Enhancing Language Models: RAG, Fine Tuning & Prompt Engineering
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In this riveting episode by IBM Technology, we delve into the fascinating world of enhancing large language models through innovative techniques like Retrieval Augmented Generation (RAG), fine tuning, and prompt engineering. It's like fine-tuning a high-performance car to extract every ounce of power. RAG involves scouring for fresh data, beefing up the prompt with newfound information, and crafting a response enriched with context. It's like giving your engine a shot of nitrous for an extra kick.
Fine tuning, on the other hand, is akin to customizing your ride with specialized parts to dominate the racetrack. By training a model on specific data sets, it gains profound domain expertise, tweaking its internal parameters for optimal performance. It's like transforming a regular sedan into a race-ready beast. And let's not forget prompt engineering, a sophisticated art form that fine-tunes the model's output without additional training or data retrieval. It's like adjusting your driving style to conquer any terrain effortlessly.
These methods, though distinct, can be seamlessly combined for maximum impact. Picture a legal AI system: RAG fetches specific cases and recent court decisions, prompt engineering ensures adherence to legal document formats, and fine tuning hones the model's grasp on firm-specific policies. It's like assembling a dream team of experts to tackle any challenge head-on. Each method offers its unique strengths - RAG expands knowledge, prompt engineering provides flexibility, and fine tuning cultivates deep domain expertise. It's all about choosing the right tool for the job and steering towards success in the ever-evolving landscape of language models.
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Image copyright Youtube
Image copyright Youtube
Image copyright Youtube
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