Prompt Grounding (AKA Retrieval-Augmented Generation), is a technique that involves providing data sources as context to the model to help it generate more accurate or specific responses.
For example, instead of just asking the model:
In one sentence, what is a large language model?
A large language model (LLM) is an artificial intelligence tool that uses deep learning techniques to process and generate human language, mimicking the complexities of natural language usage.
You could provide the Wikipedia article on large language models as context:
Here is a wikipedia article on large language models: [content from https://en.wikipedia.org/wiki/Large_language_model]
Use that Wikipedia article as context to answer this question.
In one sentence, what is a language model?
A language model is an artificial neural network capable of general-purpose language generation and text-based natural language processing tasks – its abilities include classification, text generation, and understanding natural language – examples include OpenAI's GPT series and Google's PaLM.
This allows the model to ground its responses in the information provided in the context, leading to more informed and accurate outputs. Prompt grounding can be particularly useful when the task requires specific knowledge or expertise that is available in external sources.
For example, to understand an academic paper, you could provide the model with the paper as context:
User: [content from https://arxiv.org/pdf/1706.03762.pdf]
Tell me everything about the document above in two sentences
Assistant: The document appears to be a research paper titled "Attention Is All You Need" published in 2017. It introduces the Transformer, a new neural network architecture based solely on attention mechanisms, which achieved state-of-the-art results on machine translation tasks at the time.
User: In one sentence, why is it called "attention is all you need"?
Assistant: It is called "Attention is All You Need" because the Transformer architecture relies solely on attention mechanisms (specifically self-attention) to draw connections between different positions in the input and output sequences, completely doing away with the need for traditional sequential architectures like recurrent or convolutional layers.
User: Explain this paper to someone who doesn't understand AI
This paper introduces the Transformer model, a new method for handling sequence data in machine learning. Unlike previous models that processed sequences word by word, the Transformer uses an attention mechanism. This allows it to focus on different parts of the input sequence when generating each word in the output sequence, enabling it to process all words in the input sequence simultaneously. This makes the Transformer faster and more efficient. The paper demonstrates that the Transformer achieves high performance on translation tasks.
Prompt grounding can also be used to prevent hallucination or generation of false information by providing the model with reliable sources of information.
Prompt grounding works best when for tasks that require specific knowledge or expertise that can be found in certain sources, such as summarization, question answering, and other similar tasks.
