@emelythegoat0: I love my siblings more than anything 🥹 #fyp #xyzbca #wockst★rz

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Saturday 17 October 2026 14:43:03 GMT

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🧁 :

Oh my gosh. I thought you meant ur getting another sibling.

2026-10-18 06:55:58

0

️ :

So cute

2026-10-17 14:51:51

1

𝔪𝔢𝔩𝔩𝔰:)𝔰𝔞𝔫 :

AWWWW

2026-10-17 15:40:27

1

call me big strap daddy. :

ouuu shiii

2026-10-17 17:47:19

0

_.Champagnemami :

[Heartwarming]

2026-10-17 16:24:58

1

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Prompting vs RAG vs Fine-tuning — the question every AI team argues about, answered in one picture. 🧠 Stop asking

Prompting vs RAG vs Fine-tuning — the question every AI team argues about, answered in one picture. 🧠 Stop asking "should I fine-tune?" Ask: is the problem style, facts, or judgment? 1️⃣ Prompting — fixes style. Tell the model how to answer: JSON, concise, this tone. → Use when: the facts are right but the format or tone is wrong. No training data. But the model never learns anything new. 2️⃣ RAG (Retrieval Augmented Generation) — fixes facts. The model still doesn't know it, but it can look it up in your docs. → Use when: facts change often, or live in your private docs. Update the docs, and the answers update the same day. No retraining. 3️⃣ Fine-tuning — fixes judgment. Train on examples until the model makes the call the way your team would. → Use when: the pattern is stable, but no prompt can capture it. Needs good examples, and a retrain every time the pattern changes. 👀 The part nobody says: most "we need to fine-tune" problems are really facts problems. Fine-tuning doesn't keep facts fresh, and when your pricing changes next week, the model won't know. Rule: style = prompt, facts = RAG, judgment = fine-tune. Try them in that order. Where teams go wrong: spending weeks building a training set to teach the model their product docs, when RAG would have shipped in days and stayed current. Send this to whoever just said "let's fine-tune it". 📸 Screenshot the last frame — the whole decision in one line. Save it before your next "should we fine-tune?" meeting. Follow @hackproduct — we turn scary AI-engineering concepts into things you can actually ship. ⚡ . . #AIengineering #machinelearning #softwareengineering #LLM #RAG

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