- Register for an OpenAI account and generate your API key. Store it securely (e.g., in a
.envfile). - Optionally, register with Gemini if planning to experiment with multimodal models.
- Verify basic access by making a simple “Hello world” request to the OpenAI API.
- Learn core guidance: write clear instructions, use reference text, decompose complex tasks, let the model “think,” integrate external tools, and iterate systematically (gocodeo.com, learnprompt.pro).
- Explore strategies like zero-shot, few-shot, chain-of-thought prompting, ReAct, RAG, and multimodal prompts (GitHub).
- Practice by crafting and testing different prompt styles, evaluating results, and refining.
- Design custom tools (functions) such as weather fetchers, database readers, etc., and integrate via OpenAI function-calling APIs (Technical Explore).
- Explore frameworks like LangChain or OpenAI Agents SDK to orchestrate conversations and tool use.
- Build a simple full-stack demo: e.g. a React UI + Express/Python backend that calls OpenAI.
- Integrate prompt-engineering and function-calling to create a working AI assistant.
- Study what MCP is: a JSON‑RPC-based open standard for giving context and tool access to LLMs (openai.github.io, learn.microsoft.com, en.wikipedia.org).
- Learn that MCP is adopted by Anthropic, Google (Gemini), OpenAI, Microsoft, and more (de.wikipedia.org).
- Understand security risks like prompt injection, tool spoofing, and permissions management (en.wikipedia.org).
- Try existing MCP servers from the official repos (e.g. GitHub “servers” directory) (en.wikipedia.org).
- Use OpenAI Agents SDK + MCP to consume those tools in an AI agent (dev.to).
- If inclined, build your own MCP server (e.g. GitHub access, database, local file). Explore community templates and reference server code (GitHub).
- Develop a UI that interacts with your agent + backend MCP server.
- Show how the agent retrieves context or executes tools in real-time (e.g. file content fetch, GitHub issues lookup).
- Learn to build agents using OpenAI’s Agents SDK and compare to MCP-based workflows (prompthub.us).
- Study common agent frameworks: AutoGPT, ReAct, CrewAI, etc. (AmanXai).
- Practice building a single agent with planning, tool use, and reasoning.
- Build multi-agent setups (e.g., coordinator agent, specialist agents), possibly using Agent-to-Agent (A2A) or Agent Communication Protocol (ACP) (arxiv.org).
- Learn how agents can outsource tasks, exchange capabilities, and coordinate workflows.
- Run or test A2A-compatible servers or frameworks.
- Integrate your multi-agent system and simulate collaboration, delegation, and negotiation.
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Design and build a full-fledged AI application that unifies:
- Multiple agents (via OpenAI Agents SDK or frameworks),
- Tool integrations and context via MCP servers,
- Agent-to-agent communication (A2A or ACP),
- Front-end visual interface.
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Emphasize ethical design, tool permissions, secure data access, and mitigation of safety risks in MCP systems (arxiv.org, en.wikipedia.org, businessinsider.com).
- Follow the OpenAI official Prompt Engineering guide for best practices (platform.openai.com).
- Explore MCP theory, roadmap, and community development plans (modelcontextprotocol.io, en.wikipedia.org).
- Watch industry commentary: how protocols (MCP, A2A) are shaping the future of autonomous AI ecosystems (businessinsider.com, reuters.com, axios.com).
- Take free or low-cost courses like those from Google, Microsoft, DeepLearning.AI for foundations in generative AI and prompt engineering (lifewire.com).
This roadmap integrates practical engineering steps, the latest standards (MCP, A2A), secure design, and hands-on projects to help you progress from beginner to advanced in generative AI development.