Enhancing LLM performance with RAG and LangChain – a dive into advanced functionalities
The retrieval-augmented generation (RAG) framework has become instrumental in tailoring large language models (LLMs) for specific domains or tasks, bridging the gap between the simplicity of prompt engineering and the complexity of model fine-tuning.
Prompt engineering stands as the initial, most accessible technique for customizing LLMs. It leverages the model’s capacity to interpret and respond to queries based on the input prompt. For example, to inquire if Nvidia surpassed earnings expectations in its latest announcement, directly providing the earnings call content within the prompt can compensate for the LLM’s lack of immediate, up-to-date context. This approach, while straightforward, hinges on the model’s ability to digest and analyze the provided information within a single or a series of carefully crafted prompts.
When the scope of inquiry exceeds what...
