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RAG vs Fine-Tuning

RAG vs Fine-Tuning: When to Choose, What to Choose, and Why<br>Big language models (like ChatGPT, Gemini) are very smart. But they donu2019t know everything, and they donu2019t always stay up to date. Thatu2019s why people use two main tricks to make them better:<br><br>RAG (Retrieval-Augmented Generation) u2192 like giving the model a library card. It can go read fresh documents before answering.<br>Fine-Tuning (FT) u2192 like training the model in school. It learns a subject deeply, so it can answer in a certain way every time.

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RAG vs Fine-Tuning

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  1. RAG vs Fine-Tuning Which approach to choose for your AI / LLM use caseAgicent

  2. Agenda • • Introduction • • What is RAG? • • What is Fine-Tuning? • • Key Differences & Tradeoffs • • Use Cases / When to Use Which • • Hybrid Approach • • Summary / Recommendation • • Contact

  3. Introduction • In the world of large language models (LLMs), two popular strategies for tailoring them to domain data are Retrieval‑Augmented Generation (RAG) and Fine‑Tuning. This presentation explores how they differ, their pros/cons, and guidance on selecting one or combining both.

  4. What is RAG (Retrieval‑Augmented Generation) • • Uses external documents retrieved during inference as context. • • Model doesn’t need to store all knowledge internally. • • Enables incorporating up‑to‑date and dynamic information. • • Ideal for applications requiring current data and citations.

  5. What is Fine‑Tuning • • Trains or re-trains a pre-trained model on domain-specific data. • • Embeds knowledge directly in model weights. • • Can be full or parameter-efficient (e.g., LoRA, PEFT). • • Reduces dependency on external retrieval at runtime.

  6. Key Differences & Tradeoffs • • Knowledge Storage: RAG uses external data; Fine-Tuning embeds data internally. • • Updates: RAG reflects new data instantly; Fine-Tuning requires retraining. • • Cost: RAG cheaper to deploy; Fine-Tuning expensive but simpler at runtime. • • Risk: RAG grounded in documents; Fine-Tuning may hallucinate if data is limited.

  7. Use Cases / When to Use Which • RAG: • • Dynamic, frequently updated data • • Large and evolving domains • • Requires citations or transparency • Fine‑Tuning: • • Stable domain • • Need for brand tone/consistency • • Prioritize inference speed • Hybrid: • • Combine both for specialization + freshness.

  8. Hybrid Approach Benefits & Challenges • Benefits: • • Robust and adaptable responses • • Less retraining needed • Challenges: • • More components to manage • • Must align retrieval with model behavior.

  9. Summary & Recommendation • There’s no universal best choice — it depends on your data dynamics and goals. • • Fine‑Tuning: Stable domains, deep accuracy. • • RAG: Dynamic data, transparency, freshness. • • Hybrid: Often delivers the best of both worlds.

  10. Contact Us For collaboration or to explore RAG and Fine‑Tuningsolutions: • 📍 Agicent Technologies Pvt. Ltd. • 🌐 www.agicent.com • ✉️ sales@agicent.com • 📞 +1 - 646-466-4369

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