Lesson · Hinglish · ⏱ 7 min padhne mein

🎯 Problem framing: kya AI ko yeh solve karna chahiye, aur kaise?

🏠 Asli zindagi ki misaal

Patient clinic mein aakar kehta hai 'mujhe antibiotics de do'. Achha doctor pehle poochta hai: kya symptoms hain, kab se hain, kya try kiya. Aksar asli zaroorat request se alag hoti hai. Jab client kehta hai 'humein AI chatbot chahiye', to achha AI professional dawai likhne se pehle diagnose karta hai.

💡 Aasaan shabdon mein

Technology se nahi, problem se shuru karo. Poocho: problem kisko hai, kitni baar hoti hai, aur aaj iski kya keemat hai? Success ko measurable metric mein define karo ('average ticket handling time 9 se 5 minute karna'). Check karo ki solution ke liye data aur workflow maujood hain ya nahi. Phir sabse simple approach chuno jo kaam kare, chahe woh ek rule ho, dashboard, classic ML model, LLM feature ya agent. AI tab sahi fit hai jab task baar-baar hota ho, pattern-based ho, kabhi-kabhi ki galti bardaasht ho, aur data ho. Tab sahi nahi jab har galti mehengi ho aur rules pehle se kaam kar rahe hon.

🪜 Step by step

Problem business ki bhasha mein likho: "Agents har ticket pe sahi policy dhoondhne mein 6 minute lagaate hain."
Aaj ka baseline naapo: kitna time, kitne, kitna kharch, kitni baar galat hota hai.
Solutions simple se complex ki taraf list karo: behtar process, rule, search, classic ML, LLM, agent.
Feasibility check karo: data hai, naap sakte hain, aur AI galat ho to kya hoga?
Ek success metric aur deadline ke saath chhote pilot pe sehmati banao.

🔑 Zaroori shabd

Success metric: Number jo dikhaaye ki solution ne kaam kiya ya nahi.
Baseline: Naye solution ke bina aaj cheezein kaise chal rahi hain.
Simplest viable approach: Sabse kam complex solution jo goal poora kare.

👀 Example dekho

text

Ask: "Build an AI chatbot for our support team"
Discovery: 60% of tickets are 'where is my order?'
Baseline:  9 min average handling, 30k tickets/month
Better fix: auto-reply with live order status (API) + LLM drafts for the rest
Metric:    handling time 9 → 5 min, CSAT ≥ 4.3

Diagnosis ne solution badal diya, aur use sasta aur zyaada effective bana diya.

⚠️ Aam galtiyaan jinse bachna hai

Problem ke bajaye technology se shuru karna ("chalo agents use karte hain").
Baseline nahi, to koi prove nahi kar sakta ki AI ne cheezein behtar ki.

✅ Jaldi se check karo

Client kehta hai 'customers ko chhodne se rokne ke liye AI chahiye'. Sabse accha pehla step kya hai?

Chatbot banao
Turant deep learning model train karo
'Chhodna' define karo, baseline naapo, aur dekho kaunsa data hai

Jawaab dekho

C. 'Chhodna' define karo, baseline naapo, aur dekho kaunsa data hai: Saaf definition, baseline aur data ke bina har model ek andaaza hai.

🙋 Seekhne wale yeh poochhte hain

Accha AI use case kaise pehchaanun?

Baar baar hone wala, mehenga, zyaadatar text ya pattern based kaam, jahan draft ya suggestion madad kare aur galtiyaan pakdi ja sakein.

Agar data abhi hai hi nahi to?

Ikattha karna shuru karo, ya aise LLM se shuru karo jise kam examples chahiye, aur waqt ke saath dataset banane ke liye asli usage log karo.

📌 Yaad rakho

Problem → metric → data → sabse simple solution. Dawai se pehle diagnosis.

Poora course, aapke roadmap ke hisaab se

Progress tracking, quick checks aur step-by-step order ke saath. Yeh lesson in courses ka hissa hai:

AI Forward Deployed Engineer · AI Product Manager

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