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  "path": "/take-genai-drive/",
  "publishedAt": "2026-05-28T19:10:49.000Z",
  "site": "https://www.jjude.com",
  "tags": [
    "Twitter",
    "conversation",
    "Joseph Jude"
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  "textContent": "We have seen multiple technology waves over the last 30 years:\n\n  * the Internet,\n  * mobile,\n  * cloud,\n  * social media,\n  * e-commerce,\n  * and data platforms.\n\n\n\nMost of those technologies required specialists before ordinary users could meaningfully benefit from them.\n\nGenerative AI is different.\n\nFor perhaps the first time in technology history, the technology itself can teach you how to use it.\n\nYou can literally ask:\n\n  * “What can you do for me?”\n  * “How should I use you?”\n  * “What are your limitations?”\n  * “Help me learn prompting.”\n\n\n\nAnd the technology responds directly to the user.\n\nThat changes everything.\n\nThis blog is a practical guide based on a recent session with CAs, investors, and professionals on how to approach GenAI without hype, fear, or unnecessary technical jargon.\n\n## # The Biggest Mistake People Make With AI\n\nMost people approach AI in one of two wrong ways:\n\n### # Mistake 1 — Waiting to Understand Everything\n\nPeople feel they must first understand:\n\n  * neural networks,\n  * transformers,\n  * LLMs,\n  * embeddings,\n  * vector databases,\n  * GPU architectures.\n\n\n\nbefore they can start using AI.\n\nThat is unnecessary for most professionals.\n\nYou did not learn:\n\n  * internal combustion engineering before driving a car,\n  * TCP/IP before using the internet,\n  * or semiconductor physics before using a smartphone.\n\n\n\nThe same principle applies here.\n\n### # Mistake 2 — Trying It Once and Giving Up\n\nMany people:\n\n  * ask one vague question,\n  * get a generic answer,\n  * conclude “AI is overrated,”\n  * and stop experimenting.\n\n\n\nGenAI is not a search engine.\n\nIt behaves more like:\n\n  * a collaborator,\n  * an assistant,\n  * or a junior analyst.\n\n\n\nThe quality of output depends heavily on:\n\n  * the clarity of your thinking,\n  * the quality of your context,\n  * and iterative refinement.\n\n\n\n## # The Driver → Mechanic → Assembler Framework\n\nThis is the mindset I have personally used across multiple technology waves over the last 30 years.\n\nIt applies surprisingly well to GenAI.\n\n### # Step 1 — Start as a Driver\n\nMost people should begin here.\n\nThe goal is simple:\n\n> use the technology before deeply understanding the internals.\n\nThink about learning to drive a car.\n\nNobody starts by:\n\n  * opening the hood,\n  * studying engine mechanics,\n  * understanding fuel injection systems,\n  * or analyzing gearbox engineering.\n\n\n\nYou first learn:\n\n  * steering,\n  * acceleration,\n  * braking,\n  * parking,\n  * and road awareness.\n\n\n\nGenAI should be approached the same way.\n\nStart using it.\n\nExperiment with it.\n\nDrive it.\n\n#### # Practical Advice\n\nUse AI in areas such as:\n\n  * work,\n  * investing,\n  * writing,\n  * communication,\n  * summarization,\n  * learning,\n  * planning,\n  * brainstorming,\n  * and analysis.\n\n\n\nDo not overthink the technology initially.\n\nFocus on:\n\n  * capability,\n  * usefulness,\n  * and workflow.\n\n\n\n### # Step 2 — Drive in Different Contexts\n\nThis is where real learning begins.\n\nDo not test AI only in one narrow scenario.\n\nUse it:\n\n  * professionally,\n  * personally,\n  * analytically,\n  * creatively,\n  * in familiar domains,\n  * and unfamiliar domains.\n\n\n\n#### # Most Important Advice\n\nTest AI especially in areas where you already have expertise.\n\nWhy?\n\nBecause experts can:\n\n  * detect hallucinations,\n  * identify weak reasoning,\n  * understand nuance,\n  * and evaluate quality.\n\n\n\nThis helps you understand:\n\n  * where AI works extremely well,\n  * where it struggles,\n  * and where human judgment is still essential.\n\n\n\n## # Example Contexts to Explore\n\n### # For CAs\n\n  * explaining tax changes,\n  * drafting client communication,\n  * summarizing regulations,\n  * extracting insights from PDFs,\n  * comparing financial statements.\n\n\n\n### # For Investors\n\n  * company analysis,\n  * summarizing annual reports,\n  * identifying business risks,\n  * generating investment questions,\n  * comparing management commentary.\n\n\n\n### # For Professionals\n\n  * meeting summaries,\n  * first drafts,\n  * brainstorming,\n  * presentations,\n  * SOP generation,\n  * workflow acceleration.\n\n\n\n## # Step 3 — Become a Mechanic\n\nOnce comfortable using AI, you begin customizing it.\n\nThis is where concepts like:\n\n  * prompts,\n  * context,\n  * memory,\n  * and workflows\n\n\n\nbecome important.\n\nYou are no longer just driving.\n\nYou are tuning the vehicle.\n\n### # The Three Important Controls of GenAI\n\nThink of these as the steering wheel, brakes, and dashboard of AI systems.\n\n### # 1. Prompt\n\nA prompt is the instruction or direction you give AI.\n\nBad prompt:\n\n> “How should I invest 25 lakhs?”\n\nThis is too generic.\n\nBetter prompt:\n\n> “I am a long-term Indian retail investor with moderate risk appetite and existing real estate exposure. How should I think about diversification across equity, debt, and gold?”\n\nNotice the difference.\n\nThe second prompt contains:\n\n  * goals,\n  * constraints,\n  * context,\n  * and intent.\n\n\n\nThe result improves dramatically.\n\n#### # Important Insight\n\nPrompting is not about “magic words.”\n\nIt is about:\n\n  * clarity of thinking,\n  * structured communication,\n  * and iterative refinement.\n\n\n\nGood prompts often reflect good thinking.\n\n### # 2. Context\n\nContext defines the environment within which AI should operate.\n\nWithout context:\n\n  * AI gives generic answers.\n\n\n\nWith context:\n\n  * AI gives personalized and relevant responses.\n\n\n\nExamples of context:\n\n  * profession,\n  * geography,\n  * risk appetite,\n  * business model,\n  * time horizon,\n  * communication style,\n  * audience type.\n\n\n\n#### # Example\n\nInstead of:\n\n> “Analyze ITC.”\n\nTry:\n\n> “Analyze ITC from the perspective of a long-term retail investor focused on debt, promoter quality, and cash flow consistency.”\n\nThe difference is enormous.\n\n### # 3. Memory\n\nModern GenAI systems increasingly remember:\n\n  * preferences,\n  * recurring patterns,\n  * writing styles,\n  * and long-term interactions.\n\n\n\nOver time, AI becomes more personalized.\n\nThis is where the system starts feeling less like:\n\n  * a search engine,\n\n\n\nand more like:\n\n  * a collaborative assistant.\n\n\n\n## # Different AI Tools and Their Best Usage\n\nNot all AI systems are optimized for the same tasks.\n\nTool | Best For\n---|---\nChatGPT | reasoning, ideation, writing\nClaude | long documents and analysis\nGemini | Google ecosystem integration\nPerplexity | research with citations\nCopilot | productivity workflows\nMidjourney | image generation\n\nOne important lesson:\n\n> there is no single “best AI.”\n\nDifferent tools work better for different use cases.\n\n## # What AI Is Surprisingly Good At\n\nGenAI is already extremely useful for:\n\n  * summarization,\n  * communication,\n  * brainstorming,\n  * simplifying complexity,\n  * comparing documents,\n  * extracting insights,\n  * generating first drafts,\n  * structuring thoughts,\n  * and accelerating workflows.\n\n\n\n## # What AI Is Still Bad At\n\nThis is equally important.\n\nAI still struggles with:\n\n  * factual consistency,\n  * legal accountability,\n  * nuanced financial judgment,\n  * deep strategic reasoning,\n  * and reliability across long workflows.\n\n\n\nMost importantly:\n\n> AI sounds confident even when it is wrong.\n\nThat means:\n\n  * verification remains essential,\n  * human judgment still matters,\n  * and blind trust is dangerous.\n\n\n\n## # Demo Section\n\n### # Demo 1 — Prompt Evolution\n\n#### # Starting Prompt\n\n> “How should I invest 25 lakhs?”\n\n#### # Improved Prompt\n\n(Add goals, constraints, time horizon, and risk profile)\n\n#### # Advanced Prompt\n\n(Add portfolio exposure, taxation considerations, and diversification requirements)\n\n#### # Key Learning\n\nBetter prompts produce better outcomes.\n\n### # Demo 2 — Company Analysis\n\nExample workflow:\n\n  * analyze annual report,\n  * summarize risks,\n  * identify red flags,\n  * compare management commentary,\n  * extract financial insights.\n\n\n\n## # Demo 3 — AI Memory and Personalization\n\nExample:\n\n  * asking AI what it understands about the user,\n  * checking whether the context is accurate,\n  * refining memory over time.\n\n\n\nKey insight:\n\n> Personalized AI becomes significantly more useful.\n\n## # Demo 4 — Failure Demonstration\n\nExamples:\n\n  * hallucinated data,\n  * incorrect citations,\n  * fabricated numbers,\n  * overconfident answers.\n\n\n\nThis is one of the most important things to understand about GenAI.\n\n### # Key Lesson\n\nUse AI:\n\n  * as an accelerator,\n  * as a thinking partner,\n  * as a workflow assistant,\n\n\n\nbut not:\n\n  * as an unquestionable authority.\n\n\n\n## # What Comes Next: AI Agents\n\nToday:\n\n  * AI mostly answers questions.\n\n\n\nTomorrow:\n\n  * AI will increasingly execute workflows.\n\n\n\nExamples:\n\n  * monitoring portfolios,\n  * drafting recurring reports,\n  * tracking compliance,\n  * reconciling invoices,\n  * generating research summaries,\n  * automating repetitive tasks.\n\n\n\nThis is where the industry is heading rapidly.\n\n## # Final Thoughts\n\nThe future is not about:\n\n  * humans versus AI.\n\n\n\nIt is about:\n\n  * humans working effectively with AI.\n\n\n\nThe professionals who benefit the most will likely be those who:\n\n  * experiment actively,\n  * learn continuously,\n  * verify critically,\n  * and combine domain expertise with AI leverage.\n\n\n\nDo not fear the technology.\n\nDo not blindly trust it either.\n\nTake it for a drive.\n\nGot comments? Send them to me via Twitter or join the conversation.\n\n**'Taking GenAI for a Drive'** appeared on Joseph Jude.",
  "title": "Taking GenAI for a Drive"
}