Key techniques to improve the accuracy of your LLM app: Prompt engineering vs Fine-tuning vs RAG

Large Language Models (LLMs) are pivotal in AI democratization but can produce inaccurate or biased outputs, necessitating optimization techniques for improved accuracy. This blog post explores three main methods: prompt engineering, fine-tuning, and retrieval-augmented generation (RAG). Prompt engineering involves crafting various types of prompts, such as zero-shot, few-shot, and chain-of-thought, to influence model output based on task complexity. Fine-tuning adjusts a pre-trained model's weights using task-specific data, enhancing performance in specialized domains like healthcare or programming. RAG integrates real-time external data retrieval to ensure contextually accurate responses, augmenting LLMs without altering internal parameters. Each technique offers distinct advantages, and their application depends on specific goals, often requiring a combination for optimal results. Emphasizing an iterative process of testing and refining, these methods collectively aim to enhance LLM reliability and relevance.