Useful resources curated from the internet and created by various good creators. I believe this should be sufficient to gain a strong hold on GenAI-related tasks, likely more than 85% of the time. While this field is changing rapidly, the fundamentals remain the same. Avoid getting distracted by the constant influx of new models and technologies. Focus on mastering GenAI fundamentals and start building projects alongside your learning. Implementing what you learn will solidify your understanding. Dedicate at least one hour every day to GenAI, and you will have a strong grasp of the tech stack within 1-2 months.
Learning GenAI is now essential as it integrates into all aspects of software engineering and will soon be a mandatory requirement for all positions. Be prepared—it's easier to learn than you might think.
PS: I planned to create a new playlist on my GenAI learning, where I am working on 7-8 projects, including 3 in production, but unavoidable personal and professional circumstances delayed this. I will try to record YouTube videos in the coming months to share insights from my 15+ months of experience in GenAI development.
This is a nice video to refresh your high-level overview of generative AI in brief. This is optional.
- Replace the OpenAI LLM used anywhere with the free Google’s Gemini Pro API-based LLM.
- How to setup Google's free Gemini Pro API Key
- Note: To access the Gemini Model via Google AI, you only need a Google account and an API key. A Google Cloud account is not required. It's easy and straightforward as shown in the above yt video.
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Useful LLM Concepts
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Some Cool examples (18 Min)
I prefer going through the LangChain documentation, which is well-written and includes example notebooks, as it updates very quickly. Referring to most of the LangChain YouTube videos might give you outdated content after a few weeks.
- Prompt Engineering Colab Notebook
- Course
- Blogs
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GitHub
- Deeplearning.ai Short Course - Agent
- Deeplearning.ai Autogen
- Prompt Guide - LLM Agents
- Architecting & Testing reliable Agent (Using LangGraph)
- Nvidia Blog
- Ttruefoundry Blog
- Agpt Blog
- Webscraping (Gemini Pro LLM & Streamlit UI)
Contributions to add good impactful resources/codes to the list are welcome!
Here’s how you can help:
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Fork the Repository
Click on the "Fork" button at the top right corner of the page to create a personal copy of the repository.
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Clone the Repository
Clone your forked repository to your local machine:
git clone https://github.com/genieincodebottle/generative-ai.git
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Create a New Branch
Create a new branch for your feature or bug fix:
git checkout -b your-branch-name
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Make Your Changes
Make your changes and commit them with a clear message:
git commit -m "Brief description of your changes" -
Push Your Changes Push your changes to your forked repository:
git push origin your-branch-name
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Create a Pull Request
Go to the original repository and create a pull request. Make sure to explain your changes and why they should be merged.
- 31 May 2024 - Added Multimodel doc
- 01 Jun 2024
- Added resources related LLM Evaluation
- Added resources related LLMOPs
- 11 Jun 2024
- Added GenAI & LLM Essential Terms
- 07 Jul 2024
- Added LLM Ledaerboard links & benchmarks
- 16 Jul 2024
- Added resource related Graph RAG
- Updated Interview pdf with Advance Topics
- 19 Jul 2024
- Added resource related GenAI Coding round preparation
- 31 Jul 2024
- Added Conversational Analytics project repo link
- 01 Aug 2024
- Organised the doc and some formatting