Gen AI Developer Roadmap

#genai #rag #llm #mcp

Gen AI Developer Roadmap: Week-wise Syllabus 🗓️

This syllabus is designed for developers with existing full-stack skills. The pace can be adjusted based on individual learning speed and prior experience.

Week 1: Gen AI Foundations & First Chatbot 🚀

Concepts:
- What is Generative AI?
- Understanding LLMs (Large Language Models).
- Introduction to RAG (Retrieval Augmented Generation) systems.
- OpenAI APIs and Hugging Face overview.
- Understanding GPT models.
Tools & Setup:
- Python (or TypeScript if preferred).
- Jupyter Notebooks, VS Code.
Project: Build a simple Command-Line Interface (CLI) Chatbot using OpenAI's chat completions API. Focus on understanding basic API interaction and the role of system prompts.

Week 2: Prompt Engineering & Token Management 💬

Concepts:
- System Prompting: Designing effective system prompts for specialized chatbots.
- Prompt Engineering Techniques:
  - Zero-shot prompting
  - Few-shot prompting
  - Chain-of-thought prompting
- Token Management: Understanding token input/output and its impact on cost.
- LLM Parameters: Exploring parameters like temperature, top tokens, and max length to control output.
Project: Create an Email Generator application that utilizes prompt templates and roles to generate email content.

Week 3: Introduction to LangChain & Context Management 🔗

Concepts:
- LangChain: Dive into this library for building LLM applications, including its chaining, agent, memory, and prompt template tools.
- Context Window Limitations: Understanding challenges when dealing with large contexts and token limits.
- Chunking: Strategies for breaking down large documents into smaller, manageable chunks.
- Vector Embeddings: Basics of how text is converted into numerical vectors.
- Querying: How to query these vector embeddings.
Project: Start building an AI-powered PDF Q&A Bot. This is the initial step towards a RAG application, focusing on basic document processing and querying.

Week 4: Deep Dive into RAG Systems 📚

Concepts:
- Retrieval Augmented Generation (RAG): Techniques to efficiently build RAG systems from scratch.
- Vector Stores: Working with vector databases like ChromaDB and PineconeDB.
- Cosine Similarity: Understanding how vector similarity is calculated.
- Advanced Chunking and Indexing: Optimizing these for better retrieval.
Projects:
- Continue developing the AI-powered PDF Q&A Bot, refining its RAG capabilities.
- Build a Resume Analyzer Bot that can process resumes and answer questions, potentially integrating knowledge graphs for enhanced querying.

Week 5: Advanced RAG & Tooling 🛠️

Concepts:
- React Agents: Understanding this agent paradigm (distinct from ReactJS) for LLMs to interact with tools.
- Tool Binding: How to effectively connect external tools to LLMs.
- Explore common pre-built tools (Serp API, Calculator, Web Search, Doc splitting, Web scraping, Weather).
- Ability to create custom tools for specific use cases.
Project: Develop an AI Travel Planner that uses external APIs (like weather or booking APIs) integrated with LLMs for NLP. This project will solidify your understanding of integrating external tools.

Week 6: Multi-Agent Systems & Orchestration with LangGraph 🌐

Concepts:
- Multi-Agent Systems: Designing systems where different LLMs (e.g., OpenAI, Claude, Gemini), each with specific strengths (coding, math, reasoning, cost-efficiency), collaborate.
- LangGraph: Learn to use LangGraph for orchestrating complex, graph-based reasoning workflows between multiple agents and models.
- Observability & Monitoring: Importance of monitoring and debugging complex LLM applications and graphs.
Project: Implement a Multi-Agent System for a complex task (e.g., a research assistant that uses different agents for information retrieval, summarization, and synthesis).

Week 7: Deployment & Web App Integration 🚀

Concepts:
- API Deployment: Exposing LLM application endpoints to the frontend.
- FastAPI: Using this framework for building scalable and efficient backend servers.
- Docker: Containerizing your Gen AI applications for consistent deployment.
- API Routing, Authentication, JSON Input/Output.
- Frontend Integration: Connecting the Gen AI backend with a web application.
Project: Build an AI Code Reviewer application. This involves deploying a Gen AI model as an API and integrating it into a workflow (e.g., a pull request review system).

Week 8: Model Context Protocol (MCP) & Advanced Optimizations ⚙️

Concepts:
- Model Context Protocol (MCP): Understanding this standardized approach to providing context to LLMs.
- Building MCP Servers and Clients for standardized tool discovery and invocation across different models and platforms.
- Deployment Optimizations: Implementing rate limiting and various caching strategies (prompt caching, response caching).
- Logging & Tracing: Using tools like LangSmith and OpenTelemetry for enhanced debugging and performance tracking.
Project: Implement an MCP-compliant system for a tool or data source, demonstrating standardized context sharing. Also, optimize an existing project with caching and rate limiting.

Week 9: Full-Stack Gen AI Projects & Open-Source LLMs 💡

Concepts:
- Full-Stack Gen AI Project Design: Applying all learned skills to create end-to-end applications.
- Fine-tuning vs. RAG: A deeper understanding of when to choose one over the other based on project requirements.
- Open-Source LLMs: Running models like Llama, Mistral locally using tools like Ollama.
- Local Vector Databases & Embedding Models: Utilizing these for specific use cases.
- Hugging Face Transformers: Advanced usage for tokenization and de-tokenization.
Projects:
- Develop a Full-Stack AI Feedback Project (e.g., an application for collecting, analyzing, and acting on user feedback with AI).
- Experiment with Open-Source LLMs for a specific task, comparing their performance and cost-effectiveness against proprietary models.

Week 10: Cost Optimization & Paradigm Shift 🧠

Concepts:
- Cost Optimization Techniques:
  - Token counting
  - Token streaming
  - Prompt caching
- Paradigm Shift: Understanding the fundamental difference in working with AI (unpredictable outputs, experimental approach) compared to deterministic traditional software development.
- Autonomous vs. Controlled Workflows: Designing and managing AI environments with different levels of autonomy.
Project: Optimize an existing Gen AI project for cost efficiency, incorporating various token and caching strategies. Reflect on the shift in mindset required for building robust AI-powered software.