RAG, Finetuning, and Prompt Engineering - Extending the Capabilities of LLMs

Large Language Models have revolutionized AI with their ability to understand and generate human-like text. However, these models have inherent limitations in their knowledge and capabilities. This comprehensive guide explores three key techniques that have emerged to address these limitations and extend LLM capabilities.

Large Language Models (LLMs) have revolutionized artificial intelligence with their ability to understand and generate human-like text. However, these models have inherent limitations in their knowledge and capabilities. Three key techniques have emerged over the time to address these limitations and extend LLM capabilities: Retrieval-Augmented Generation (RAG), finetuning, and prompt engineering.

This comprehensive guide explores each approach, their purposes, and how they compare in extending LLM capabilities beyond their inherent constraints.

RAG enhances LLMs by connecting them to external knowledge sources, enabling them to access information beyond their training data.

Knowledge Retrieval : When a user asks a question, RAG searches an external knowledge base for relevant information.

Context Integration : The retrieved information is provided to the LLM as additional context.

Augmented Generation : The LLM uses this additional context alongside its internal knowledge to generate a response.

Finetuning adapts pre-trained LLMs to specific domains, tasks, or styles by additional training on specialized datasets.