Lesson · Hinglish · ⏱ 6 min padhne mein

⚖️ Decisions ke liye AI literacy: build vs buy, cost aur limits

Q: Company ko apni policies pe internal Q&A bot chahiye. Aam taur pe sahi pehla approach kya hai?

Policy documents pe RAG ke saath model API. API + RAG sasta, fast hai aur jawaab current rakhta hai. Zero se training bahut zyaada hai.

🏠 Asli zindagi ki misaal

Dukaan ki billing ke liye aap developers rakh kar software banwa sakte ho, ya ₹500 mahine mein ready app subscribe kar sakte ho. Agar billing aapki dukaan ko khaas nahi banaati, to aap kharidte ho. AI mein bhi yahi choice aati hai: existing AI product ya API use karo, use customise karo, ya apna model banao.

💡 Aasaan shabdon mein

Zyaadatar companies ko apne foundation models train nahi karne chahiye. Normal seedhi yeh hai: off-the-shelf AI tool kharido → acche prompts ke saath model API use karo → apne data pe RAG jodo → fine-tune karo → custom models banao. Upar tabhi chadho jab neeche wala step fail ho. Costs mein API tokens, engineering time, evals, monitoring aur galtiyon ke liye zaroori human review shaamil hai. Limits jaano: models hallucinate karte hain, inconsistent ho sakte hain, tools ke bina exact calculation mein mushkil hoti hai, aur data privacy rules tay kar sakte hain ki kaunse providers use kar sakte ho. Acche AI decisions value, cost, risk aur launch ke time ko tolte hain.

🪜 Step by step

Zaroorat saaf define karo: task, volume, quality bar, latency aur data sensitivity.
Dekho kya maujood hai: SaaS product, model API, open model jo host kar sakte ho.
Top do options ko apne eval set pe test karo, vendor demos pe nahi.
Saal bhar ka total cost compare karo: usage, infrastructure, banane aur maintain karne wale engineers, aur switch karne ka cost.
Decide karo, aur nikalne ka raasta rakho: vendors ko apne interface ke peeche rakho taaki switch kar sako.

🔑 Zaroori shabd

Foundation model: Bada general-purpose model (GPT, Claude, Llama) jis pe doosre cheezein banaate hain.
Total cost of ownership: Time ke saath saare costs: API, log, monitoring aur galtiyaan.
Vendor lock-in: Ek provider pe aisi dependency jo badalna mushkil ho.

👀 Example dekho

text

Need: summarise 2,000 customer calls/day
Buy:   speech-analytics SaaS ≈ ₹2 lakh/month, live in 2 weeks
Build: transcription + LLM API ≈ ₹60k/month + 2 engineers × 3 months
Decision: buy now, revisit build if volume ×5 or custom insights are needed

Saaf, numbers-based decision, aur dobara sochne ka trigger bhi.

⚠️ Aam galtiyaan jinse bachna hai

Aisi problems ke liye custom models banana jo ek acchi API pehle se acche se solve karti hai.
Sirf per token price compare karna aur engineering aur maintenance time ignore karna.

✅ Jaldi se check karo

Company ko apni policies pe internal Q&A bot chahiye. Aam taur pe sahi pehla approach kya hai?

Research lab hire karo
Zero se foundation model train karo
Policy documents pe RAG ke saath model API

Jawaab dekho

C. Policy documents pe RAG ke saath model API: API + RAG sasta, fast hai aur jawaab current rakhta hai. Zero se training bahut zyaada hai.

🙋 Seekhne wale yeh poochhte hain

Mahine ka LLM cost kaise estimate karun?

Mahine ki requests × (input tokens × input price + output tokens × output price). Retries aur growth ke liye margin jodo.

Kya open source models kaafi acche hain?

Kai tasks ke liye haan, khaaskar fine tuning ke baad. Decide karne se pehle apne eval set pe API model ke against test karo.

📌 Yaad rakho

Buy → API → RAG → fine-tune → build. Zaroorat ho tabhi upar chadho.

📖 Is lesson ke shabd (AI dictionary)

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 Product Manager

Course mein seekho