Here is what the Journey Looks like:
By Sivasankar Natarajan
𝐇𝐞𝐫𝐞 𝐢𝐬 𝐰𝐡𝐚𝐭 𝐭𝐡𝐚𝐭 𝐣𝐨𝐮𝐫𝐧𝐞𝐲 𝐥𝐨𝐨𝐤𝐬 𝐥𝐢𝐤𝐞:
𝟏. 𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧𝐬
Understand how LLMs work, what they can and can’t do, and how prompting drives their behavior.
𝟐. 𝐏𝐫𝐨𝐦𝐩𝐭 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠
Learn to craft prompts that define context, roles, and outcomes the skill every AI professional needs.
𝟑. 𝐔𝐬𝐞 𝐂𝐚𝐬𝐞𝐬
Apply AI to solve real problems: content creation, research, summarization, automation, and more.
𝟒. 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐅𝐞𝐚𝐭𝐮𝐫𝐞𝐬
Work with plugins, APIs, and multi-modal data. Build custom GPTs and extend model capabilities.
𝟓. 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬
Use AI to boost productivity, generate ideas, support customers, and transform workflows.
𝟔. 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦 𝐁𝐚𝐬𝐢𝐜𝐬
Set up automations with no-code tools and triggers. Understand the building blocks of AI workflows.
𝟕. 𝐀𝐩𝐩 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧𝐬
Connect CRMs, databases, and tools like Slack, Sheets, or Gmail into your AI systems.
𝟖. 𝐌𝐮𝐥𝐭𝐢-𝐒𝐭𝐞𝐩 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬
Automate complex business processes by chaining tasks and decisions together.
𝟗. 𝐄𝐫𝐫𝐨𝐫 𝐇𝐚𝐧𝐝𝐥𝐢𝐧𝐠 & 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧
Debug workflows, monitor performance, and ensure reliability at scale.
𝟏𝟎. 𝐀𝐈 𝐄𝐧𝐡𝐚𝐧𝐜𝐞𝐦𝐞𝐧𝐭𝐬
Incorporate LLMs into existing workflows to make them smarter and more adaptive.
𝟏𝟏. 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤 𝐁𝐚𝐬𝐢𝐜𝐬
Learn how to coordinate multiple agents and integrate external tools effectively.
𝟏𝟐. 𝐌𝐞𝐦𝐨𝐫𝐲 & 𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥
Use vector databases to store and recall context enabling agents to “remember” and improve over time.
𝟏𝟑. 𝐌𝐮𝐥𝐭𝐢-𝐒𝐭𝐞𝐩 𝐑𝐞𝐚𝐬𝐨𝐧𝐢𝐧𝐠
Design agents that think step-by-step, plan ahead, and break tasks into logical actions.
𝟏𝟒. 𝐌𝐮𝐥𝐭𝐢-𝐀𝐠𝐞𝐧𝐭 𝐂𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐨𝐧
Enable agents to share tasks, communicate, and solve problems together just like human teams.
𝟏𝟓. 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐎𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧
Design, deploy, and scale entire AI systems. Build solutions that act autonomously and deliver real business value.