AI agents](https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents) - [@article@Context Engineering vs Prompt Engineering](https://medium.com/data-science-in-your-pocket/context-engineering-vs-prompt-engineering-379e9622e19d) +- [@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) - [@video@Context Engineering vs. Prompt Engineering: Smarter AI with RAG & Agents](https://www.youtube.com/watch?v=vD0E3EUb8-8) \ No newline at end of file diff --git a/src/data/roadmaps/ai-engineer/content/purpose-and-functionality@WcjX6p-V-Rdd77EL8Ega9.md b/src/data/roadmaps/ai-engineer/content/purpose-and-functionality@WcjX6p-V-Rdd77EL8Ega9.md index b63805107..5882a05fc 100644 --- a/src/data/roadmaps/ai-engineer/content/purpose-and-functionality@WcjX6p-V-Rdd77EL8Ega9.md +++ b/src/data/roadmaps/ai-engineer/content/purpose-and-functionality@WcjX6p-V-Rdd77EL8Ega9.md @@ -5,4 +5,5 @@ A vector database is designed to store, manage, and retrieve high-dimensional ve Visit the following resources to learn more: - [@article@What is a Vector Database? Top 12 Use Cases](https://lakefs.io/blog/what-is-vector-databases/) -- [@article@Vector Databases: Intro, Use Cases](https://www.v7labs.com/blog/vector-databases) \ No newline at end of file +- [@article@Vector Databases: Intro, Use Cases](https://www.v7labs.com/blog/vector-databases) +- [@article@When (Not) to Use Vector DB](https://towardsdatascience.com/when-not-to-use-vector-db/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration) \ No newline at end of file diff --git a/src/data/roadmaps/ai-engineer/content/rag-and-dynamic-filters@LnQ2AatMWpExUHcZhDIPd.md b/src/data/roadmaps/ai-engineer/content/rag-and-dynamic-filters@LnQ2AatMWpExUHcZhDIPd.md index c9b88c9c9..b0a13a1a1 100644 --- a/src/data/roadmaps/ai-engineer/content/rag-and-dynamic-filters@LnQ2AatMWpExUHcZhDIPd.md +++ b/src/data/roadmaps/ai-engineer/content/rag-and-dynamic-filters@LnQ2AatMWpExUHcZhDIPd.md @@ -5,4 +5,5 @@ Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by pr Visit the following resources to learn more: - [@article@4 context engineering strategies every AI engineer needs to know](https://newsletter.owainlewis.com/p/4-context-engineering-strategies) -- [@article@Context Engineering](https://blog.langchain.com/context-engineering-for-agents/) \ No newline at end of file +- [@article@Context Engineering](https://blog.langchain.com/context-engineering-for-agents/) +- [@article@Is RAG Dead? The Rise of Context Engineering and Semantic Layers for Agentic AI](https://towardsdatascience.com/beyond-rag/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration) \ No newline at end of file diff --git a/src/data/roadmaps/ai-engineer/content/rag-usecases@GCn4LGNEtPI0NWYAZCRE-.md b/src/data/roadmaps/ai-engineer/content/rag-usecases@GCn4LGNEtPI0NWYAZCRE-.md index 9c9c4f895..623029469 100644 --- a/src/data/roadmaps/ai-engineer/content/rag-usecases@GCn4LGNEtPI0NWYAZCRE-.md +++ b/src/data/roadmaps/ai-engineer/content/rag-usecases@GCn4LGNEtPI0NWYAZCRE-.md @@ -6,4 +6,5 @@ Visit the following resources to learn more: - [@article@Retrieval augmented generation use cases: Transforming data into insights](https://www.glean.com/blog/retrieval-augmented-generation-use-cases) - [@article@Retrieval Augmented Generation (RAG) – 5 Use Cases](https://theblue.ai/blog/rag-news/) -- [@video@Introduction to RAG](https://www.youtube.com/watch?v=LmiFeXH-kq8&list=PL-pTHQz4RcBbz78Z5QXsZhe9rHuCs1Jw-) \ No newline at end of file +- [@video@Introduction to RAG](https://www.youtube.com/watch?v=LmiFeXH-kq8&list=PL-pTHQz4RcBbz78Z5QXsZhe9rHuCs1Jw-) +- [@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) \ No newline at end of file diff --git a/src/data/roadmaps/ai-engineer/content/rag@IX1BJWGwGmB4L063g0Frf.md b/src/data/roadmaps/ai-engineer/content/rag@IX1BJWGwGmB4L063g0Frf.md index 3c9f87509..ee10c2ca7 100644 --- a/src/data/roadmaps/ai-engineer/content/rag@IX1BJWGwGmB4L063g0Frf.md +++ b/src/data/roadmaps/ai-engineer/content/rag@IX1BJWGwGmB4L063g0Frf.md @@ -5,4 +5,5 @@ Retrieval-Augmented Generation (RAG) is an AI approach that combines information Visit the following resources to learn more: - [@article@What is Retrieval-Augmented Generation? - Google](https://cloud.google.com/use-cases/retrieval-augmented-generation) +- [@article@RAG Explained: Understanding Embeddings, Similarity, and Retrieval](https://towardsdatascience.com/rag-explained-understanding-embeddings-similarity-and-retrieval/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration) - [@video@What is Retrieval-Augmented Generation? - IBM](https://www.youtube.com/watch?v=T-D1OfcDW1M) \ No newline at end of file diff --git a/src/data/roadmaps/ai-engineer/content/tokens@2WbVpRLqwi3Oeqk1JPui4.md b/src/data/roadmaps/ai-engineer/content/tokens@2WbVpRLqwi3Oeqk1JPui4.md index d34c836af..90125f19a 100644 --- a/src/data/roadmaps/ai-engineer/content/tokens@2WbVpRLqwi3Oeqk1JPui4.md +++ b/src/data/roadmaps/ai-engineer/content/tokens@2WbVpRLqwi3Oeqk1JPui4.md @@ -1,9 +1,8 @@ -# Tokens +# Tokens in Large Language Models -Tokens are the fundamental building blocks of large language models (LLMs). They are discrete units of text that the model processes and uses to understand and generate language. These units can be words, parts of words, or even individual characters, depending on the model's vocabulary. LLMs work by predicting the next token in a sequence, based on the preceding tokens and their learned patterns. +Tokens are fundamental units of text that LLMs process, created by breaking text into smaller components such as words, subwords, or characters. Understanding tokens is crucial because models predict the next token in sequences, API costs are based on token count, and models have maximum token limits for input and output. Visit the following resources to learn more: - [@article@Explaining Tokens — the Language and Currency of AI](https://blogs.nvidia.com/blog/ai-tokens-explained/) -- [@article@Understanding Tokens and Parameters in Model Training: A Deep Dive](https://www.functionize.com/blog/understanding-tokens-and-parameters-in-model-training) -- [@video@Most devs don't understand how LLM tokens work](https://www.youtube.com/watch?v=nKSk_TiR8YA&t=33s) \ No newline at end of file +- [@article@Understanding Tokens and Parameters in Model Training: A Deep Dive](ttps://www.functionize.com/blog/understanding-tokens-and-parameters-in-model-training) \ No newline at end of file diff --git a/src/data/roadmaps/ai-engineer/content/tools--function-calling@eOqCBgBTKM8CmY3nsWjre.md b/src/data/roadmaps/ai-engineer/content/tools--function-calling@eOqCBgBTKM8CmY3nsWjre.md index c2e75f23e..62d32fd3c 100644 --- a/src/data/roadmaps/ai-engineer/content/tools--function-calling@eOqCBgBTKM8CmY3nsWjre.md +++ b/src/data/roadmaps/ai-engineer/content/tools--function-calling@eOqCBgBTKM8CmY3nsWjre.md @@ -4,7 +4,7 @@ Tools and function calling equip AI agents with the ability to interact with the Visit the following resources to learn more: -- [@article@A Comprehensive Guide to Function Calling in LLMs](https://thenewstack.io/a-comprehensive-guide-to-function-calling-in-llms/) -- [@article@What are Tools? - Hugging Face](https://huggingface.co/learn/agents-course/en/unit1/tools) +- [@course@A Comprehensive Guide to Function Calling in LLMs](https://thenewstack.io/a-comprehensive-guide-to-function-calling-in-llms/) +- [@official@What are Tools? - Hugging Face](https://huggingface.co/learn/agents-course/en/unit1/tools) - [@article@Compare 50+ AI Agent Tools in 2026](https://aimultiple.com/ai-agent-tools) - [@article@AI Agents Explained in Simple Terms for Beginners](https://www.geeky-gadgets.com/ai-agents-explained-for-beginners/) \ No newline at end of file diff --git a/src/data/roadmaps/ai-engineer/content/what-is-an-ai-engineer@GN6SnI7RXIeW8JeD-qORW.md b/src/data/roadmaps/ai-engineer/content/what-is-an-ai-engineer@GN6SnI7RXIeW8JeD-qORW.md index 4cb7875dc..b8652465c 100644 --- a/src/data/roadmaps/ai-engineer/content/what-is-an-ai-engineer@GN6SnI7RXIeW8JeD-qORW.md +++ b/src/data/roadmaps/ai-engineer/content/what-is-an-ai-engineer@GN6SnI7RXIeW8JeD-qORW.md @@ -5,4 +5,5 @@ AI engineers are professionals who specialize in designing, developing, and impl Visit the following resources to learn more: - [@article@How to Become an AI Engineer: Duties, Skills, and Salary](https://www.simplilearn.com/tutorials/artificial-intelligence-tutorial/how-to-become-an-ai-engineer) -- [@article@AI Engineers: What they do and how to become one](https://www.techtarget.com/whatis/feature/How-to-become-an-artificial-intelligence-engineer) \ No newline at end of file +- [@article@AI Engineers: What they do and how to become one](https://www.techtarget.com/whatis/feature/How-to-become-an-artificial-intelligence-engineer) +- [@article@I Transitioned from Data Science to AI Engineering: Here’s Everything You Need to Know](https://towardsdatascience.com/i-transitioned-from-data-science-to-ai-engineering-heres-everything-you-need-to-know/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration) \ No newline at end of file diff --git a/src/data/roadmaps/ai-engineer/content/zero-shot@15XOFdVp0IC-kLYPXUJWh.md b/src/data/roadmaps/ai-engineer/content/zero-shot@15XOFdVp0IC-kLYPXUJWh.md index a4db7f4a8..b661c2bb0 100644 --- a/src/data/roadmaps/ai-engineer/content/zero-shot@15XOFdVp0IC-kLYPXUJWh.md +++ b/src/data/roadmaps/ai-engineer/content/zero-shot@15XOFdVp0IC-kLYPXUJWh.md @@ -1,10 +1,9 @@ -# Zero-Shot Prompting +# Zero Shot Prompting -Zero-shot prompting is a prompt engineering method that relies on the pretraining of a large language model (LLM) to infer an appropriate response. In contrast to other prompt engineering methods, such as few-shot prompting, models aren’t provided with examples of output when prompting with the zero-shot technique.1 +Zero-shot prompting is a prompt engineering method that relies on the pretraining of a large language model (LLM) to infer an appropriate response. In contrast to other prompt engineering methods, such as few-shot prompting, models aren’t provided with examples of output when prompting with the zero-shot technique. Visit the following resources to learn more: - [@article@What is zero-shot prompting?](https://www.ibm.com/think/topics/zero-shot-prompting) - [@article@Zero-Shot Prompting](https://www.promptingguide.ai/techniques/zeroshot) -- [@article@Technique #3: Examples in Prompts: From Zero-Shot to Few-Shot](https://learnprompting.org/docs/basics/few_shot) - [@video@Zero-shot, One-shot and Few-shot Prompting Explained | Prompt Engineering 101](https://www.youtube.com/watch?v=sW5xoicq5TY) \ No newline at end of file