GenAI-Intermediate-to-Advanced-Roadmap

A concise 8–10 week plan to elevate your Generative AI skills—covering OpenAI APIs, RAG, agent frameworks, and advanced reasoning—through hands‑on mini‑projects and curated resources.

Roadmap Link : https://chatgpt.com/s/t_687cb1383cf0819190bcf134f9d4a879

Generative AI Intermediate‑to‑Advanced Roadmap

An 8–10 week guided self‑study plan to take your Generative AI skills from intermediate to advanced. Each week pairs clear study goals, core topics, hands‑on project ideas, and curated resources (free & paid) so you can build portfolio‑worthy applications and stay on the cutting edge.

🗓️ Week‑by‑Week Roadmap

Week 1: API Deep Dive

Study Goals:

• Master OpenAI text (Chat Completion), image (DALL·E), and audio (Whisper) endpoints.
• Explore Google Gemini via Vertex AI and Anthropic Claude APIs.
• Brainstorm 2–3 multimodal use cases combining text, image, and voice.

Topics Covered:

• OpenAI Playground vs. programmatic API calls
• Key parameters: temperature, max_tokens, system/user roles
• Crafting prompts for text generation and image synthesis
• Whisper usage for speech‑to‑text workflows
• Accessing and experimenting with Gemini and Claude interfaces

Project Ideas (5–8 hrs):

1. Audio Summarizer: Transcribe a podcast with Whisper, then generate bullet‑point summaries via GPT-4.
2. Image‑Driven Storyteller: Generate images with DALL·E, then have GPT write short stories inspired by them.
3. Multimodal Chatbot: Accept voice questions (Whisper), answer via GPT, and display relevant images.

Resources:

• OpenAI API Quickstart
• freeCodeCamp’s “OpenAI API Crash Course” (YouTube)
• Google Cloud Vertex AI Gemini tutorial
• Anthropic Claude API docs

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Week 2: Structured Outputs & Tool Calls

Study Goals:

• Use OpenAI’s function‐calling feature to let LLMs invoke external tools.
• Enforce JSON/schema output for reliable data extraction.
• Plan five complex API‑driven mini‑projects suitable for your portfolio.

Topics Covered:

• Defining functions with name/description/parameters in OpenAI API
• Prompting for strict JSON responses (structured_output mode)
• Few‑shot examples vs. function definitions
• Handling parse errors and re‑prompting strategies

Project Ideas (6–10 hrs):

1. Meeting Scheduler: Parse user input into calendar event JSON, call a mock calendar API.
2. Finance Analyzer: Extract and categorize expenses from natural‑language descriptions into JSON.
3. News & Weather Bot: Use get_news() and get_weather() functions dynamically.
4. Error‑Debugging Assistant: Run code snippets, capture errors via function calls, and return diagnostics.
5. Product Price Tracker: Call e‑commerce APIs to fetch product prices and output structured price history.

Resources:

• OpenAI Blog: Structured Outputs
• OpenAI Cookbook: Function Calling
• DataCamp tutorial: “Generate Structured JSON with OpenAI”
• Isaac Fulford’s “Prompt Engineering” (DeepLearning.AI short course)

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Week 3: Advanced RAG & Vector Databases

Study Goals:

• Build a full RAG pipeline to ground LLMs in external documents.
• Experiment with vector stores (Chroma, Pinecone, Weaviate, FAISS).
• Implement a basic hybrid keyword + semantic search Q&A bot.

Topics Covered:

• Document ingestion & chunking strategies
• Embedding generation (OpenAI embeddings)
• Indexing & similarity search in vector DBs
• Hybrid search: combining BM25 + vector results
• Re‑ranking retrieved passages, prompt template design

Project Idea (8–12 hrs):

• PDF Q&A Bot: Ingest a collection of PDFs, index in Pinecone, then answer user questions by retrieving top‑k passages and prompting GPT-4.

Resources:

• DeepLearning.AI RAG Course
• LangChain RAG tutorial
• LlamaIndex documentation on Retrieval QA
• Pinecone & Weaviate quickstart guides

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Week 4: Integrated Generative App

Study Goals:

• Combine APIs, RAG, and function calls into a single cohesive application.
• Design system architecture, handle edge cases, and document your solution.

Topics Covered:

• Orchestrating multiple API calls and retrieval steps
• Error handling (invalid JSON, missing data)
• Prompt chaining and state management
• User interface layer (CLI, minimal web UI with Gradio)

Project Ideas (10–15 hrs):

• Research Assistant: Upload documents, ask questions, and get answers with citations.
• Trip Planner: Input trip details, call flight/hotel APIs (mocked), and generate itinerary PDF.

Resources:

• OpenAI Cookbook: “Chaining API Calls”
• Gradio tutorial for quick web app UIs
• Example repos: “Chat with PDF” on Hugging Face Spaces

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Week 5: Agent Frameworks Survey

Study Goals:

• Understand LLM‑based agents and compare four leading frameworks.
• Map out when to use each framework and its orchestration model.

Topics Covered:

• Agent definitions: observe, reason, act
• LangChain Agents & LangGraph (workflow graphs)
• Google ADK (hierarchical sub‑agents)
• CrewAI (role‑based “crews”)
• Microsoft AutoGen (multi‑agent conversation patterns)

Resources:

• LangGraph GitHub & tutorial
• Google ADK blog post & sample code
• CrewAI documentation & example flows
• Microsoft AutoGen repo & commander‑solver example

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Week 6: Multi‑Agent Use Cases

Study Goals:

• Study collaboration patterns (master–worker, peer debate, sequential flows).
• Design five real‑world scenarios leveraging multi‑agent systems.

Example Use Cases:

1. Coder & Reviewer: Agent A writes code; Agent B reviews and proposes fixes.
2. Research Duo: Researcher agent gathers facts; Analyst agent synthesizes a report.
3. Support Team: Problem‑solving agent + policy‑enforcer agent for customer queries.
4. Creative Suite: Plot agent, character agent, editor agent co‑author a story.
5. Personal Planner: Manager agent delegates tasks to booking/call‑scheduler agents.

Resources:

• CrewAI “Planner/Writer/Editor” example
• Google ADK Weather & Greeting agents
• AutoGen commander‑solver demo
• Academic “Chain‑of‑Agents” paper (overview)

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Week 7: Single‑Agent Project

Study Goals:

• Build an autonomous agent that reasons and calls tools end‑to‑end.
• Implement tool discovery, error recovery, and session state.

Project Ideas (8–12 hrs):

• Smart Data Analyst: Agent queries a data API, generates plots, and summarizes insights.
• Web Researcher: Agent uses search_web() and scrape_page() tools to answer open‑ended queries.

Resources:

• OpenAI Cookbook: “Functions + Reasoning Models”
• LangChain ReAct agent example
• DeepLearning.AI: “Agentic RAG with LlamaIndex”

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Week 8: Multi‑Agent Implementation

Study Goals:

• Develop a working system of 2–3 agents interacting to complete a complex workflow.
• Choose and configure a framework to manage agent communication and memory.

Project Ideas (12–20 hrs):

• AI Pair Programmer: Coder agent + reviewer agent iterate until code passes tests.
• Consulting Team: Research agent + synthesizer agent answer business strategy questions.

Resources:

• AutoGen multi‑agent guides
• CrewAI flows for sequential tasks
• LangChain multi‑agent patterns

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Week 9: Advanced Reasoning Techniques

Study Goals:

• Explore Test‑Time Compute, Chain‑of‑Thought variants (tree/graph‑of‑thought, self‑consistency).
• Experiment with reasoning‑optimized models (GPT‑4o, Claude Sonnet).

Topics Covered:

• Enhancing performance with multiple reasoning passes
• Self‑consistency voting and ensemble of thought chains
• Tree‑of‑Thought search algorithms
• Reasoning‑mode models and extended context

Resources:

• Geodesic Capital Blog: “Test‑Time Compute”
• Two Minute Papers on CoT & tree‑of‑thought
• ArXiv: “Optimizing Test‑Time Compute”
• OpenAI & Anthropic system/model cards

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Week 10: Latest Platforms & Future Trends

Study Goals:

• Hands‑on with ChatGPT Agent (auto‑tooling, web browsing, code execution).
• Explore Claude Code SDK for deep IDE integration.
• Survey large‑context models, multimodal advances, regulation, and open‑source movements.

Topics Covered:

• ChatGPT Agent features & safety model
• Claude Code for automated code tasks
• 100k+ token context use cases
• Emerging AI policies (EU AI Act) and open models (LLaMA‑3, etc.)
• Ethical considerations for autonomous AI

Resources:

• OpenAI DevDay: ChatGPT Agent announcement
• Anthropic blog: Claude Code deep dive
• Meta & Google research on long‑context models
• Industry news on AI regulation & open‑source releases

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🤝 Contributing

We welcome contributions:

• Add or update weekly guides and resources
• Report typos or suggest improvements via Issues
• Submit Pull Requests with enhancements

Please see CONTRIBUTING.md for details.

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📝 License

This project is licensed under the MIT License. See LICENSE.md for full text.

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Ready to level up your Generative AI expertise? Start with Week 1 and build something amazing! 🚀

Build and orchestrate 2–3 agents working together on a complex workflow.

Advanced Reasoning Explore test‑time compute, chain‑of‑thought variants, and reasoning‑optimized models.

Latest Trends Hands‑on with ChatGPT Agent and Claude Code SDK; survey large context, multimodal advances, and ethics.