Lesson 20 — Beginner

Choosing the Right Model

Ab tak humne model ko samjha — ab usse chunna hai. Is lesson mein hum ek practical framework dekhenge: 8 dimensions par har model ko check karo, model cards aur pricing pages se zaroori cheezein padho, aur ek step-by-step selection workflow follow karo — jismein benchmark scores sirf ek step hain, decision nahi.

⏱ 10 min• Beginner• Benchmarks & Leaderboards

Model chunna ek process hai, ranking nahi

WHAT

Model chunna ek requirement-first process hai — pehle apne application ki zarooratein likho, phir unhe models se match karo. Naam, hype aur rank baad mein aate hain. Best model wahi jo tumhare task par reliably chala hai.

WHEN

Naya feature banate waqt, model switch karte waqt, aur periodic cost review mein. Pehli baar chunne se lekar har upgrade tak — yahi process chalta hai.

WHERE

Shortlist banate waqt (leaderboard se), eval test karte waqt (apne examples par), aur final sign-off ke waqt (cost, latency, privacy ke parameters par).

Sabse pehle ek galat-fehmi door: model chunna ranking dekh kar nahi hota. Pehle tumhari requirement likhi jaati hai, phir candidates chhote kiye jaate hain, phir apne data par test hota hai — aur us test ke baad hi decision hota hai. Is process ka naam hai selection workflow.

8 dimensions — har candidate ko in par check karo

8 DIMENSIONS — har candidate ko in par check karo:

1  CAPABILITY        : task kar bhi sakta hai ya nahi?
2  QUALITY           : MERE task par kitna reliably karta hai?
3  LATENCY           : TTFT aur tokens-per-second — chat mein key
4  COST              : meri expected usage par per-request kitna?
5  CONTEXT           : mera input itna fit ho jaata hai?
6  MODALITIES        : text ke saath image/audio/video chahiye?
7  PRIVACY & DEPLOY  : API chalegi ya managed/self-host zaroori?
8  PRODUCT FEATURES  : structured output, tool calling, streaming?

Capability aur context toh gate hain: inmein se koi fail ho toh model out — chahe rank kitni bhi ho. Baaki dimensions tab matter karte hain jab gate pass ho.
Latency aur cost ka weight tumhare product se decide hota hai: batch pipeline mein latency chalegi, chat app mein dono nahi.

Yaad rakho: 8 dimensions ka matlab hai requirement sheet pehle likho. Jab tak must aur nice-to-have alag nahi kiye, tab tak koi bhi model best nahi lagta — har naya model amazing lagta hai.

Model cards, pricing pages aur API docs — kya padhna hai

Model card: benchmark tables, cutoff date, license, modality support — sab yahin. Aur dhyan do ki card ka snapshot aur API ka live version ek hi ho.
Pricing page: input aur output ke per-token rates alag hote hain — apne expected token mix par cost estimate karo, list price par akela nahi.
API documentation: context limit, supported parameters, rate limits, structured-output aur tool-calling support — feature claims yahin verify hoti hain.

Trap: Marketing page par badi-badi capability likhi ho, phir bhi API docs check karo — limits, rate-limits aur parameters wahan hote hain. Aur kisi aur ki purani likhi hui sankhya par bharosa mat karo, hamesha current page dekho.

Selection workflow — step by step

SELECTION WORKFLOW

1. DEFINE TASK       : 10-20 real examples + rubric likho
2. IDENTIFY REQS     : capability, latency, cost, privacy, features
3. SHORTLIST         : leaderboard + docs se 2-4 candidates
4. TEST ON YOUR DATA : sabko same examples par chalao
5. MEASURE QUALITY   : rubric se score karo
6. MEASURE LATENCY   : TTFT aur total time
7. MEASURE COST      : actual token use se per-request cost
8. INSPECT FAILURES  : kahan tootte hain — wahin asli farq hai
9. CHOOSE            : ab decision data par hai, hype par nahi

Pro Tip: Ye workflow chhota rakho — 10-20 examples kaafi hain shuru ke liye. Poora mahina mat ghumao; chhota test, jaldi decision, phir production mein eval badhate jao (L7 mein poora tareeka).

Practical decision rules

Batch pipeline? Latency ki chhinta nahi — sasta aur accurate model chuno, reasoning budget thoda badha bhi sakte ho.
Real-time chat? TTFT priority — chhota model, chhota output cap, streaming on.
High-stakes (medical, legal, finance)? Quality aur evals pehle, cost baad mein — aur human-in-the-loop mandatory.
Hamesha fallback rakho: primary model fail ho toh secondary (chhota/sasta) par girao — L2 mein retries, timeouts aur fallback ki details.

Interview line: Model selection ek one-time choice nahi, ek loop hai — define, shortlist, test, measure, choose, phir repeat. Benchmark ek input hai is loop mein, output nahi.

Exercise

Question: Selection workflow ka pehla step kya hai? (do shabd, English mein)

Question: Apne specific task ki reliability ka sabse bharosemand check kya hai — leaderboard ya apne examples?

Question: Model comparison mein quality ke saath kya bhi zaroori measure hai — latency ya colour?

Question: Do candidates mein se ek capability gate par fail ho jaye toh — continue ya out?

Common mistakes

Hype ya brand se model chunna: Requirement sheet likho pehle. Bina must-have criteria ke har naya model amazing lagta hai, aur bill aane par pata chalta hai galti thi. Requirement se shuru karo, nameplate se nahi.
Sirf benchmark scores par final decision: Benchmark shortlist deta hai, decision nahi. Apne examples, apne rubric aur apne latency budget par test kiye bina selection adhoori hai.
Cost estimate bina usage ke karna: Per-request cost expected input/output token mix se banti hai — lamba context wale app ka bill chhote prompt wale se alag. Apna usage model banao, list price par akela dhoka mat khao.

Desi example: Tailor ke paas kapda — model selection ka desi analogy

Tailor ke paas jaoge toh wo pehle poochhta hai occasion (task), budget (cost), fitting (latency) aur kapda (deployment). Sabse mehnga kapda hamesha sahi nahi — function dekh kar chuniye, aur trial fitting (test) ke bina silwai (deploy) mat karwao:

Occasion (task)     : chai meeting ya shaadi?
Budget (cost)       : per-metre rate dekho, sirf brand nahi
Fitting (latency)   : tailor ki speed — tez chahiye?
Fabric (deployment) : bahar ka (API) ya khud ka (self-host)
Decision            : requirement + trial fitting = sahi choice

Interview angle — yahan se sawaal aate hain

Q: Model chunne ke 8 dimensions kaunse hain? — Capability, quality, latency, cost, context, modalities, privacy/deployment, product features.
Q: Selection workflow kaisa hai? — Define task → identify requirements → shortlist → apne data par test → quality, latency, cost measure → failures inspect → choose.
Q: Benchmark scores final decision kyun nahi? — Shortlist tak chalte hain — apne prompts, apne task aur apne rubric par test zaroori hai.
Q: Chat app ke liye selection mein kya priority? — Latency (TTFT) aur streaming — quality bhi chahiye, par user ko der bardasht nahi.

Lesson complete?

L1 LLM 101 complete! Tum ab model ko samajh kar baat karte ho — tokens, training, inference, sampling, context, failures, landscape, benchmarks aur selection. Agla level — L2 Working with LLM APIs — mein code karke asli API calls, streaming aur orchestration seekhoge.