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Most people even today don't know these skills. I've explained in very simple below. 1. 𝗣𝗿𝗼𝗺𝗽𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 Learning how to give clear, structured, and goal-driven instructions to AI models to get accurate and useful outputs. Eg: ChatGPT, Claude, Gemini, PromptPerfect 2. 𝗔𝗜 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 Building systems where AI connects with tools, apps, and databases to automate repetitive tasks and business processes. Eg: Make, Zapier, n8n 3. 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 & 𝗔𝗴𝗲𝗻𝘁 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 Designing AI systems that can make decisions, use tools, and complete multi-step tasks independently. Eg: LangChain, CrewAI, AutoGen 4. 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹-𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 (𝗥𝗔𝗚) Allowing AI to access and use external or private data like documents, PDFs, or company knowledge for accurate responses. Eg: LlamaIndex, LangChain, Pinecone 5. 𝗠𝘂𝗹𝘁𝗶𝗺𝗼𝗱𝗮𝗹 𝗔𝗜 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 Building AI that can understand and generate text, images, audio, and video instead of just text-based interactions. Eg: OpenAI GPT Models, Claude, Gemini 6. 𝗙𝗶𝗻𝗲-𝗧𝘂𝗻𝗶𝗻𝗴 & 𝗖𝘂𝘀𝘁𝗼𝗺 𝗔𝗜 𝗔𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝘁𝘀 Training or adapting models to specialize in specific industries, workflows, or business needs. Eg: Hugging Face, OpenAI Fine-Tuning, LoRA 7. 𝗟𝗟𝗠 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 & 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 Testing AI outputs, tracking performance, and ensuring reliability, accuracy, and consistency across systems. Eg: TruLens, Helicone, PromptLayer 8. 𝗔𝗜 𝗧𝗼𝗼𝗹 𝗦𝘁𝗮𝗰𝗸𝗶𝗻𝗴 & 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻𝘀 Combining multiple AI tools, APIs, and software systems to build complete AI-driven solutions. Eg: Notion, Zapier, Asana 9. 𝗦𝗮𝗮𝗦 𝗔𝗜 𝗔𝗽𝗽 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 Creating scalable AI-powered applications with user authentication, billing systems, dashboards, and deployment infrastructure. Eg: Bolt Supabase, Lovable etc. 10. 𝗠𝗼𝗱𝗲𝗹 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗣𝗿𝗼𝘁𝗼𝗰𝗼𝗹 (𝗠𝗖𝗣) Managing memory, conversation context, and tool interactions so AI systems stay accurate and relevant over time. Eg: LangChain, Github MCP etc. 11. 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗔𝗜 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 & 𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 Building AI systems that break complex problems into steps, plan execution, and improve decisions independently. Eg: BabyAGI, LangChain, AutoGPT 12. 𝗔𝗣𝗜 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝘄𝗶𝘁𝗵 𝗟𝗟𝗠𝘀 Using AI models programmatically inside applications, products, and backend systems instead of only using chat interfaces. Eg: SerpApi, LangChain, Flowise 13. 𝗖𝘂𝘀𝘁𝗼𝗺 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀 & 𝗩𝗲𝗰𝘁𝗼𝗿 𝗦𝗲𝗮𝗿𝗰𝗵 Creating semantic search systems that allow AI to understand meaning and retrieve the most relevant information. Eg: Weaviate, Pinecone, OpenAI Embeddings 14. 𝗔𝗜 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 & 𝗦𝗮𝗳𝗲𝘁𝘆 Implementing guardrails, compliance systems, and ethical frameworks to ensure AI behaves safely and responsibly. Eg: Guardrails AI, Rebuff AI, Lakera AI ✅ Repost for others so they can learn these AI skills.