# Course Syllabus and Timelines By the end of this course, you will: * Be proficient in developing LLM-based applications for production applications from day 0. * Have a clear understanding of LLM architecture and pipeline. * Be able to perform prompt engineering to use generative AI tools such as ChatGPT. * Create an open-source project on a real-time stream of data or static data. ## Module Release Timelines and the Coursework:

Module	Topics
1 – Basics of LLMs	What is generative AI and how it's different Understanding LLMs Advantages and Common Industry Applications Bonus section: Google Gemini and Multimodal LLMs --- When to learn: 21 August
2 – Word Vectors	What are word vectors and word-vector relationships? Role of context Transforming vectors in LLM responses Overview of Transformers Architecture Bonus Resource: Transformers Architecture, Self-attention, Multi-head attention, and Vision Transformers Bonus Resource: Talk on Future of LLMs by the Co-Creator of ChatGPT, Łukasz Kaiser --- When to learn: 21-25 Aug Quiz Releasing on: 25 Aug (Revised)
3 – Prompt Engineering	Introduction and in-context learning Best practices to follow: Few Shot Prompting and more Token Limits Prompt Engineering Exercise (Ungraded) -- When to learn: 25-27 Aug
Refresher Module	Overview of learnings so far sent over registered email address. Release of bootcamp keynote session(s). -- Will be sent via email to registered email IDs on 28th August <Link to register>
4 – RAG and LLM Architecture	Introduction to RAG LLM Architecture Used by Enterprises RAG vs Fine-Tuning and Prompt Engineering Key Benefits of RAG for Realtime Applications Bonus: Similarity Search for Efficient Information Retrieval Bonus: Use of LSH + kNN and Incremental Indexing Bonus: Forgetting in LLMs and Stream Data Processing (archived live interactions) -- When to learn: 28-31 Aug Quiz Releasing on: 28 Aug
5 – Hands-on Development of Realtime LLM ApplicationsQu	Installing Dependencies and Pre-requisites Building a Dropbox RAG App using open-source Building Realtime Discounted Products Fetcher for Amazon Users Building RAG applications with local models Leveraging Pathway with LlamaIndex/Langchain (Bonus) Problem Statements for Projects Project Submission -- When to learn: 1-5 Sept
6 – Project Development: Tracks and Submission	Problem Statements Release for the Projects Window for Sharing/Reviewing Project Ideas via Discord Channel Online Office Hours Projects Submission Project Feedback (after the submissions deadline) -- Module Yet to Be Released. Releasing 5th Sept

### Note: * Once the problem statements and the hands-on development module are released on the 5th September 2024, the project submissions will remain open till the **11th September 2024, 11:59 pm CEST.** * There'll be weekly sessions to help you cover the curriculum. They'll be scheduled on Thursdays. If there's a dedicated live session beyond this, then you'll be notified beforehand. ### What are Bonus Sections/Resources? Throughout the bootcamp, you'll see some modules or links labeled as bonus resources. These are **not compulsory** for building a project by the end of the bootcamp or attempting the quizzes. Nonetheless, they are relevant resources that could enhance your understanding, although they might require additional prerequisites. Depending on **your starting point** and the pace you're progressing through the bootcamp, you can explore or park these bonus materials. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://dsg-iit-roorkee.gitbook.io/dsg-iit-roorkee-bootcamp/start-here-welcome-to-the-bootcamp/course-syllabus-and-timelines.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.